CN115670517A - Fetal ventricle ratio measuring method and ultrasonic imaging system - Google Patents
Fetal ventricle ratio measuring method and ultrasonic imaging system Download PDFInfo
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Abstract
The application provides a measuring method of fetal ventricle ratio and an ultrasonic imaging system, wherein the method is applied to the ultrasonic imaging system, the ultrasonic imaging system comprises a processor and comprises the following steps: the processor acquires an ultrasonic image of a lateral ventricle cross section of a fetus; the processor determining a brain midline based on the ultrasound image of the lateral ventricle transverse plane; the processor obtaining a first distal-most point of a lateral ventricles lateral wall in the ultrasound image that is farthest from the brain midline and obtaining a length of a first perpendicular from the first distal-most point to the brain midline; the processor obtaining a second farthest point inside the skull in the ultrasound image that is farthest from the brain midline and obtaining a length of a second perpendicular to the brain midline from the second farthest point; the processor determines a ventricular rate of the fetus from the length of the first perpendicular line and the length of the second perpendicular line. The method and the device improve the working efficiency of doctors and increase the accuracy of the obtained ventricular rate.
Description
Technical Field
The present application relates to the field of ultrasound imaging technology, and more particularly, to a method for measuring fetal ventricular ratio and an ultrasound imaging system.
Background
In modern obstetrical examination, the ultrasonic technology becomes the most widely used examination mode due to the characteristics of safety, convenience, no damage, low price and the like, and particularly, the ultrasonic technology can avoid potential damages of X-rays and the like to a mother body and a fetus, so that the application value of the ultrasonic technology in obstetrical examination is obviously superior to that of other examinations, and the ultrasonic technology becomes one of main auxiliary means for diagnosing by obstetricians.
In diagnosing fetal nervous system dysplasia, the lateral ventricle is one of the important examination items. The lateral ventricles are located in the deep part of the cerebral hemisphere, and are respectively arranged at the left and the right to form a C-shaped ventricular cavity, and cerebrospinal fluid is filled in the cavities. The lateral ventricle with a width greater than 10mm is called lateral ventricle widening, which is mostly caused by the cerebrospinal fluid excess of the fetus, the central nervous system abnormality such as the brain midline structural dysplasia or local occupation, etc., and can also be an intracranial expression of other systemic abnormalities such as chromosome abnormality, virus infection, etc. Therefore, the change of the lateral ventricle during the development of the fetus is an important basis for a doctor to judge the fetal nervous system diseases and the pathological changes, and even an important basis for the doctor to make a judgment on whether to recommend continuing pregnancy.
In addition to directly measuring the lateral ventricular width, ventricular ratio is also widely used to determine whether a lateral ventricular abnormality has occurred. In the process of measuring the fetal ventricular ratio, a doctor is often required to manually measure the distance from the midline of the brain to the lateral ventricle outer wall and the distance from the midline of the brain to the inner face of the fetal skull, namely, manually drawing a vertical line from the farthest point of the lateral ventricle outer wall from the midline of the brain to the midline of the brain and a vertical line from the farthest point of the inner face of the skull from the midline of the brain to the midline of the brain.
However, it is very difficult for a doctor to manually draw an accurate vertical line without using other tools, so that in actual clinical practice, measuring and calculating the ventricular rate often consumes too much time and energy, and is inefficient and low in accuracy.
In view of at least one of the above problems, the present application proposes a new fetal ventricle ratio measuring method and an ultrasound imaging system.
Disclosure of Invention
A series of concepts in a simplified form are introduced in the summary section, which is described in further detail in the detailed description section. This summary of the invention is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
In one aspect, the present embodiment provides a method for measuring a fetal ventricular ratio, which is applied to an ultrasound imaging system, where the ultrasound imaging system includes a processor, and the method includes:
the processor acquires an ultrasonic image of a lateral ventricle cross section of a fetus;
the processor determining a brain midline based on the ultrasound image of the lateral ventricle transverse plane;
the processor obtaining a first distal-most point of a lateral ventricles lateral wall in the ultrasound image that is farthest from the brain midline and obtaining a length of a first perpendicular from the first distal-most point to the brain midline;
the processor obtaining a second farthest point inside the skull in the ultrasound image that is farthest from the brain midline and obtaining a length of a second perpendicular to the brain midline from the second farthest point;
the processor determines a ventricular rate of the fetus from the length of the first perpendicular and the length of the second perpendicular.
In one example, the processor acquires a first distal-most point of a lateral ventricles lateral wall in the ultrasound image that is farthest from the brain midline, comprising:
the processor responds to an operation instruction for the ultrasonic image, and acquires a first farthest point, which is farthest from the brain midline, of the lateral ventricle outer side wall in the ultrasonic image; or
The processor automatically acquires a first distal-most point of the lateral ventricles in the ultrasound image that is farthest from the midline of the brain.
In one example, the processor automatically acquires a first distal-most point in the ultrasound image at which the lateral ventricular wall is farthest from the brain midline, comprising:
identifying a lateral ventricle posterior horn region in the acquired ultrasound image;
calculating a distance of each pixel within the lateral posterior ventricular corner region to the brain midline;
selecting a pixel farthest from the brain midline as the first farthest point.
In one example, the processor acquires a second farthest point inside the skull in the ultrasound image that is farthest from the brain midline, comprising:
the processor responds to an operation instruction for the ultrasonic image, and acquires a second farthest point which is farthest away from the brain midline on the inner side of the skull in the ultrasonic image; or
The processor automatically acquires a second farthest point inside the skull in the ultrasound image that is farthest from the brain midline.
In one example, the processor automatically acquires a second farthest point inside the skull from the brain midline in the ultrasound image, comprising:
identifying and acquiring a craniocerebral hyperechoic ring region in the ultrasonic image;
calculating the distance from each pixel of the inner side edge of the brain hyperechoic ring area to the brain midline;
selecting the pixel farthest from the brain midline as the second farthest point.
In one example, the processor determines a brain midline based on the ultrasound image of the lateral ventricle transverse plane, comprising:
the processor determines the brain midline in an ultrasound image of the lateral ventricle transverse plane in response to operating instructions for the ultrasound image; or
The processor determines the brain midline in an ultrasound image of the lateral ventricle transverse plane by an intelligent recognition method.
In one example, the measurement method further comprises:
the processor automatically generating a first perpendicular from the first distal-most point to the midline of the brain;
displaying the first vertical line in the ultrasound image; and/or
The processor automatically generating a second perpendicular from the second most-distal point to the midline of the brain;
displaying the second perpendicular line in the ultrasound image.
In one example, the measurement method further comprises:
displaying the brain midline, and/or displaying the ventricle ratio in the ultrasound image.
In one example, the measurement method further comprises:
acquiring a first adjusting instruction for adjusting the position of the first farthest-most point, and adjusting the position of the first farthest-most point based on the first adjusting instruction; and/or
And acquiring a second adjusting instruction for adjusting the position of the second farthest-most point, and adjusting the position of the second farthest-most point based on the second adjusting instruction.
In one example, the measurement method further comprises:
the processor determines whether the fetus has a lateral ventricle abnormality according to the threshold range of the ventricle ratio, and outputs prompt information when the lateral ventricle is abnormal;
and displaying the prompt message.
In another aspect, an embodiment of the present application provides an ultrasound imaging system, including:
an ultrasonic probe;
the transmitting circuit is used for exciting the ultrasonic probe to transmit ultrasonic waves to the fetus;
the receiving circuit is used for receiving the ultrasonic echo based on the ultrasonic wave returned from the fetus to obtain an ultrasonic echo signal;
a processor configured to: obtaining an ultrasonic image of the lateral ventricle cross section of the fetus according to the ultrasonic echo signal;
the processor is further configured to perform the aforementioned method of measuring fetal ventricular ratio;
and the display is used for displaying various visual information.
According to the fetal ventricle ratio measuring method and the ultrasonic imaging system, the working process of a doctor is optimized, the working efficiency of the doctor is improved, the accuracy of the finally obtained fetal ventricle ratio to be measured is increased, the repeatability of the obtained ventricular ratio is good, and a better basis is provided for the diagnosis of the fetal lateral ventricle development condition of the doctor.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
In the drawings:
FIG. 1 shows a schematic block diagram of an ultrasound imaging system according to an embodiment of the present application;
FIG. 2 shows an ultrasound image of a lateral ventricle cross-section with a fetal ventricle ratio manually measured;
FIG. 3 shows an ultrasound image of a lateral ventricle cross-section measuring fetal ventricle ratio in accordance with an embodiment of the present application;
fig. 4 shows a schematic flow chart of a method of measuring fetal ventricular ratio according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, exemplary embodiments according to the present application will be described in detail below with reference to the accompanying drawings. It should be understood that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and that the present application is not limited by the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the application described in the application without inventive step, shall fall within the scope of protection of the application.
In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present application. It will be apparent, however, to one skilled in the art, that the present application may be practiced without one or more of these specific details. In other instances, well-known features of the art have not been described in order to avoid obscuring the present application.
It is to be understood that the present application is capable of implementation in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the application to those skilled in the art.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of the associated listed items.
In order to provide a thorough understanding of the present application, a detailed structure will be provided in the following description in order to explain the technical solution proposed in the present application. Alternative embodiments of the present application are described in detail below, however, the present application may have other implementations in addition to these detailed descriptions.
Next, an ultrasound imaging system according to an embodiment of the present application is first described with reference to fig. 1, and fig. 1 shows a schematic structural block diagram of an ultrasound imaging system 100 according to an embodiment of the present application.
As shown in fig. 1, the ultrasound imaging system 100 includes an ultrasound probe 110, transmit circuitry 112, receive circuitry 114, a processor 116, and a display 118. Further, the ultrasound imaging system 100 may further include a transmit/receive selection switch 120 and a beam forming module 122, and the transmit circuit 112 and the receive circuit 114 may be connected to the ultrasound probe 110 through the transmit/receive selection switch 120.
The ultrasound probe 110 includes a plurality of transducer elements, which may be arranged in a line to form a linear array, or in a two-dimensional matrix to form an area array, or in a convex array. The transducer elements are used for transmitting ultrasonic waves according to the excitation electric signals or converting the received ultrasonic waves into electric signals, so that each transducer element can be used for realizing the mutual conversion of electric pulse signals and ultrasonic waves, thereby realizing the transmission of the ultrasonic waves to the brain of the tested fetus and also receiving the ultrasonic wave echoes reflected by the brain of the tested fetus. When ultrasonic detection is carried out, which transducer elements are used for transmitting ultrasonic waves and which transducer elements are used for receiving the ultrasonic waves can be controlled through a transmitting sequence and a receiving sequence, or the transducer elements are controlled to be time-slotted for transmitting the ultrasonic waves or receiving echoes of the ultrasonic waves. The transducer elements participating in the ultrasonic wave transmission can be simultaneously excited by the electric signals, so that the ultrasonic waves are transmitted simultaneously; alternatively, the transducer elements participating in the transmission of the ultrasonic beam may be excited by several electrical signals having a certain time interval, so as to continuously transmit the ultrasonic wave having a certain time interval.
During ultrasound imaging, the transmit circuit 112 is used to excite the ultrasound probe to transmit ultrasound waves to the fetus, and the transmit circuit 112 sends a delay-focused transmit pulse to the ultrasound probe 110 through the transmit/receive selector switch 120. The ultrasonic probe 110 is excited by the transmission pulse to transmit a corresponding ultrasonic waveform to the tissue of the body to be measured along a corresponding two-dimensional (2D) scanning plane, and after a certain delay, the receiving circuit 114 receives an ultrasonic echo with tissue information reflected from the tissue of the body to be measured and converts the ultrasonic echo back into an electrical signal again. The receiving circuit 114 receives the electrical signals generated by the ultrasound probe 110, obtains ultrasound echo signals, and sends the ultrasound echo signals to the beam forming module 122, and the beam forming module 122 performs processing such as focusing delay, weighting, and channel summation on the ultrasound echo data, and then sends the ultrasound echo data to the processor 116. The signal processing module of the processor 116 processes the signal, and the processor 116 obtains an ultrasound image of the lateral ventricle cross section of the fetus according to the ultrasound echo signal, specifically, the processor 116 performs corresponding two-dimensional ultrasound image reconstruction on the signal processed by the signal processing module according to the spatial position relationship of each transmission/reception signal, and obtains two-dimensional ultrasound data, such as an ultrasound image, of the body tissue to be measured after corresponding partial or all image post-processing steps, such as denoising, smoothing, enhancing, and the like.
Alternatively, the processor 116 may be implemented as software, hardware, firmware, or any combination thereof, and may use a single or multiple Application Specific Integrated Circuits (ASICs), a single or multiple general purpose Integrated circuits, a single or multiple microprocessors, a single or multiple programmable logic devices, or any combination of the foregoing, or other suitable circuits or devices. Also, the processor 116 may control other components in the ultrasound imaging system 100 to perform the respective steps of the method for measuring fetal ventricular ratio in various embodiments of the present description, and various details regarding the method for measuring fetal ventricular ratio will be described below.
The display 118 is connected with the processor 116, and the display 118 may be a touch display screen, a liquid crystal display screen, or the like; alternatively, the display 118 may be a separate display, such as a liquid crystal display, a television, or the like, separate from the ultrasound imaging system 100; alternatively, the display 118 may be a display screen of an electronic device such as a smart phone, a tablet computer, and the like. The number of the display 118 may be one or more.
The display 118 is used to display various visual information that may be used to display the ultrasound images obtained by the processor 116. In addition, the display 118 can provide a graphical interface for human-computer interaction for the user while displaying the ultrasound image, and one or more controlled objects are arranged on the graphical interface, so that the user can input operation instructions by using the human-computer interaction device to control the controlled objects, thereby executing corresponding control operation. For example, an icon is displayed on the graphical interface, and the icon can be operated by the human-computer interaction device to execute a specific function, such as drawing a brain midline, a first farthest point, a first vertical line, a second farthest point, a second vertical line and the like on the ultrasonic image.
Optionally, the ultrasound imaging system 100 may further include a human-computer interaction device other than the display 118, which is connected to the processor 116, for example, the processor 116 may be connected to the human-computer interaction device through an external input/output port, which may be a wireless communication module, a wired communication module, or a combination thereof. The external input/output port may also be implemented based on USB, bus protocols such as CAN, and/or wired network protocols, etc.
The human-computer interaction device may include an input device for detecting input information of a user, for example, control instructions for the transmission/reception timing of the ultrasonic waves, operation input instructions for drawing points, lines, frames, or the like on the ultrasonic images, or other instruction types. The input device may include one or more of a keyboard, mouse, scroll wheel, trackball, mobile input device (e.g., mobile device with touch screen display, cell phone, etc.), multi-function knob, and the like. The human-computer interaction device may also include an output device such as a printer.
The ultrasound imaging system 100 may also include a memory 124 for storing instructions executed by the processor 116, storing received ultrasound echoes, and the like. The memory may be a flash memory card, solid state memory, hard disk, etc. Which may be volatile memory and/or non-volatile memory, removable memory and/or non-removable memory, etc.
It should be understood that the components included in the ultrasound imaging system 100 shown in fig. 1 are merely illustrative and that more or fewer components may be included. This is not limited by the present application.
Fig. 2 shows an ultrasound image of a lateral ventricle cross section of a fetus, on the image of the lateral ventricle cross section, a skull strong echo ring is in an oval shape, the posterior horn of the lateral ventricle is clearly displayed, a strong echo vena plexus is arranged inside, part of the thalamus can be seen in the center of the image, and a brain midline, a transparent compartment and a brain lateral fissure can be seen. The lateral ventricle cross-sectional image can be a sectional image obtained by two-dimensional ultrasonic scanning and can also be a three-dimensional ultrasonic middle sectional image. In the figure, the perpendicular line A and the perpendicular line B are respectively a perpendicular line from the farthest point of the lateral ventricle outer side wall from the brain midline to the brain midline and a perpendicular line from the farthest point of the skull inner side from the brain midline to the brain midline, which are drawn by a doctor manually, so that time and labor are wasted, the accuracy is difficult to ensure, and the obtained result has poor repeatability.
In view of the above problems, the present application provides some methods for measuring fetal ventricular ratio, which will be explained and illustrated one by one hereinafter.
The method for measuring the fetal ventricular ratio proposed by the embodiment of the present application is described below with reference to fig. 4, and fig. 4 is a schematic flow chart of the method for measuring the fetal ventricular ratio proposed by the embodiment of the present application. The method for measuring fetal ventricular ratio of the embodiment of the present application is used for an ultrasound imaging system including an ultrasound probe, a processor and a display, and the ultrasound imaging system may be implemented as the ultrasound imaging system 100 as above. Specifically, the method for measuring the fetal ventricular rate of the embodiment of the application comprises the following steps:
in step S410, the processor acquires an ultrasound image of a lateral ventricle cross-section of the fetus;
in step S420, the processor determines a brain midline based on the ultrasound image of the lateral ventricle transverse plane;
in step S430, the processor acquires a first distal-most point of a lateral ventricles lateral wall in the ultrasound image, which is farthest from the brain midline, and acquires a length of a first perpendicular from the first distal-most point to the brain midline;
in step S440, the processor acquires a second farthest point inside the skull in the ultrasound image, which is farthest from the brain midline, and acquires a length of a second perpendicular from the second farthest point to the brain midline;
in step S450, the processor determines a ventricular rate of the fetus according to the length of the first perpendicular line and the length of the second perpendicular line.
According to the fetal ventricle ratio measuring method, the work flow of a doctor is optimized, the work efficiency of the doctor is improved, the accuracy of the finally obtained fetal ventricle ratio to be measured is improved, the repeatability of the obtained ventricle ratio is good, and a better basis is provided for the diagnosis of the fetal lateral ventricle development condition by the doctor.
Specifically, in step S410, an ultrasound image, which may be a two-dimensional ultrasound image or a three-dimensional ultrasound image, may be acquired by the aforementioned ultrasound imaging system 100, in the ultrasound imaging process, the transmitting circuit is configured to excite the ultrasound probe to transmit an ultrasound wave to the brain of the fetus, and the transmitting circuit transmits the delayed focused transmitting pulse to the ultrasound probe through the transmitting/receiving selection switch. The ultrasonic probe is excited by the emission pulse to emit corresponding ultrasonic waveforms to the tested organism tissue along a corresponding two-dimensional (2D) scanning plane, and after a certain time delay, the receiving circuit receives ultrasonic echoes with brain information reflected from the brain of a fetus and converts the ultrasonic echoes into electric signals again. The receiving circuit receives the electric signals generated by the conversion of the ultrasonic probe to obtain ultrasonic echo signals, and the ultrasonic echo signals are sent to the beam synthesis module, and the beam synthesis module carries out focusing delay, weighting, channel summation and other processing on the ultrasonic echo data and then sends the ultrasonic echo data to the processor. The processor also carries out corresponding two-dimensional ultrasonic image reconstruction on the signals processed by the signal processing module according to the spatial position relation of each transmitting/receiving signal, and obtains two-dimensional ultrasonic data of the lateral ventricle cross section of the fetus, such as a two-dimensional ultrasonic image, after corresponding partial or all image post-processing steps of denoising, smoothing, enhancing and the like.
Alternatively, the ultrasound image of the present application may be an ultrasound still image or an ultrasound dynamic image (e.g., a video/4D image). Alternatively, a certain frame in the dynamic image may be selected from the acquired ultrasound data as a static image input, and the like.
In determining the midline of the brain in the ultrasound image of the lateral ventricle transverse plane, step S420 can be implemented in a variety of ways.
The midline of the brain may be determined in an ultrasound image of a lateral ventricle transverse plane, for example, by a combination of manual and automated means. In some embodiments, step S420 may include: a processor determines the brain midline in an ultrasound image of the lateral ventricle transverse plane in response to operating instructions for the ultrasound image. The processor determines a straight line passing through the two non-coincident points marked by the user in the ultrasonic image of the lateral ventricle transverse plane according to the two non-coincident points marked by the user, wherein the determined straight line is the midline of the brain; or the operating instruction may be a straight line marked by the user in the ultrasound image of the lateral ventricle cross-section, which the processor identifies and treats as a brain midline.
It is worth mentioning that in the present application, the operation instruction may be generated by detecting a marking operation for a lateral ventricle transverse plane, the operation includes, but is not limited to, sliding a trackball (a direction indicator similar to a mouse wheel on an ultrasound imaging system) mark, sliding and clicking a mouse mark, a user manually touching a mark on a touch screen, pressing up, down, left and right direction key marks of a keyboard, and the like.
The brain midline can also be determined in the ultrasound image of the lateral ventricle transverse plane by means of automatic identification. For example, the processor determines a brain midline based on the ultrasound image of the lateral ventricle transverse plane, including: the processor determines the brain midline in an ultrasound image of the lateral ventricle transverse plane by an intelligent identification method. The method has the advantages that the brain midline is automatically determined in the ultrasonic image of the lateral ventricle transverse section through the intelligent identification method, so that errors caused by marking the brain midline by a user can be reduced, the accuracy of the determined brain midline is improved, the scene that the user manually marks the brain midline is reduced, the mechanical labor is avoided, the work flow of determining the brain midline by a doctor is optimized, the work efficiency is effectively improved, and the diagnosis flow of the doctor is quicker and smoother.
It should be understood that in the present application, various intelligent identification methods such as a line detection algorithm, a structure detection algorithm, a machine learning method, a deep learning algorithm, etc. can be adopted to determine the brain midline in the ultrasound image of the lateral ventricle transverse plane.
In some embodiments, the brain midline is determined in an ultrasound image of a lateral ventricle transverse plane by a line detection algorithm. Because the brain midline is a linear structure positioned on the symmetry axis of the cross section of the cranium and the echo of the brain is obviously distinguished from the surrounding tissues, the brain midline can be detected by using a linear detection algorithm. For example, all the lines in the ultrasound image of the lateral ventricle transverse plane are detected by a line detection algorithm, and the line closest to the long axis of the craniocerebral transverse plane is selected as the line where the brain midline is located. The line detection algorithm includes, but is not limited to, hough transform, radon transform, LSD fast line detection algorithm, FLD line detection algorithm, EDlines line detection algorithm, LSWMS line detection algorithm, cannyLines line detection algorithm, etc.
In some embodiments, the brain midline is determined in an ultrasound image of a lateral ventricle transverse plane by a structure detection algorithm. For example, a characteristic anatomical structure located on the brain midline is detected based on a structure detection algorithm, and a straight line fitting is performed on the characteristic anatomical structure to obtain the brain midline. When the obtained ultrasonic image is a sectional image of the thalamus, the special anatomical structures on the midline of the brain comprise the thalamus, the transparent separation cavity, the lumbricus part and the like, and when the obtained ultrasonic image is a sectional image of the thalamus, the special anatomical structures on the midline of the brain comprise the thalamus, the transparent separation cavity and the like, and the midline of the brain is obtained by performing straight line fitting on points on the obtained cerebellum, the transparent separation cavity and the lumbricus part of the cerebellum or the thalamus and the transparent separation cavity. Common structure detection methods include, but are not limited to, the algorithm of the Otsu threshold value (OSTU), the level set (LevelSet), and the like.
In some embodiments, the brain midline is determined in the ultrasound image of the lateral ventricle transverse plane by a machine learning method or a deep learning algorithm. The machine learning method or the deep learning algorithm is used for distinguishing the characteristics or the rules of the target area in the learning database, and then positioning and identifying the targets of other images according to the learned characteristics or rules, so that the automatic determination of the brain midline is realized. For example, an ultrasound image of a lateral ventricle transverse plane is input as input data into a pre-trained machine learning model or a deep learning model for calculation to obtain a linear equation of a brain midline as output data, wherein the pre-trained machine learning model or the deep learning model is trained using a preset database of calibrated ultrasound images including at least one linear equation of which the brain midline has been calibrated, in the training process, the ultrasound image is calibrated as input data, the linear equation of which the brain midline of the ultrasound image is calibrated as output data, and parameters of the model are optimized in the process of training the machine learning model or the deep learning model.
Taking the example of determining a brain centerline in a two-dimensional image, the step of training the deep learning model to obtain a pre-trained deep learning model may comprise: 1. constructing a training sample database: the training sample database comprises a large amount of calibrated fetal lateral ventricle section data, wherein the specific calibration can be set according to an actual task, and can be the positions of the left end point and the right end point of the brain midline or a Mask (Mask) for accurately segmenting the brain midline region. 2. Positioning and identifying: after a training sample database is constructed, a deep learning algorithm is designed to learn the characteristics or rules for distinguishing target areas in the training sample database to realize the positioning of the brain midline, and specific implementation includes but is not limited to the following situations. For example, the positions of the left and right endpoints of the midline of the brain or the two endpoints of the long axis of the transverse section of the brain can be detected and identified based on a deep learning method, and in general, a network can be generated by stacking convolutional layers, full-link layers and the like, and feature learning and parameter regression can be performed on the endpoint coordinates in a training sample database. For an input image, the coordinates of two end points can be obtained through network regression, and therefore the position of the brain midline can be determined. Common networks are R-CNN, fast R-CNN, faster R-CNN, SSD, YOLO, etc. For another example, an end-to-end semantic segmentation method based on deep learning is used to perform accurate segmentation on a region where a brain centerline is located, and the specific process may refer to the above description, where the difference is that a full-link layer is removed, and upsampling or deconvolution is added to make input and output sizes the same, so as to obtain a brain centerline region of an input image, and determine a position of a straight line where the brain centerline is located according to a segmentation result. Common networks include FCN, U-Net, mask R-CNN, and the like.
Further, after the brain midline is determined based on the above manner, the brain midline can be displayed in the ultrasonic image, so that a doctor can conveniently and intuitively know the condition of the brain midline from the ultrasonic image. For example, in fig. 3, the line segment C shown in the ultrasound image is the determined brain midline.
Further, in order to facilitate acquisition of the lengths of the first farthest point and the first perpendicular from the first farthest point to the brain midline, step S430 may be implemented by the following several implementation manners.
The first most distal point of the lateral ventricular wall, which is farthest from the brain midline, can be obtained in an ultrasound image of a lateral ventricular cross-section, for example, by a combination of manual and automated means. In some embodiments, the processor acquires a first distal-most point of a lateral ventricle in the ultrasound image that is farthest from the brain midline, comprising: the processor is used for responding to an operation instruction of the ultrasonic image, and acquiring a first farthest point, which is farthest from the brain midline, of the lateral ventricle outer side wall in the ultrasonic image. Wherein the operation instruction may be a point marking operation performed by the user in the ultrasound image of the lateral ventricle transverse plane, the processor determining the first most-distal point according to the point marking operation.
It is worth mentioning that in the present application, the operation instruction may be generated by detecting a marking operation for a lateral ventricle transverse plane, the operation includes, but is not limited to, sliding a trackball (a direction indicator similar to a mouse wheel on an ultrasound imaging system) mark, sliding and clicking a mouse mark, a user manually touching a mark on a touch screen, pressing up, down, left and right direction key marks of a keyboard, and the like.
The first most distal point of the lateral ventricle outer wall farthest from the brain midline can also be obtained in the ultrasound image of the lateral ventricle transverse section by means of automatic identification. For example, the processor acquires a first distal-most point of a lateral ventricle in the ultrasound image that is farthest from the brain midline, including: the processor automatically acquires a first distal-most point of the lateral ventricle in the ultrasound image that is farthest from the brain midline. The first farthest point of lateral ventricle outer wall farthest from the brain midline is obtained automatically in the ultrasonic image of lateral ventricle transverse section through the processor, so that the error caused by marking the first farthest point by a user can be reduced, the accuracy of the first farthest point is improved, the scene of manually marking the first farthest point by the user is reduced, the mechanical labor is avoided, the work flow of obtaining the first farthest point by a doctor is optimized, the work efficiency is effectively improved, and the diagnosis flow of the doctor is more rapid and smooth.
It should be understood that in the present application, a plurality of identification methods such as a machine learning algorithm, a deep learning method, etc. can be adopted to automatically obtain the first farthest point of the lateral ventricle outer wall farthest from the brain midline in the ultrasound image of the lateral ventricle transverse plane.
In some embodiments, the processor automatically acquires a first distal-most point in the ultrasound image at which the lateral ventricles lateral wall is farthest from the brain midline, comprising: identifying a lateral ventriculo-posterior horn region in the acquired ultrasound image; calculating a distance of each pixel within the lateral posterior ventricular horn region to the brain midline; selecting the pixel farthest from the brain midline as the first farthest point. For example, to obtain the first farthest point of the lateral ventricles in the two-dimensional image that is farthest from the midline of the brain, the step of training the deep learning model to obtain a pre-trained deep learning model may comprise: 1. constructing a training sample database: the training sample database comprises a plurality of calibration results of the lateral ventricle posterior horn area of the fetal craniocerebral transverse section, wherein the calibration results are masks of the lateral ventricle area posterior horn (including the lateral ventricle posterior horn side wall). 2. And (3) accurate segmentation step: similar to the process of determining the central line of the brain through the deep learning algorithm, a semantic segmentation network is constructed and the characteristics or rules for distinguishing the lateral ventricle area in the training sample database are learned, so that the network can acquire all pixel positions in the lateral ventricle posterior horn area in the input image. 3. Determining the position of the farthest point of the lateral ventricle outer wall from the brain midline: and combining the determined position of the straight line of the brain midline, calculating the distance from the pixels in the posterior horn area of the lateral ventricle to the brain midline, and selecting the pixel with the largest distance as a first farthest point from the lateral ventricle to the brain midline.
After the first farthest point is generated according to the above-mentioned manner, a vertical line from the first farthest point to the brain centerline determined in step S420 is generated in a manner combining manual and automatic or automatic, and the generated vertical line is the first vertical line, as shown by the vertical line a in fig. 3. In some embodiments, the processor automatically generates a first perpendicular from the first most distal point to the brain midline and displays the first perpendicular in an ultrasound image.
The generated first perpendicular is measured to determine its length, or the perpendicular distance from the first most distant point to the brain midline, i.e. the length of the first perpendicular, is calculated directly.
Further, in order to facilitate acquisition of the lengths of the second farthest point and the second farthest point to the second perpendicular to the brain midline, step S440 may be implemented by the following several implementation manners.
The second most distal point of the medial side of the skull, which is farthest from the brain midline, can be obtained in an ultrasound image of a lateral ventricle transverse plane, for example, by a combination of manual and automated means. In some embodiments, the processor acquires a second farthest point inside the skull from the brain midline in the ultrasound image, comprising: the processor is used for responding to an operation instruction of the ultrasonic image, and acquiring a second farthest point which is farthest away from the brain midline on the inner side of the skull in the ultrasonic image. Wherein the operation instruction may be a point marking operation performed by the user in the ultrasound image of the lateral ventricle transverse plane, the processor determining the second-most-distal point according to the point marking operation.
It is worth mentioning that in the present application, the operation command may be generated by detecting a marking operation for a lateral ventricle transverse plane, the operation including, but not limited to, sliding a trackball (a direction indicator similar to a mouse wheel on an ultrasound imaging system) mark, sliding and clicking a mouse mark, a user touching a mark on a touch screen by hand, pressing up, down, left and right direction key marks of a keyboard, and the like.
The second farthest point on the inner side of the skull, which is farthest from the brain midline, can also be obtained in the ultrasound image of the lateral ventricle transverse plane by means of automatic identification. For example, the processor acquires a second farthest point inside the skull from the brain midline farthest in the ultrasound image, including: the processor automatically acquires a second farthest point inside the skull in the ultrasound image that is farthest from the brain midline. The processor automatically obtains the farthest second farthest point from the brain midline on the inner side of the skull in the ultrasonic image of the lateral ventricle transverse section, so that the error caused by marking the second farthest point by the user can be reduced, the accuracy of the second farthest point is improved, the scene of manually marking the second farthest point by the user is reduced, the mechanical labor is avoided, the work flow of obtaining the second farthest point by the doctor is optimized, the work efficiency is effectively improved, and the diagnosis flow of the doctor is more rapid and smooth.
It should be understood that in the present application, a variety of identification methods, such as machine learning algorithms, deep learning methods, etc., may be employed to automatically identify the second-most distal point of the medial skull, which is farthest from the midline of the brain, in the ultrasound image of the lateral ventricle transverse plane.
In some embodiments, the processor automatically acquires a second farthest point inside the skull in the ultrasound image that is farthest from the brain midline, comprising: identifying and acquiring a brain hyperechoic ring area in the ultrasonic image; calculating the distance from each pixel of the inner side edge of the brain hyperechoic ring area to the brain midline; selecting the pixel farthest from the brain midline as the second farthest point. That is, in the ultrasound image, the second most distant point refers to the pixel of the greatest distance from the pixel of the medial border of the hyperechoic cranial ring area to the brain midline. For example, to obtain the second farthest point of the inside of the skull farthest from the brain centerline in the two-dimensional image, the step of training the deep learning model to obtain a pre-trained deep learning model may comprise: 1. constructing a training sample database: the training sample database comprises a large number of calibration results of the craniocerebral hyperecho ring area of the fetal craniocerebral transverse section, and the calibration results are masks of the craniocerebral hyperecho ring area. 2. And (3) accurate segmentation step: similar to the process of determining the brain midline through the deep learning algorithm, a semantic segmentation network is constructed and the characteristics or rules for distinguishing the areas of the craniocerebral hyperecho rings in the training sample database are learned, so that the network can acquire all the pixel positions of the areas of the craniocerebral hyperecho rings in the input image. 3. Determining the position of the farthest point of the inner side edge of the brain hyperechoic ring area from the brain midline: and calculating the distance from the pixel at the inner side edge of the area of the cerebral hyperechoic ring to the cerebral midline by combining the determined position of the straight line of the cerebral midline, and selecting the pixel with the largest distance as the second farthest point from the inner side of the skull to the cerebral midline.
After the second farthest point is generated according to the above-mentioned manner, a vertical line from the second farthest point to the brain centerline determined in step S420 is generated in a manual and automatic manner, and the generated vertical line is the second vertical line, as shown by the vertical line B in fig. 3. In some embodiments, the processor automatically generates a second perpendicular from the second most distal point to the brain midline and displays the second perpendicular in an ultrasound image.
The generated second perpendicular is measured to determine its length, or the perpendicular distance from the second most distal point to the midline of the brain is calculated directly, i.e., the length of the second perpendicular.
It is worth noting that neither the manual or automatic combination nor the automatic way can ensure that the acquired positions of the first farthest-most point and the second farthest-most point are necessarily accurate. Therefore, in the present application, after the first farthest-most point and the second farthest-most point are acquired, the positions of the first farthest-most point and the second farthest-most point can also be adjusted by the adjustment instruction so that the acquired first farthest-most point and second farthest-most point are as accurate as possible.
In some embodiments, a first adjustment instruction for adjusting the position of the first farthest-most point is obtained, and the position of the first farthest-most point is adjusted based on the first adjustment instruction. The first adjustment instruction may be a point marking operation, a coordinate adjustment operation, or the like performed by the user in the ultrasound image of the lateral ventricle transverse plane, and the processor adjusts the position of the first farthest point according to the point marking operation, the coordinate adjustment operation, or the like. Taking the coordinate adjustment operation as an example, after the first farthest point is acquired in step S430, the user can input the adjustment coordinate (X, Y), and the first farthest point is displaced by the X distance on the X axis and the Y distance on the Y axis according to the adjustment coordinate (X, Y), so that the position of the first farthest point after the adjustment position is acquired.
In some embodiments, a second adjustment instruction for adjusting the position of the second farthest-most point is obtained, and the position of the second farthest-most point is adjusted based on the second adjustment instruction. The second adjustment instruction may be a point marking operation, a coordinate adjustment operation, or the like that is performed by the user in the ultrasound image of the lateral ventricle transverse plane, and the processor adjusts the position of the second farthest point according to the point marking operation, the coordinate adjustment operation, or the like. Taking the coordinate adjustment operation as an example, after the second farthest point is acquired in step S430, the user can input the adjustment coordinate (X, Y), and the second farthest point is displaced by the X distance on the X axis and the Y distance on the Y axis according to the adjustment coordinate (X, Y), so that the position of the second farthest point after the adjustment position is acquired.
It is worth mentioning that in the present application, the first adjustment instruction and the second adjustment instruction may be generated by detecting an operation of a user, the operation including, but not limited to, a point mark operation (e.g., sliding a trackball (a direction indicator similar to a mouse wheel on an ultrasound imaging system) mark, sliding and clicking a mouse mark, a user touching a mark on a touch screen, pressing up, down, left and right direction key marks of a keyboard, etc.), a data input operation, and the like.
In some embodiments, a position identifier, such as a graphic identifier, corresponding to the first farthest-most point and/or the second farthest-most point may also be displayed on the ultrasound image, so that the user can visually observe whether the first farthest-most point or the second farthest-most point has shifted.
In some embodiments, the respective lengths may also be displayed around the first vertical line and/or the second vertical line so that the user can visually see the lengths, or the lengths may be displayed in a concentrated manner in other areas of the ultrasound image.
Further, after the length of the first perpendicular line and the length of the second perpendicular line are obtained, the ventricular rate of the fetus can be determined according to the length of the first perpendicular line and the length of the second perpendicular line. Assuming that the length of the first perpendicular line is a and the length of the second perpendicular line is b, the ventricular rate of the fetus can be calculated by the following formula:
in some embodiments, in order to facilitate the physician to intuitively understand the ventricular ratio from the ultrasound image, the method further comprises: displaying the ventricle ratio in the ultrasound image.
After the ventricular ratio is calculated, whether the fetus has a lateral ventricular abnormality can be determined according to the ventricular ratio. In some embodiments, the processor determines whether there is a lateral ventricle abnormality in the fetus based on a threshold range in which the ventricular ratio is located, and outputs a prompt when there is an abnormality in the lateral ventricle. The presentation information may be character information, image information, sound information, or any combination thereof, and for example, an abnormality warning image and an abnormality warning sound are output when there is an abnormality in the lateral ventricle. When the image information includes information that can be displayed, such as text information, image information, and the like, the prompt information can be displayed through a display, such as a mobile phone screen, a computer screen, and the like.
In some embodiments, the ventricular ratio is defined as a ratio of a distance between a midline of the brain and an outer lateral ventricle (i.e. a length of the first perpendicular line) and a distance between the midline of the brain and an inner skull of the fetus (i.e. a length of the second perpendicular line), wherein a threshold range of the ventricular ratio for a normal fetus is also dependent on a gestational week of the fetus, wherein the threshold range may be different for different gestational weeks, for example, a mean value of the ventricular ratio for 12 weeks of the normal fetus is 0.7, a mean value for 15 weeks of the pregnancy is 0.56, and a mean value for 30 weeks of the pregnancy is 0.3, and the threshold range of the ventricular ratio for a normal fetus in different gestational weeks can be determined according to the data, such that the processor determines whether there is a lateral ventricular abnormality in the fetus according to the threshold range of the ventricular ratio, and more specifically, the processor further comprises: acquiring gestational week data of a fetus, acquiring a threshold range of corresponding ventricle ratios of normal fetuses according to the gestational week data, comparing the ventricle ratio of the tested fetus with the threshold range, indicating that the ventricle ratio of the tested fetus is normal and the lateral ventricle of the tested fetus is normal when the ventricle ratio is in the threshold range, and indicating that the lateral ventricle is possibly abnormal if the ventricle ratio exceeds the threshold range, thereby outputting prompt information.
According to the fetal ventricle ratio measuring method and the ultrasonic imaging system, the work flow of a doctor is optimized, the work efficiency of the doctor is improved, the accuracy of the finally obtained fetal ventricle ratio to be measured is improved, the repeatability of the obtained ventricle ratio is good, and a better basis is provided for the diagnosis of the fetal lateral ventricle development condition by the doctor.
Although the example embodiments have been described herein with reference to the accompanying drawings, it is to be understood that the above-described example embodiments are merely illustrative and are not intended to limit the scope of the present application thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the scope or spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as claimed in the appended claims.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described device embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another device, or some features may be omitted, or not executed.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the description of exemplary embodiments of the present application, various features of the present application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various inventive aspects. However, the method of the present application should not be construed to reflect the intent: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
It will be understood by those skilled in the art that all of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where such features are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.
Various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some of the modules according to embodiments of the present application. The present application may also be embodied as apparatus programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present application may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website, or provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
The above description is only for the specific embodiments of the present application or descriptions thereof, and the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present application, and should be covered by the protection scope of the present application. The protection scope of the present application shall be subject to the protection scope of the claims.
Claims (11)
1. A method for measuring fetal ventricular rate, applied to an ultrasound imaging system including a processor, the method comprising:
the processor acquires an ultrasonic image of a lateral ventricle cross section of a fetus;
the processor determining a brain midline based on the ultrasound image of the lateral ventricle cross-section;
the processor obtaining a first distal-most point of a lateral ventricles lateral wall in the ultrasound image that is farthest from the brain midline and obtaining a length of a first perpendicular from the first distal-most point to the brain midline;
the processor acquiring a second farthest point inside the skull in the ultrasound image that is farthest from the brain midline and acquiring a length of a second perpendicular from the second farthest point to the brain midline;
the processor determines a ventricular rate of the fetus from the length of the first perpendicular and the length of the second perpendicular.
2. The measurement method of claim 1, wherein the processor acquiring a first distal-most point of a lateral ventricles lateral wall in the ultrasound image that is farthest from the brain midline comprises:
the processor responds to an operation instruction for the ultrasonic image, and acquires a first farthest point, which is farthest from the brain midline, of the lateral ventricle outer side wall in the ultrasonic image; or
The processor automatically acquires a first distal-most point of the lateral ventricles in the ultrasound image that is farthest from the midline of the brain.
3. The measurement method of claim 2, wherein the processor automatically acquiring a first distal-most point of the lateral ventricles lateral wall in the ultrasound image that is farthest from the brain midline comprises:
identifying a lateral ventriculo-posterior horn region in the acquired ultrasound image;
calculating a distance of each pixel within the lateral posterior ventricular horn region to the brain midline;
selecting the pixel farthest from the brain midline as the first farthest point.
4. The measurement method of claim 1, wherein the processor acquiring a second farthest point inside the skull in the ultrasound image that is farthest from the brain midline comprises:
the processor responds to an operation instruction for the ultrasonic image, and acquires a second farthest point which is farthest away from the brain midline on the inner side of the skull in the ultrasonic image; or
The processor automatically acquires a second farthest point inside the skull in the ultrasound image that is farthest from the brain midline.
5. The measurement method of claim 4, wherein the processor automatically acquires a second farthest point inside the skull in the ultrasound image that is farthest from the brain midline, comprising:
identifying and acquiring a craniocerebral hyperechoic ring region in the ultrasonic image;
calculating the distance from each pixel of the inner side edge of the brain hyperechoic ring area to the brain midline;
selecting the pixel farthest from the brain centerline as the second farthest point.
6. The measurement method of claim 1, wherein the processor determines a brain midline based on the ultrasound image of the lateral ventricle transverse plane, comprising:
the processor determining the brain midline in an ultrasound image of the lateral ventricle cross-section in response to operating instructions for the ultrasound image; or
The processor determines the brain midline in an ultrasound image of the lateral ventricle transverse plane by an intelligent identification method.
7. The measurement method according to any one of claims 1 to 6, characterized in that the measurement method further comprises:
the processor automatically generating a first perpendicular from the first distal-most point to the brain midline;
displaying the first vertical line in the ultrasound image; and/or
The processor automatically generating a second perpendicular from the second most-distal point to the midline of the brain;
displaying the second perpendicular line in the ultrasound image.
8. The measurement method according to any one of claims 1 to 7, characterized in that the measurement method further comprises:
displaying the brain midline, and/or displaying the ventricle ratio in the ultrasound image.
9. The measurement method according to any one of claims 1 to 8, characterized in that the measurement method further comprises:
acquiring a first adjusting instruction for adjusting the position of the first farthest-most point, and adjusting the position of the first farthest-most point based on the first adjusting instruction; and/or
And acquiring a second adjusting instruction for adjusting the position of the second farthest point, and adjusting the position of the second farthest point based on the second adjusting instruction.
10. The measurement method according to any one of claims 1 to 9, characterized in that the measurement method further comprises:
the processor determines whether the fetus has a lateral ventricle abnormality according to the threshold range of the ventricle ratio, and outputs prompt information when the lateral ventricle is abnormal;
and displaying the prompt message.
11. An ultrasound imaging system, characterized in that the ultrasound imaging system comprises:
an ultrasonic probe;
the transmitting circuit is used for exciting the ultrasonic probe to transmit ultrasonic waves to the fetus;
the receiving circuit is used for receiving the ultrasonic echo which is returned from the fetus and is based on the ultrasonic wave to obtain an ultrasonic echo signal;
a processor to: obtaining an ultrasonic image of the lateral ventricle cross section of the fetus according to the ultrasonic echo signal;
the processor is further configured to perform a method of measuring a fetal ventricle ratio of any of the preceding claims 1 to 10;
and the display is used for displaying various visual information.
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