WO2022133806A1 - 胎儿颜面部容积图像修复方法和超声成像系统 - Google Patents

胎儿颜面部容积图像修复方法和超声成像系统 Download PDF

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Publication number
WO2022133806A1
WO2022133806A1 PCT/CN2020/138631 CN2020138631W WO2022133806A1 WO 2022133806 A1 WO2022133806 A1 WO 2022133806A1 CN 2020138631 W CN2020138631 W CN 2020138631W WO 2022133806 A1 WO2022133806 A1 WO 2022133806A1
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Prior art keywords
fetal
image
data
face
fetal face
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PCT/CN2020/138631
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English (en)
French (fr)
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林穆清
董国豪
邹耀贤
陈志杰
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深圳迈瑞生物医疗电子股份有限公司
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Priority to PCT/CN2020/138631 priority Critical patent/WO2022133806A1/zh
Priority to CN202080103766.6A priority patent/CN116157821A/zh
Publication of WO2022133806A1 publication Critical patent/WO2022133806A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T19/00—Manipulating 3D models or images for computer graphics
    • G06T19/20—Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/64—Three-dimensional objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2210/00—Indexing scheme for image generation or computer graphics
    • G06T2210/41—Medical
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2219/00—Indexing scheme for manipulating 3D models or images for computer graphics
    • G06T2219/20—Indexing scheme for editing of 3D models
    • G06T2219/2021—Shape modification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/03—Recognition of patterns in medical or anatomical images

Definitions

  • the present application relates to the technical field of ultrasound imaging, and more particularly, to a method and an ultrasound imaging system for volumetric image restoration of a fetal face.
  • ultrasound technology has become one of the most widely used, most frequently used, and one of the fastest methods of examination due to its high reliability, speed and convenience, real-time imaging, and repeatable examinations.
  • ultrasound has become the most important examination technology in obstetrics; ultrasound examination can avoid the radiation hazards of X-ray, CT and other technologies; at the same time, compared with MR technology, it has the advantages of real-time imaging and low price.
  • Ultrasonography in obstetrics can comprehensively evaluate and screen the fetus including morphology, respiratory system, nervous system and other physiological systems, and detect various neonatal pathological problems in advance.
  • 3D/4D some new obstetric examination contents have gradually been added, such as 3D/4D imaging of the face of the fetus.
  • the results of ultrasound data rendering are often limited by ultrasound data.
  • the fetal posture, the relative position of the fetus in the mother, and the occlusion of the placenta will lead to poor imaging quality of the rendering results.
  • Doctors usually need to collect multiple times, sometimes It is also necessary for the pregnant woman to move around repeatedly to change the posture and position of the fetus, which is time-consuming and labor-intensive and affects the efficiency of the doctor.
  • a first aspect of the embodiments of the present application provides a method for restoring fetal facial volumetric images, including:
  • the ultrasonic echo signals are processed to obtain volumetric image data of the fetal face
  • the rendered image is displayed.
  • a second aspect of the embodiments of the present application provides a method for restoring a fetal facial volume image, including:
  • the ultrasonic echo signals are processed to obtain volumetric image data of the fetal face
  • the rendered image after inpainting is displayed.
  • a third aspect of the embodiments of the present application provides a method for restoring fetal facial volumetric images, including:
  • the rendered image is displayed.
  • a fourth aspect of the embodiments of the present application provides a method for restoring a fetal facial volume image, including:
  • the rendered image after inpainting is displayed.
  • the method for repairing a volumetric image of the fetal face includes filling in the missing data of the fetal face, and/or filling in the partial data of the fetal face that is occluded. to remove.
  • a fifth aspect of the embodiments of the present application provides an ultrasound imaging system, including:
  • a transmitting circuit for exciting the ultrasonic probe to transmit ultrasonic waves
  • a receiving circuit for receiving the echo of the ultrasonic wave through the ultrasonic probe to obtain an ultrasonic echo signal
  • a processor for performing the steps of the method as described above.
  • the missing, occluded or poor-quality volume image data is restored to improve the fetal facial rendering imaging.
  • imaging conditions such as fetal posture when collecting image data of the fetal face
  • FIG. 1 shows a schematic block diagram of an ultrasound imaging system according to an embodiment of the present application
  • FIG. 2 shows a schematic flowchart of a method for restoring a volumetric image of a fetal face according to an embodiment of the present invention
  • FIG. 3 shows a schematic flowchart of a method for restoring a fetal facial volume image according to another embodiment of the present invention
  • FIG. 4 shows a schematic flowchart of a method for restoring a fetal facial volume image according to another embodiment of the present invention
  • FIG. 5 shows a schematic flowchart of a method for restoring a volumetric image of a fetal face according to yet another embodiment of the present invention.
  • the fetal facial volume image restoration method and ultrasonic imaging system provided by the present application can be applied to the human body, and can also be applied to various animals.
  • FIG. 1 shows a schematic structural block diagram of an ultrasound imaging system 100 according to an embodiment of the present application.
  • the ultrasound imaging system 100 includes an ultrasound probe 110 , a transmit/receive circuit 112 , a processor 114 , a display 116 , and a memory 118 . Further, the ultrasound imaging system 100 may further include a beam forming circuit, a transmit/receive selection switch, and the like.
  • the ultrasonic probe 110 includes a plurality of transducer array elements, and the plurality of transducer array elements can be arranged in a row to form a linear array, or arranged in a two-dimensional matrix to form an area array, and the plurality of transducer array elements can also be arranged form a convex array.
  • the transducer is used to transmit ultrasonic waves according to the excitation electrical signal, or convert the received ultrasonic waves into electrical signals, so each array element can be used to realize the mutual conversion of electrical pulse signals and ultrasonic waves, so as to realize the tissue emission to the target area of the measured object.
  • Ultrasound can also be used to receive ultrasound echoes reflected back by tissue.
  • transducers are used for transmitting ultrasonic waves and which transducers are used for receiving ultrasonic waves, or which transducers are used for transmitting ultrasonic waves or receiving ultrasonic waves in time slots through the transmitting sequence and receiving sequence.
  • the transducers participating in ultrasonic emission can be excited by electrical signals at the same time, so as to emit ultrasonic waves at the same time; or, the transducers participating in ultrasonic beam emission can also be excited by several electrical signals with a certain time interval, so as to continuously emit a certain time interval. Ultrasound.
  • the transmit/receive circuit 112 may be connected to the ultrasound probe 110 through a transmit/receive selection switch.
  • the transmit/receive selection switch may also be called a transmit/receive controller, which may include a transmit controller and a receive controller.
  • the transmit controller is used to excite the ultrasound probe 110 to transmit ultrasound to the area where the fetal face is located via the transmit circuit; the receive controller uses The ultrasound probe 110 receives the ultrasound echoes returned from the region where the face of the fetus is located through the receiving circuit, so as to obtain ultrasound echo data.
  • the transmitting/receiving circuit 112 sends the electrical signal of the ultrasonic echo into the beam forming circuit, and the beam forming circuit performs processing such as focusing delay, weighting and channel summation on the electrical signal, and then sends the processed ultrasonic echo data to the beam forming circuit. into the processor 114.
  • the processor 114 may be implemented by software, hardware, firmware or any combination thereof, and may use circuits, single or multiple application specific integrated circuits (ASICs), single or multiple general-purpose integrated circuits, single or multiple microprocessors, single or multiple programmable logic devices, or any combination of the foregoing circuits and/or devices, or other suitable circuits or devices, thereby enabling the processor 114 to perform the various corresponding steps of the method. Also, the processor 114 may control other components in the ultrasound imaging system 100 to perform desired functions.
  • ASICs application specific integrated circuits
  • microprocessors single or multiple programmable logic devices
  • the processor 114 may control other components in the ultrasound imaging system 100 to perform desired functions.
  • the processor 114 processes the received ultrasound echo data to obtain volumetric image data of the fetal face.
  • the ultrasound probe 110 transmits/receives ultrasound in a series of scanning planes, and is integrated by the processor 114 according to its three-dimensional spatial relationship, so as to realize the scanning of the fetal face in space and the reconstruction of the image.
  • the volumetric image data of the fetal face is obtained.
  • the processor 114 may acquire volumetric image data of the fetal face.
  • the processor 114 is further configured to perform repair processing on the volume image data, and then render the repaired data to obtain a rendered image; the processor 114 is further configured to render the volume image data to obtain the fetus The initial rendered image of the face; performing restoration processing on the initial rendered image of the fetal face.
  • the rendered image obtained by processor 114 may be stored in memory or displayed on display 116 .
  • the display 116 is connected to the processor 114, and the display 116 may be a touch display screen, a liquid crystal display screen, etc.; or the display 116 may be an independent display device such as a liquid crystal display, a television set, etc. independent of the ultrasound imaging system 100; or the display 116 may be Displays of electronic devices such as smartphones, tablets, etc.
  • the number of displays 116 may be one or more.
  • the display 116 may include a main screen and a touch screen, where the main screen is mainly used for displaying ultrasound images, and the touch screen is mainly used for human-computer interaction.
  • Display 116 may display ultrasound images obtained by processor 114 .
  • the display 116 can also provide a graphical interface for the user to perform human-computer interaction while displaying the ultrasonic image, set one or more controlled objects on the graphical interface, and provide the user with a human-computer interaction device to input operating instructions to control these objects.
  • the controlled object so as to perform the corresponding control operation.
  • an icon is displayed on a graphical interface, and the icon can be operated by using a human-computer interaction device to perform a specific function.
  • the ultrasound imaging system 100 may further include other human-computer interaction devices other than the display 116, which are connected to the processor 114.
  • the processor 114 may be connected to the human-computer interaction device through an external input/output port, and the external input/output port may be connected to the human-computer interaction device.
  • the output port can be a wireless communication module, a wired communication module, or a combination of the two.
  • External input/output ports may also be implemented based on USB, bus protocols such as CAN, and/or wired network protocols, and the like.
  • the human-computer interaction device may include an input device for detecting the user's input information, for example, the input information may be a control instruction for the ultrasonic transmission/reception sequence, or a point, line or frame drawn on the ultrasonic image. Manipulate input instructions, or may also include other instruction types.
  • the input device may include one or a combination of a keyboard, a mouse, a scroll wheel, a trackball, a mobile input device (eg, a mobile device with a touch display screen, a cell phone, etc.), a multi-function knob, and the like.
  • the human-computer interaction apparatus may also include an output device such as a printer.
  • the ultrasound imaging system 100 may also include memory for storing instructions executed by the processor, storing received ultrasound echoes, storing ultrasound images, and the like.
  • the memory may be a flash memory card, solid state memory, hard disk, or the like. It may be volatile memory and/or non-volatile memory, removable memory and/or non-removable memory, and the like.
  • the components included in the ultrasound imaging system 100 shown in FIG. 1 are only illustrative, and may include more or less components. This application is not limited to this.
  • FIG. 2 is a schematic flow chart of a method 200 for restoring a volumetric image of a fetal face and face according to an embodiment of the present application.
  • a method 200 for restoring a fetal facial volume image includes the following steps:
  • step S210 ultrasonic waves are transmitted to the face of the fetus through an ultrasonic probe, and echoes of the ultrasonic waves are received to obtain ultrasonic echo signals.
  • the rendered image of the face of the fetus can intuitively find the abnormal development of the face and face of the fetus, such as cleft lip; on the other hand, compared with the abstract B-mode image, the rendered image of the face can more vividly show the fetus in the womb
  • the actual condition of the cavity can realize the structural examination and deformity screening of the fetus in the early pregnancy, and can provide the pregnant woman with relevant pregnancy information as early as possible.
  • ultrasound image acquisition may be performed based on the ultrasound imaging system 100 shown in FIG. 1 .
  • the user moves the ultrasound probe 110 to select an appropriate position and angle, and the transmit circuit in the transmit/receive circuit 120 sends a set of delayed-focused pulses to the ultrasound probe 110, and the ultrasound probe 110 transmits ultrasonic waves along the 2D scanning plane to the face of the fetus.
  • the ultrasonic probe 110 receives the reflected ultrasonic echo, it converts it into an electrical signal, and the beam synthesizing circuit performs focusing delay, weighting and channel summation processing on the signals obtained by multiple transmission/reception, so as to realize beam synthesizing, It is then sent to the processor 114 for subsequent signal processing.
  • step S220 the ultrasonic echo signal is processed to obtain volumetric image data of the fetal face.
  • the volume image data of the fetal face may be three-dimensional volume data or four-dimensional volume data, which is not limited herein.
  • the three-dimensional spatial relationship of the ultrasonic echoes obtained by the ultrasound probe 110 transmitted/received in a series of scanning planes can be integrated, so as to realize the scanning of the fetal face in the three-dimensional space and the reconstruction of the 3D image .
  • image post-processing steps such as denoising, smoothing, and enhancement, the volumetric image data of the fetal face is obtained.
  • a volume of pre-reconstructed (polar coordinate) volume data is obtained after a complete probe sector scan cycle is completed through the above processing, and then the volume data is sent to the 3D reconstruction module to obtain the reconstruction.
  • the latter (Cartesian coordinate system) volume data.
  • the visual information is obtained, and then sent to the display for display.
  • 4D ultrasound repeats the above process in the time dimension to obtain multi-volume volume data and display them one by one.
  • three-dimensional volume data is used as an example for detailed description unless otherwise specified.
  • the three-dimensional ultrasound data of the fetal head may be acquired, or only the three-dimensional data of the fetal face area may be acquired.
  • a two-dimensional slice containing key information may also be intercepted from the three-dimensional volume data, so as to repair multiple two-dimensional slices frame by frame in subsequent steps.
  • step S230 repair processing is performed on the volumetric image data of the fetal face based on a deep learning algorithm and/or an image repair algorithm.
  • restoration processing is performed on the volumetric image data of the fetal face, and then the restored volumetric image data of the fetal face is rendered to obtain a rendered image of the fetal face.
  • the restoration processing of the volumetric image data of the fetal face may be performed according to the specific conditions of the volumetric image data.
  • the The repairing process can be to fill in the missing part of the data; in another embodiment, when the part of the face of the fetus is occluded, the occluded part can be filled, and the data that occludes the face of the fetus can also be removed. , so as to expose the occluded area of the fetal face, so as to completely display the fetal face.
  • the inpainting process can use an enhancement method based on a deep learning algorithm to achieve a better enhancement effect; it can also use an enhancement method based on a traditional image inpainting algorithm to achieve a certain enhancement effect.
  • the restoration processing can be performed directly using the 3D volume data as input, or multiple 2D slices in the 3D volume data can be selected and the restoration processing can be performed on the multiple 2D slices frame by frame, respectively.
  • the repair processing method is only different in the specific algorithm dimension and feature dimension used in the implementation, and the specific steps of the repair processing are similar, which can be implemented by deep learning algorithms or traditional image repair methods. .
  • a deep learning algorithm is used to perform repair processing on the volumetric image data of the fetal face.
  • the repair method based on the generative model of the deep learning algorithm (hereinafter referred to as the generative model) is to learn the feature space distribution of a large number of volumetric image data of the fetal face through the generative model, and the learned generative model can infer the low quality of the input.
  • the missing part of the volume image data or the occlusion and other influencing factors are generated, and then the high-quality restored volume image data corresponding to the input low-quality volume image data is generated, so as to realize the restoration of the fetal face.
  • volume image data that can be completely displayed on the face of the fetus is called high-quality data
  • the data that is missing from the face of the fetus and/or the volume image data where the face of the fetus is occluded is called low-quality data. Repair to obtain repaired high-quality data. Unless otherwise specified, both the high-quality data and the low-quality data refer to the explanation.
  • the specific steps for the deep learning algorithm to repair the volumetric image data of the fetal face include:
  • the first step is to establish a database. This step is to build a database required for the training of the generative model of the deep learning algorithm, so that the generative model can learn the feature space distribution of a large number of fetal facial data.
  • the database includes the data that the fetal face can be completely displayed and the data that the fetal face is missing and/or the data that the fetal face is blocked.
  • the database includes several paired or unpaired 3D ultrasound data of the fetal face, wherein the paired 3D ultrasound data means that the low-quality data and the high-quality data are from the same fetus, and the acquisition conditions are similar;
  • the unpaired data refers to a pair of low-quality data and high-quality data in the database that are not from the same fetus. It should be noted that whether the data is paired only affects which generation model is finally used to achieve enhancement, and the two are not mutually exclusive methods, that is, they can exist alone or at the same time, which is not limited here.
  • the generation model has the function of the repair processing, and is used for taking the volume image data or the slice taken out from the volume image data as input, and outputting the repaired volume image data or slice.
  • the generative models of common deep learning algorithms include Generative Adversarial Networks (GANs).
  • GANs Generative Adversarial Networks
  • the generative models based on deep learning algorithms also include many other variants, such as conditional generative adversarial networks. (C-GAN), W-GAN, Cycle-GAN, etc., can achieve the enhancement of low-quality data.
  • C-GAN conditional generative adversarial networks.
  • W-GAN W-GAN
  • Cycle-GAN Cycle-GAN, etc.
  • GAN Generative Adversarial Network
  • GAN usually consists of two parts: a generator and a discriminator.
  • the generator consists of an encoder and a decoder.
  • the function of the encoder is to map the input volumetric image data of the fetal face to the feature space, and the decoder will map the results of the encoder. Transform to the enhanced feature space, and output the enhanced result, that is, obtain the volume image data of the fetal face after restoration processing.
  • the generator also includes a discriminator for training the generator, and the discriminator takes the result of the generator as input; through data training, the discriminator can distinguish the input fetuses.
  • the volumetric image data of the face belongs to low-quality data or high-quality data, so it can be used to evaluate whether the output of the generator conforms to the enhanced data; when the output of the generator is closer to the low-quality data, the discriminator will give A large penalty, training the generator to make its output close to high-quality data; otherwise, the discriminator will give a small penalty.
  • the generative model is trained using paired or unpaired volumetric image data of the fetal face, resulting in a generative model that augments the input low-quality data into a high-quality data.
  • the volumetric image data of the fetal face can be three-dimensional volume data or four-dimensional volume data, and is not limited to any one.
  • the volumetric image data of the fetus' face is subjected to the restoration process using a conventional image restoration algorithm.
  • Traditional image inpainting algorithms are usually implemented using the method of Patch Match, which includes:
  • volumetric image data of the fetal face Divide the volumetric image data of the fetal face into very small image blocks, and then calculate the image features of the missing or occluded areas of the fetal facial volumetric image data, including but not limited to contour features, texture features, etc., Then, in the non-missing area of the volumetric image data of the fetal face, find an image block that best matches the image features of the image block to be repaired and has the highest similarity to fill the current missing image block to realize image repair.
  • a database can be established, and an image block database can be constructed from image blocks of a large number of samples.
  • an image restoration algorithm for restoration the image blocks in the currently input fetal facial volume image are first selected, If no enough matching image blocks are found in the non-missing area of the currently input fetal facial volume image, the matching is performed in the image block database, and the image blocks on other sample images are used to match the current fetal facial volume image to be repaired. .
  • the deep learning algorithm and/or the image inpainting algorithm does not require the user to specify problematic (missing or occluded) regions of the volumetric image data, and the missing or occluded positions are automatically learned by the generative model.
  • specifying the area where data is missing or blocked in the volumetric image data of the fetus' face can be done automatically or manually, and is not limited to any one.
  • the user can also manually select areas in the volumetric image data with missing or occluded data on the interactive interface, for example, the user can specify the missing or occluded areas through the interactive interface using methods such as clicking, framing, etc. s position.
  • the area specified by the user is marked in the form of a heat map or a mask.
  • the missing or occluded range can be determined according to the position specified by the user, and then the specific position can be inpainted by the traditional image inpainting algorithm.
  • the above manual method only manually selects the position or area where the data is missing or occluded, and then uses the deep learning algorithm or image inpainting algorithm described above to learn the law of the volume image data from a large number of samples. , the repair process is performed, so as to realize the enhancement of low-quality data.
  • the data of the above-mentioned restoration processing method is not limited to any one, such as three-dimensional volume data or four-dimensional volume data.
  • the repair processing can be repaired through a plurality of three-dimensional volume data, or can be directly repaired through a single three-dimensional volume data.
  • using a single three-dimensional volume data for repair processing is to directly obtain the structural features of the fetal face according to the three-dimensional volume data, and then infer the specific structure of the missing part.
  • the generative model can infer the corresponding high-quality data according to the characteristics of the input image volume data.
  • the restoration process using multi-volume 3D volume data is to synthesize multiple volumes of different 3D volume data collected at different times and at different angles. Therefore, some data that are originally missing or occluded can be completed by this method. For example, at a certain angle, the volume image data has missing or occluded parts, but it may be complete at other angles, so a more complete data can be obtained by merging the data of multiple angles with each other.
  • the fetal face volume image data at the first angle has a first region where data is missing or occluded; in the fetal face volume image data at the second angle The first area can be displayed completely; the volumetric image data of the fetal face under the first angle and the second angle are fused with each other, so as to repair the first area with missing or occluded data at the first angle.
  • the above-mentioned method for fusing the volumetric image data of the fetal face under the first angle and the second angle is the deep learning algorithm and/or the image restoration algorithm mentioned above, and the difference is that different moments and different angles are used.
  • Multi-volume volume data (four-dimensional volume data) is used as input.
  • the generative model can also obtain more additional information from multi-volume volume data to realize data repair processing. .
  • the repaired volume image data of the fetal face is rendered to obtain a rendered image of the fetal face; wherein, the rendering method may be a conventional method, which is not limited herein.
  • the rendered image is displayed.
  • steps S240 and S250 according to the volume image data of the fetal face after the restoration process, a three-dimensional rendering algorithm such as ray tracing is used to obtain the VR rendering imaging result of the fetal face, and the rendered image is displayed on the display.
  • a three-dimensional rendering algorithm such as ray tracing is used to obtain the VR rendering imaging result of the fetal face, and the rendered image is displayed on the display.
  • a visualization algorithm may be used to render the repaired three-dimensional volume data, so as to obtain a three-dimensional ultrasound image, which is displayed on a display device.
  • the rendering includes, for example, a surface rendering method or a volume rendering method, which is not limited in this embodiment of the present invention.
  • the method may further include rendering the volumetric image data of the fetal face to obtain an initial rendered image of the fetal face.
  • a switching device may also be provided in the ultrasonic imaging system for freely switching the display between the rendered image rendered after the restoration process and the initial rendered image.
  • the switching device may be a button or a key-pressing manner, which is not limited herein.
  • the rendered image is the image obtained after repair processing. Some missing data or occluded areas are repaired. Some data may come from other fetuses. Therefore, when the displayed image is switched to the rendered image obtained after repair processing, the output And display the prompt information obtained by repairing the rendered image, so as to give a prompt to the doctor: the rendered image is obtained after repairing and cannot be used for diagnosis.
  • the above-mentioned fetal facial volume image restoration method repairs the missing, occluded or poor-quality volume image data after acquiring the fetal facial volume image data, so as to improve the imaging quality of the fetal facial and face, and reduce the damage to the fetal facial image.
  • the doctor's reliance on imaging conditions such as fetal posture when collecting the 3D rendering of the ultrasound of the fetal face and face improves the doctor's work efficiency and realizes a more efficient clinical workflow.
  • the ultrasound imaging system 100 provided by the embodiment of the present application may be used to implement the above-mentioned method 200 for reconstructing a volumetric image of the fetal face.
  • the ultrasound imaging system 100 may include the ultrasound probe 110 , the transmit/receive circuit 112 , the processor 114 , and the display 116 , and the relevant descriptions of the various components can be referred to above.
  • the transmitting/receiving circuit 112 is used to excite the ultrasonic probe 110 to transmit ultrasonic waves to the face of the fetus, and receive the echoes of the ultrasonic waves to obtain ultrasonic echo signals;
  • the processor 114 is used for : process the ultrasonic echo signal to obtain the volumetric image data of the fetal face; perform repair processing on the volumetric image data of the fetal face based on the deep learning algorithm and/or image restoration algorithm;
  • the volume image data of the face is rendered to obtain a rendered image of the fetal face;
  • the display 116 is used to display the rendered image.
  • the display 116 is also used to display the rendered image.
  • a switching device may also be provided in the ultrasound imaging system 100 to freely switch the display between the rendered image rendered after the restoration process and the initial rendered image.
  • the prompt information obtained by the restoration of the image is output and displayed, so as to give a prompt to the doctor that the image is obtained by restoration and cannot be used for diagnosis.
  • the ultrasound imaging system 100 of the embodiment of the present application can repair the missing, occluded or poor-quality volume image data, and improve the quality of the rendered imaging of the fetal face.
  • FIG. 3 is a schematic flow chart of a method 300 for restoring a fetal facial volume image according to an embodiment of the present application.
  • the fetal facial volume image restoration method 300 includes the following steps:
  • step S310 ultrasonic waves are transmitted to the face of the fetus through an ultrasonic probe, and echoes of the ultrasonic waves are received to obtain ultrasonic echo signals;
  • step S320 the ultrasonic echo signal is processed to obtain volumetric image data of the fetal face
  • step S330 the volume image data of described fetal facial part is rendered, obtains the initial rendering image of described fetal facial part;
  • step S340 repair processing is performed on the initial rendered image of the fetal face based on a deep learning algorithm and/or an image repair algorithm
  • step S350 the rendered image after the restoration process is displayed.
  • Steps S310 , S320 and S350 in the fetal facial volume image restoration method 300 according to the embodiment of the present application are generally the same as steps S210 , S220 and S250 in the fetal facial volume image restoration method 200 described with reference to FIG. 2 . Similarly, for the sake of brevity, the same details are not repeated here.
  • steps S330 and S340 and steps S230 and S240 first perform repair processing on the volumetric image data of the fetal face, and then render the repaired volumetric image data of the fetal face. , and then obtain the rendered image of the fetal face, and in step S330 and step S340, the volume image data of the fetal face is first rendered to obtain the initial rendered image of the fetal face, and then the initial rendered image is Repair processing is performed, so the difference lies in the order of rendering and repair processing.
  • the rendering method in step S330 may refer to step S240, and the repairing process in step S340 may refer to the repairing method in step S240, except that the repaired data types are different, but the methods are similar, and will not be repeated here.
  • the ultrasound imaging system 100 provided by the embodiment of the present application may be used to implement the above-mentioned method 300 for reconstructing the volumetric image of the fetal face.
  • the ultrasound imaging system 100 may include the ultrasound probe 110 , the transmit/receive circuit 112 , the processor 114 , and the display 116 , and the relevant descriptions of the various components can be referred to above.
  • the transmitting/receiving circuit 112 is used to excite the ultrasonic probe 110 to transmit ultrasonic waves to the face of the fetus, and receive the echoes of the ultrasonic waves to obtain ultrasonic echo signals; the processor 114 is used to: Render the volumetric image data of the fetal face to obtain an initial rendered image of the fetal face; perform repair processing on the initial rendered image of the fetal face based on a deep learning algorithm and/or an image restoration algorithm; the display 116 is used to display Fix the processed rendered image.
  • the method 300 and the ultrasound imaging system of the embodiment of the present application repair the missing, occluded or poor-quality volume image data, thereby improving the rendering and imaging performance of the fetal face. It can reduce the dependence of doctors on imaging conditions such as fetal posture when collecting ultrasound 3D renderings of the face and face of the fetus, improve the work efficiency of doctors, and achieve a more efficient clinical workflow.
  • FIG. 4 is a schematic flowchart of a method 400 for restoring a fetal facial volume image according to an embodiment of the present application.
  • a method 400 for restoring a fetal facial volume image includes the following steps:
  • step S410 obtain volumetric image data of the fetal face
  • step S420 repair processing is performed on the volumetric image data of the fetal face
  • step S430 rendering the repaired volume image data of the fetal face to obtain a rendered image of the fetal face
  • step S440 the rendered image is displayed.
  • Steps S430 and S440 in the fetal facial volume image restoration method 400 according to the embodiment of the present application are substantially similar to steps S240 and S250 in the fetal facial volume image restoration method 200 described with reference to FIG. 2 .
  • steps S410 and S420 are mainly described in detail below.
  • the method for obtaining the volume image data of the fetal face in step S410 is not limited to a certain one, and steps S210, In step S220, for specific steps, reference may be made to the foregoing related content, which will not be repeated here. Of course, methods other than steps S210 and S220 may also be selected, which are not limited herein.
  • step S420 the volume image data of the face of the fetus is repaired to obtain the complete volume image data of the face of the fetus, wherein the repair method can be based on a deep learning algorithm and/or an image repair algorithm, such as the fetus described in Figure 2.
  • the repair method can be based on a deep learning algorithm and/or an image repair algorithm, such as the fetus described in Figure 2.
  • the specific steps can be referred to the above-mentioned related content, which will not be repeated here.
  • the ultrasound imaging system 100 provided by the embodiment of the present application can be used to implement the above-mentioned method 400 for reconstructing the volumetric image of the fetal face.
  • the ultrasound imaging system 100 may include the ultrasound probe 110, the transmit/receive circuit 112, the processor 114, and the display 116, and the relevant description of each component can be referred to above.
  • the transmitting/receiving circuit 112 is used to excite the ultrasonic probe 110 to transmit ultrasonic waves to the face of the fetus, and receive the echoes of the ultrasonic waves to obtain ultrasonic echo signals;
  • the processor 114 is used for : performing repair processing on the volume image data of the fetal face; rendering the repaired volume image data of the fetal face to obtain a rendered image of the fetal face;
  • the display 116 is used to display the rendered image.
  • the method 400 for restoring the volume image of the fetal face and the ultrasound imaging system of the embodiment of the present application restore the missing, occluded or poor-quality volume image data, thereby improving the rendering and imaging performance of the fetal face. It can reduce the dependence of doctors on imaging conditions such as fetal posture when collecting ultrasound 3D renderings of the face and face of the fetus, improve the work efficiency of doctors, and achieve a more efficient clinical workflow.
  • FIG. 5 is a schematic flowchart of a method 500 for restoring a volumetric image of a fetal face and face according to an embodiment of the present application.
  • a method 500 for restoring a fetal facial volume image includes the following steps:
  • step S510 obtain the volumetric image data of the fetal face
  • step S520 the volume image data of the fetal face is rendered to obtain a rendered image of the fetal face
  • step S530 repair processing is performed on the rendered image of the fetal face
  • step S540 the rendered image after the restoration process is displayed.
  • Step S540 in the fetal face volume image restoration method 500 according to the embodiment of the present application is substantially similar to step S250 in the fetal face volume image restoration method 200 described with reference to FIG. Details.
  • the method for obtaining the volume image data of the fetal face in step S510 is not limited to a certain one, and steps S210, In step S220, for specific steps, reference may be made to the foregoing related content, which will not be repeated here. Of course, methods other than steps S210 and S220 may also be selected, which are not limited herein.
  • steps S520 and S530 and steps S230 and S240 first perform repair processing on the volumetric image data of the fetal face, and then render the repaired volumetric image data of the fetal face. , and then obtain the rendered image of the fetal face, and in step S520 and step S530, the volume image data of the fetal face is first rendered to obtain the initial rendered image of the fetal face, and then the initial rendered image Repair processing is performed, so the difference lies in the order of rendering and repair processing.
  • the rendering method in step S520 may refer to step S240, and the repairing process in step S520 may refer to the repairing method in step S240, except that the repaired data types are different, but the methods are similar, and will not be repeated here.
  • step S530 the volume image data of the face of the fetus is repaired to obtain the complete volume image data of the face of the fetus, wherein the repair method can be based on a deep learning algorithm and/or an image repair algorithm, such as the fetus described in Figure 2.
  • the repair method can be based on a deep learning algorithm and/or an image repair algorithm, such as the fetus described in Figure 2.
  • the specific steps can be referred to the above-mentioned related content, which will not be repeated here.
  • the ultrasound imaging system 100 provided by the embodiment of the present application can be used to implement the above-mentioned method 500 for reconstructing the volumetric image of the fetal face.
  • the ultrasound imaging system 100 may include the ultrasound probe 110 , the transmit/receive circuit 112 , the processor 114 , and the display 116 , and the relevant descriptions of the various components can be referred to above.
  • the transmitting/receiving circuit 112 is used to excite the ultrasonic probe 110 to transmit ultrasonic waves to the face of the fetus, and receive the echoes of the ultrasonic waves to obtain ultrasonic echo signals; the processor 114 is used to: The volumetric image data of the fetal face is rendered to obtain an initial rendered image of the fetal face; restoration processing is performed on the initial rendered image of the fetal face; the display 116 is configured to display the restored rendered image.
  • the method 500 and the ultrasound imaging system of the embodiment of the present application repair the missing, occluded or poor-quality volume image data, thereby improving the rendering and imaging performance of the fetal face. It can reduce the dependence of doctors on imaging conditions such as fetal posture when collecting ultrasound 3D renderings of the face and face of the fetus, improve the work efficiency of doctors, and achieve a more efficient clinical workflow.
  • the disclosed apparatus and method may be implemented in other manners.
  • the device embodiments described above are only illustrative.
  • the division of the units is only a logical function division. In actual implementation, there may be other division methods.
  • multiple units or components may be combined or May be integrated into another device, or some features may be omitted, or not implemented.
  • 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.
  • a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all functions of some modules according to the embodiments of the present application.
  • DSP digital signal processor
  • the present application can also be implemented as a program of apparatus (eg, computer programs and computer program products) for performing part or all of the methods described herein.
  • Such a program implementing the present application may be stored on a computer-readable medium, or may be in the form of one or more signals. Such signals may be downloaded from Internet sites, or provided on carrier signals, or in any other form.

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Abstract

一种胎儿颜面部容积图像修复方法和超声成像系统，该方法包括：通过超声探头向胎儿颜面部发射超声波，并接收所述超声波的回波，以获得超声回波信号；对所述超声回波信号进行处理，得到所述胎儿颜面部的容积图像数据；基于深度学习算法和/或图像修复算法对所述胎儿颜面部的容积图像数据进行修复处理；对修复处理后的所述胎儿颜面部的容积图像数据进行渲染，得到所述胎儿颜面部的渲染图像；显示所述渲染图像。所述方法根据现有容积图像数据对缺失、遮挡或质量较差的容积图像数据进行修复，提高胎儿颜面部渲染成像的质量，以降低医生采集胎儿颜面部超声三维渲染图时对胎儿姿态等成像条件的依赖，提升医生的工作效率，实现更高效的临床工作流。

Description

胎儿颜面部容积图像修复方法和超声成像系统
说明书
技术领域
本申请涉及超声成像技术领域，更具体地涉及胎儿颜面部容积图像修复方法和超声成像系统。
背景技术
现代医学影像检查中，超声技术因其高可靠性、快速便捷、实时成像以及可重复检查等优点，已经成为应用最广、使用频率最高同时新技术普及应用最快的检查手段之一。尤其是在产科中，超声已经是产科中最主要的检查技术；超声检查可以避免X光，CT等技术的放射性危害；同时相较于MR技术具有可实时成像，价格低廉的优点。
产科的超声检查中可以对胎儿进行包括形态学、呼吸系统、神经系统等生理系统的全方位评估和筛查，提前发现各类新生儿病理问题。伴随着3D/4D等新的超声技术的出现，也逐渐增加了一些新的产科检查内容，例如胎儿的颜面部3D/4D成像。
在临床使用中，超声数据渲染的结果往往受超声数据限制，例如胎儿姿态、胎儿在母体内的相对位置以及胎盘遮挡等问题都会导致渲染结果成像质量变差，医生通常需要反复多次采集，有时还需要孕妇反复走动以改变胎儿姿态和位置，该过程费时费力，影响医生工作效率。
发明内容
在发明内容部分中引入了一系列简化形式的概念，这将在具体实施方式部分中进一步详细说明。本发明的发明内容部分并不意味着要试图限定出所要求保护的技术方案的关键特征和必要技术特征，更不意味着试图确定所要求保护的技术方案的保护范围。
本申请实施例第一方面提供一种胎儿颜面部容积图像修复方法，包括：
通过超声探头向胎儿颜面部发射超声波，并接收所述超声波的回波，以获得超声回波信号；
对所述超声回波信号进行处理，得到所述胎儿颜面部的容积图像数据；
基于深度学习算法和/或图像修复算法对所述胎儿颜面部的容积图像数据进行修复处理；
对修复处理后的所述胎儿颜面部的容积图像数据进行渲染，得到所述胎儿颜面部的渲染图像；
显示所述渲染图像。
本申请实施例第二方面提供一种胎儿颜面部容积图像修复方法，包括：
通过超声探头向胎儿颜面部发射超声波，并接收所述超声波的回波，以获得超声回波信号；
对所述超声回波信号进行处理，得到所述胎儿颜面部的容积图像数据；
对所述胎儿颜面部的容积图像数据进行渲染，得到所述胎儿颜面部的初始渲染图像；
基于深度学习算法和/或图像修复算法对所述胎儿颜面部的初始渲染图像进行修复处理；
显示修复处理后的所述渲染图像。
本申请实施例第三方面提供一种胎儿颜面部容积图像修复方法，包括：
获取胎儿颜面部的容积图像数据；
对所述胎儿颜面部的容积图像数据进行修复处理；
对修复处理后的所述胎儿颜面部的容积图像数据进行渲染，得到所述胎儿颜面部的渲染图像；
显示所述渲染图像。
本申请实施例第四方面提供一种胎儿颜面部容积图像修复方法，包括：
获取胎儿颜面部的容积图像数据；
对所述胎儿颜面部的容积图像数据进行渲染，得到所述胎儿颜面部的初始渲染图像；
对所述胎儿颜面部的初始渲染图像进行修复处理；
显示修复处理后的所述渲染图像。
根据权利要求所述的胎儿颜面部容积图像修复方法，其特征在于，所述修复处理包括对所述胎儿颜面部缺失的部分数据进行填补，和/或对所述胎儿颜面部被遮挡的部分数据进行去除。
本申请实施例第五方面提供一种超声成像系统，包括：
超声探头；
发射电路，用于激励所述超声探头发射超声波；
接收电路，用于通过所述超声探头接收所述超声波的回波，以获得超声回波信号；
显示器，用于输出可视化信息；
处理器，用于执行如前文所述方法的步骤。
根据本申请实施例的胎儿颜面部容积图像修复方法和超声成像系统，在获取胎儿颜面部的容积图像数据之后，对缺失、遮挡或质量较差的容积图像数据进行修复，提高胎儿颜面部渲染成像的质量，以降低医生采集胎儿颜面部的图像数据时对胎儿姿态等成像条件的依赖，提升医生的工作效率，实现更高效的临床工作流。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
在附图中：
图1示出根据本申请实施例的超声成像系统的示意性框图；
图2示出根据本发明一实施例的胎儿颜面部容积图像修复方法的示意性流程图；
图3示出根据本发明另一实施例的胎儿颜面部容积图像修复方法的示意性流程图；
图4示出根据本发明另一实施例的胎儿颜面部容积图像修复方法的示意性流程图；
图5示出根据本发明又一实施例的胎儿颜面部容积图像修复方法的示意性流程图。
具体实施方式
为了使得本申请的目的、技术方案和优点更为明显，下面将参照附图详细描述根据本申请的示例实施例。显然，所描述的实施例仅仅是本申请的一 部分实施例，而不是本申请的全部实施例，应理解，本申请不受这里描述的示例实施例的限制。基于本申请中描述的本申请实施例，本领域技术人员在没有付出创造性劳动的情况下所得到的所有其它实施例都应落入本申请的保护范围之内。
在下文的描述中，给出了大量具体的细节以便提供对本申请更为彻底的理解。然而，对于本领域技术人员而言显而易见的是，本申请可以无需一个或多个这些细节而得以实施。在其他的例子中，为了避免与本申请发生混淆，对于本领域公知的一些技术特征未进行描述。
应当理解的是，本申请能够以不同形式实施，而不应当解释为局限于这里提出的实施例。相反地，提供这些实施例将使公开彻底和完全，并且将本申请的范围完全地传递给本领域技术人员。
在此使用的术语的目的仅在于描述具体实施例并且不作为本申请的限制。在此使用时，单数形式的“一”、“一个”和“所述/该”也意图包括复数形式，除非上下文清楚指出另外的方式。还应明白术语“组成”和/或“包括”，当在该说明书中使用时，确定所述特征、整数、步骤、操作、元件和/或部件的存在，但不排除一个或更多其它的特征、整数、步骤、操作、元件、部件和/或组的存在或添加。在此使用时，术语“和/或”包括相关所列项目的任何及所有组合。
为了彻底理解本申请，将在下列的描述中提出详细的结构，以便阐释本申请提出的技术方案。本申请的可选实施例详细描述如下，然而除了这些详细描述外，本申请还可以具有其他实施方式。
本申请提供的胎儿颜面部容积图像修复方法和超声成像系统可以应用于人体，也可以应用于各种动物。
下面，首先参考图1描述根据本申请一个实施例的超声成像系统，图1示出了根据本申请实施例的超声成像系统100的示意性结构框图。
如图1所示，超声成像系统100包括超声探头110、发射/接收电路112、处理器114、显示器116以及存储器118。进一步地，超声成像系统100还可以包括波束合成电路和发射/接收选择开关等。
具体地，超声探头110包括多个换能器阵元，多个换能器阵元可以排列成一排构成线阵，或排布成二维矩阵构成面阵，多个换能器阵元也可以构成凸阵列。换能器用于根据激励电信号发射超声波，或将接收的超声波转换为 电信号，因此每个阵元可用于实现电脉冲信号和超声波的相互转换，从而实现向被测对象的目标区域的组织发射超声波、也可用于接收经组织反射回的超声波回波。在进行超声成像时，可通过发射序列和接收序列控制哪些换能器用于发射超声波，哪些换能器用于接收超声波，或者控制换能器分时隙用于发射超声波或接收超声波的回波。参与超声波发射的换能器可以同时被电信号激励，从而同时发射超声波；或者，参与超声波束发射的换能器也可以被具有一定时间间隔的若干电信号激励，从而持续发射具有一定时间间隔的超声波。
发射/接收电路112可以通过发射/接收选择开关与超声探头110连接。发射/接收选择开关也可以被称为发送/接收控制器，其可以包括发送控制器和接收控制器，发送控制器用于激励超声探头110经由发射电路向胎儿颜面部所在区域发射超声波；接收控制器用于通过超声探头110经由接收电路接收从胎儿颜面部所在区域返回的超声回波，从而获得超声回波数据。之后，发射/接收电路112将超声回波的电信号送入波束合成电路，波束合成电路对该电信号进行聚焦延时、加权和通道求和等处理，然后将处理后的超声回波数据送入处理器114。
可选地，处理器114可以通过软件、硬件、固件或其任意组合来实现，可以使用电路、单个或多个专用集成电路(Application Specific Integrated Circuit，ASIC)、单个或多个通用集成电路、单个或多个微处理器、单个或多个可编程逻辑器件、或者前述电路和/或器件的任意组合、或者其他适合的电路或器件，从而使得处理器114可以执行本说明书中的各个实施例中的方法的相应步骤。并且，处理器114可以控制所述超声成像系统100中的其它组件以执行期望的功能。
处理器114对其接收到的超声回波数据进行处理，得到胎儿颜面部的容积图像数据。作为示例，超声探头110在一系列扫描平面内发射/接收超声波，由处理器114根据其三维空间关系进行整合，实现胎儿颜面部在空间的扫描以及图像的重建。最后，由处理器114对其进行去噪、平滑、增强等部分或全部图像后处理步骤后，获取胎儿颜面部的容积图像数据。处理器114可以获取胎儿颜面部的容积图像数据。处理器114还用于对所述容积图像数据进行修复处理，然后对修复处理后的数据进行渲染，以得到渲染图像；处理器114还用于对所述容积图像数据进行渲染，得到所述胎儿颜面部的初始渲染 图像；对所述胎儿颜面部的初始渲染图像进行修复处理。处理器114得到的渲染图像可以存储于存储器中或在显示器116上显示。
显示器116与处理器114连接，显示器116可以为触摸显示屏、液晶显示屏等；或者显示器116可以为独立于超声成像系统100之外的液晶显示器、电视机等独立显示设备；或者显示器116可以是智能手机、平板电脑等电子设备的显示屏，等等。其中，显示器116的数量可以为一个或多个。例如，显示器116可以包括主屏和触摸屏，主屏主要用于显示超声图像，触摸屏主要用于人机交互。
显示器116可以显示处理器114得到的超声图像。此外，显示器116在显示超声图像的同时还可以提供给用户进行人机交互的图形界面，在图形界面上设置一个或多个被控对象，提供给用户利用人机交互装置输入操作指令来控制这些被控对象，从而执行相应的控制操作。例如，在图形界面上显示图标，利用人机交互装置可以对该图标进行操作，用来执行特定的功能。
可选地，超声成像系统100还可以包括显示器116之外的其他人机交互装置，其与处理器114连接，例如，处理器114可以通过外部输入/输出端口与人机交互装置连接，外部输入/输出端口可以是无线通信模块，也可以是有线通信模块，或者两者的组合。外部输入/输出端口也可基于USB、如CAN等总线协议、和/或有线网络协议等来实现。
其中，人机交互装置可以包括输入设备，用于检测用户的输入信息，该输入信息例如可以是对超声波发射/接收时序的控制指令，可以是在超声图像上绘制出点、线或框等的操作输入指令，或者还可以包括其他指令类型。输入设备可以包括键盘、鼠标、滚轮、轨迹球、移动式输入设备(比如带触摸显示屏的移动设备、手机等等)、多功能旋钮等等其中之一或者多个的结合。人机交互装置还可以包括诸如打印机之类的输出设备。
超声成像系统100还可以包括存储器，用于存储处理器执行的指令、存储接收到的超声回波、存储超声图像，等等。存储器可以为闪存卡、固态存储器、硬盘等。其可以为易失性存储器和/或非易失性存储器，为可移除存储器和/或不可移除存储器等。
应理解，图1所示的超声成像系统100所包括的部件只是示意性的，其可以包括更多或更少的部件。本申请对此不限定。
下面，将参考图2描述根据本申请实施例的胎儿颜面部容积图像修复方法。图2是本申请实施例的胎儿颜面部容积图像修复方法200的一个示意性流程图。
如图2所示，本申请一个实施例的胎儿颜面部容积图像修复方法200包括如下步骤：
首先，在步骤S210，通过超声探头向胎儿颜面部发射超声波，并接收所述超声波的回波，以获得超声回波信号。
其中，胎儿的颜面部的渲染成像可以直观的发现胎儿的颜面部发育异常，例如兔唇等；另一方面，相比于抽象的B模图像，颜面部的渲染图能更生动的展示胎儿在宫腔内的实际状况，可以在早孕期实现胎儿的结构检查及畸形筛查，能够尽可能早地为孕妇提供相关的妊娠信息。
示例性地，可以基于图1所示的超声成像系统100进行超声图像采集。用户移动超声探头110选择合适的位置和角度，发射/接收电路120中的发射电路将一组经过延迟聚焦的脉冲发送到超声探头110，超声探头110沿2D扫描平面向胎儿颜面部发射超声波形。超声探头110接收到反射回的超声回波后，将其转化为电信号，由波束合成电路对多次发射/接收得到的信号进行聚焦延时、加权和通道求和的处理，实现波束合成，再送入处理器114进行后续的信号处理。
在步骤S220，对所述超声回波信号进行处理，得到所述胎儿颜面部的容积图像数据。
其中，该胎儿颜面部的容积图像数据可是三维体数据，还可以是四维体数据，在此不做限定。
在本申请的一实施例中，可以对超声探头110在一系列扫描平面内发射/接收获得的超声回波的三维空间关系进行整合，从而实现胎儿颜面部在三维空间的扫描以及3D图像的重建。最后，经过去噪、平滑、增强等部分或全部图像后处理步骤后，获得胎儿颜面部的容积图像数据。
在本申请的一实施例中，一个完整的探头扇扫周期都经过以上处理完成后即获得一卷重建前的(极坐标)体数据，然后将体数据送入3D重建模块，即可获得重建后的(直角坐标系)体数据。经过图像绘制，渲染等后处理，得到可视化信息，然后送到显示器进行显示。其中，4D超声则是在时间维度上重复以上过程，获得多卷体数据并逐个显示。
在下面的实施例中，在不进行特殊说明的情况下，均以三维体数据为例进行详细的说明。
其中，可以获取胎儿头部的三维超声数据，也可以仅获取胎儿颜面部区域的三维数据。
在本申请实施例中，在该三维体数据中还可以截取包含关键信息的二维切面，以在后续的步骤中逐帧的对多个二维切面分别进行修复。
在步骤S230，基于深度学习算法和/或图像修复算法对所述胎儿颜面部的容积图像数据进行修复处理。
在本申请的一实施例中，先对该胎儿颜面部的容积图像数据进行修复处理，然后将修复后的该胎儿颜面部的容积图像数据进行渲染，进而得到该胎儿颜面部的渲染图像。
在该步骤中，对该胎儿颜面部的容积图像数据进行修复处理可以根据该容积图像数据的具体情况进行不同的修复处理，例如在一实施例中当该胎儿颜面部的部分数据缺失时，该修复处理可以为对缺失的部分数据进行填补；在另一实施例中，当该胎儿颜面部的部分被遮挡时，既可以对该遮挡部分进行填补，还可以去除对胎儿颜面部形成遮挡的数据，以露出胎儿颜面部被遮挡的区域，从而完整的显示胎儿颜面部。
该修复处理可以使用基于深度学习算法的增强方法，以达到更好的增强效果；也可以用基于传统的图像修复算法的增强方法达到一定增强效果。
在本申请该修复处理中，可以直接使用三维体数据作为输入进行该修复处理，也可以选取该三维体数据中的多个二维切面并逐帧的对多个二维切面分别做修复处理，达到数据修复的目的。在上述两种方法中，该修复处理方法在实现时只是具体使用的算法维度和特征维度等存在差异，其修复处理的具体步骤是类似的，都可以使用基于深度学习算法或传统图像修复方法实现。
在本申请的一实施例中通过深度学习算法对该胎儿颜面部的容积图像数据进行修复处理。具体地，基于深度学习算法生成式模型(后续简称生成模型)的修复方法是通过生成模型来学习大量胎儿颜面部的容积图像数据的特征空间分布，学习好的生成模型能够推理出输入的低质量的容积图像数据的缺失部分或者遮挡等影响因素，进而生成与输入的低质量的容积图像数据相对应的高质量的修复后的容积图像数据，从而实现胎儿颜面部的修复。
需要说明的是，该胎儿颜面部能完整显示的容积图像数据称为高质量数 据，该胎儿颜面部缺失的数据和/或胎儿颜面部被遮挡的容积图像数据称为低质量数据，即需要通过修复，以得到经修复处理的高质量数据，在不进行特殊说明的情况下，该高质量数据和该低质量数据均参照该解释。
深度学习算法对该胎儿颜面部的容积图像数据进行修复处理的具体步骤包括：
第一：建立数据库，该步骤为构建深度学习算法的生成模型训练所需的数据库，以用于生成模型学习大量胎儿颜面部的数据的特征空间分布。
其中，该数据库包括胎儿颜面部能完整显示的数据以及胎儿颜面部缺失的数据和/或胎儿颜面部被遮挡的数据。
在一实施例中，该数据库包括若干成对或不成对的胎儿颜面部的三维超声数据，其中成对的三维超声数据是指低质量数据和高质量数据来自同一个胎儿，且采集条件相近；其中不成对的数据是指数据库中一对低质量数据和高质量数据并非来自于同一个胎儿。需要说明的是，数据是否成对只影响最终使用何种生成模型实现增强，且两者也并不是互斥的方法，即可以单独存在，也可以同时存在，在此不做限定。
第二：基于该数据库中建立具有修复处理功能的生成模型。
其中，该生成模型具有该修复处理的功能，用于以容积图像数据或容积图像数据中取出的切面作为输入，输出修复处理后的容积图像数据或切面。
可选地，常见的深度学习算法的生成式模型包括生成对抗网络(Generative Adversarial Network，GAN)，除了该抗生成网络外，基于深度学习算法的生成模型还包括很多其他变种，例如条件生成对抗网络(C-GAN)、W-GAN、Cycle-GAN等，均可实现低质量数据的增强。
在本申请的一实施例中，以生成对抗网络(Generative Adversarial Network，GAN)为例对基于该数据库中建立具有修复处理功能的生成模型的方法进行详细的说明。该生成对抗网络(Generative Adversarial Network，GAN)的构成通常包括两部分：生成器(generator)和鉴别器(discriminator)。
其中，生成器由编码器(encoder)和解码器(decoder)两部分构成，其中编码器的作用是将输入的胎儿颜面部的容积图像数据映射到特征空间，解码器则会将编码器的结果变换到增强后的特征空间，并且输出增强的结果，即得到修复处理后的该胎儿颜面部的容积图像数据。
可选地，该生成器除了包括编码器和解码器之外还会包括一个用来训练 生成器的判别器，判别器以生成器的结果作为输入；通过数据训练，判别器能够区分输入的胎儿颜面部的容积图像数据属于低质量数据还是高质量数据，因此可以用以评估生成器的输出是否符合增强后的数据；当生成器的输出的结果更接近低质量数据时，判别器就会给一个很大的惩罚，训练生成器，使其输出结果接近高质量数据；反之判别器就会给一个很小的惩罚。使用成对或不成对胎儿颜面部的容积图像数据训练生成式模型，得到的生成模型可将输入的低质量数据增强为一个高质量数据。
第三：在该数据库中建立该生成模型之后，输入胎儿颜面部的容积图像数据，如上所述胎儿颜面部的容积图像数据可以为三维体数据或四维体数据，并不局限于某一种。
第四：根据该生成模型对输入胎儿颜面部的容积图像数据进行修复处理，以得到修复处理后的该胎儿颜面部的容积图像数据，并输出修复处理后的该胎儿颜面部的容积图像数据。
在本申请的另一实施例中，胎儿颜面部的容积图像数据使用传统的图像修复算法进行该修复处理。传统的图像修复算法通常使用图像块匹配(Patch Match)的方法实现，具体包括：
将胎儿颜面部的容积图像数据分为很小的图像块，然后通过计算胎儿颜面部的容积图像数据缺失区域或被遮挡区域的图像特征，该图像特征包括但不限于轮廓特征，纹理特征等，再在胎儿颜面部的容积图像数据中未缺失区域寻找一个与待修复的图像块的图像特征最匹配、相似度最高的图像块来填充当前缺失的图像块，实现图像修复。
在本申请的另一实施例中，可以建立数据库，通过大量样本的图像块构建一个图像块数据库，在使用图像修复算法进行修复时，首先选取当前输入的胎儿颜面部容积图像中的图像块，如果在当前输入的胎儿颜面部容积图像未缺失区域没有找到足够匹配的图像块，则在该图像块数据库中进行匹配，使用其他样本图像上的图像块来匹配当前待修复的胎儿颜面部容积图像。
在本申请的一实施例中，该深度学习算法和/或图像修复算法不需要用户指定该容积图像数据有问题的(缺失或遮挡)区域，由生成模型自动学习缺失或遮挡的位置。
此外，在上述两种修复处理的算法中，获取胎儿颜面部容积图像数据之后，可以先指定该胎儿颜面部容积图像数据中数据缺失或被遮挡的区域，然 后在指定的该区域中对数据缺失或被遮挡的区域进行该修复处理。
其中，指定该胎儿颜面部的容积图像数据中数据缺失或被遮挡的区域可以为自动方式或者手动方式，并不局限于某一种。
在本申请的另一实施例中，还可以通过用户通过手动方式在交互界面选用容积图像数据中数据缺失或被遮挡的区域，例如由用户通过交互界面使用点选、框定等方法指定缺失或遮挡的位置。又例如用户指定的区域以热点图(heat map)或掩模(mask)等形式进行标记。
在一示例中，在该深度学习算法中，用户指定的位置以热点图(heat map)或掩模(mask)等形式，与低质量数据一起作为生成模型的数据输入，然后通过前文所述的生成模型实现对低质量数据的修复处理。
在另一示例中，在该传统图像修复算法中，可以根据用户指定的位置，确定缺失或遮挡范围，再通过该传统的图像修复算法对具体位置进行修复。
需要说明的是上述手动方式中只是先通过手动的方式选定数据缺失或遮挡的位置或区域，然后再通过前文所述的深度学习算法或图像修复算法从大量样本中学习该容积图像数据的规律，进行该修复处理，从而实现对低质量数据的增强。
上述修复处理方法的数据并不局限于某一种，例如三维体数据或四维体数据。其中，该修复处理可以通过多个三维体数据修复处理，也可以通过单个三维体数据直接修复处理。
其中，使用单个三维体数据进行修复处理是直接根据三维体数据获得胎儿颜面部的结构特征，然后推测出缺失部分的具体结构，如前文所述的修复处理方法通过上述两种算法从大量样本中学习数据分布规律，当输入低质量数据时，生成模型可以根据输入的图像容积数据特征，推理出对应的高质量数据。
使用多卷三维体数据(即四维体数据)的修复处理是利用在不同时刻、不同角度下采集的多卷不同三维体数据进行合成，由于不同时刻、不同角度下采集的三维体数据状态都不一样，因此可以通过该方法将原本缺失或者遮挡的部分数据进行补全。例如在某个角度下该容积图像数据有缺失或者遮挡的部分，在其他角度可能是完整的，因此可以通过多个角度的数据相互融合得到一个更完整的数据。
在本申请的一实施例中，在多卷体数据中，在第一角度下胎儿颜面部容 积图像数据具有数据缺失或被遮挡的第一区域；在第二角度下胎儿颜面部容积图像数据中该第一区域能完整显示；将该第一角度和第二角度下的胎儿颜面部的容积图像数据相互融合，以将该第一角度下数据缺失或被遮挡的第一区域修复。
其中，上述将第一角度和第二角度下的胎儿颜面部的容积图像数据相互融合的方法为前文提及的深度学习算法和/或图像修复算法，不同之处在于使用不同时刻、不同角度的多卷体数据(四维体数据)作为输入，生成模型除了依靠从数据库中学习数据分布来推理出高质量数据以外，还可以从多卷体数据中获得更多额外的信息以实现数据的修复处理。
在该步骤S240中，对修复处理后的所述胎儿颜面部的容积图像数据进行渲染，得到所述胎儿颜面部的渲染图像；其中，该渲染方法可以为常规方法，在此不做限定。在该步骤S250中，显示所述渲染图像。
在步骤S240和步骤S250中根据修复处理后的该胎儿颜面部的容积图像数据，使用光线跟踪等三维渲染算法得到胎儿颜面部的VR渲染成像结果，将渲染图像在显示器中进行显示。
在一实施例中，获取修复后的三维体数据之后，可以对修复后的三维体数据使用可视化算法进行渲染，从而获得三维超声图像，并利用显示设备进行显示。该渲染例如包括表面绘制方法或体绘制方法等，本发明实施例对此不做限制。
在该修复方法中，在该步骤S220中，在得到该胎儿颜面部的容积图像数据之后，还可以包括对该胎儿颜面部的容积图像数据进行渲染，以得到胎儿颜面部的初始渲染图像。
在该超声成像系统中还可以设置切换装置，用于将修复处理后渲染得到的该渲染图像与该初始渲染图像之间的显示自由切换。其中，该切换装置可以为按钮，按键的方式，在此不做限定。
其中，该渲染图像为修复处理后得到的图像，部分的缺失数据或被遮挡的区域经修复得到，有些数据可能来源于其他胎儿，因此当显示图像切换至修复处理后得到的渲染图像时，输出并显示该渲染图像经修复得到的提示信息，以向医生给出提示：该渲染图像是经修复得出的，不能用于诊断。
综上所述，上述胎儿颜面部容积图像修复方法在获取胎儿颜面部的容积图像数据之后，对缺失、遮挡或质量较差的容积图像数据进行修复，提高胎 儿颜面部的成像的质量，以降低医生采集胎儿颜面部超声三维渲染图时对胎儿姿态等成像条件的依赖，提升医生的工作效率，实现更高效的临床工作流。
现在重新参照图1，本申请实施例所提供的超声成像系统100可以用于实现上述胎儿颜面部容积图像修复方法200。如上所述，超声成像系统100可以包括超声探头110、发射/接收电路112、处理器114以及显示器116，各个部件的相关描述可以参照上文。
当用于实现超声成像方法200时，发射/接收电路112用于激励该超声探头110向胎儿颜面部发射超声波，并接收该超声波的回波，以获得超声回波信号；处理器114，用于：对该超声回波信号进行处理，得到该胎儿颜面部的容积图像数据；基于深度学习算法和/或图像修复算法对该胎儿颜面部的容积图像数据进行修复处理；对修复处理后的该胎儿颜面部的容积图像数据进行渲染，得到该胎儿颜面部的渲染图像；显示器116用于显示该渲染图像。
在一个实施例中，显示器116还用于显示该渲染图像。超声成像系统100中还可以设置切换装置，用于将修复处理后渲染得到的渲染图像与该初始渲染图像之间的显示自由切换。当显示图像切换至修复处理后渲染得到的渲染图像时，输出并显示该图像经修复得到的提示信息，以向医生给出提示：该图像是经修复得出的，不能用于诊断。
以上仅描述了超声成像系统100各部件的主要功能，更多细节参见对胎儿颜面部容积图像修复方法200进行的相关描述。本申请实施例的超声成像系统100能够对缺失、遮挡或质量较差的容积图像数据进行修复，提高胎儿颜面部渲染成像的质量。
下面，将参考图3描述根据本申请另一实施例的胎儿颜面部容积图像修复方法。图3是本申请实施例的胎儿颜面部容积图像修复方法300的一个示意性流程图。
如图3所示，该胎儿颜面部容积图像修复方法300包括如下步骤：
在步骤S310，通过超声探头向胎儿颜面部发射超声波，并接收所述超声波的回波，以获得超声回波信号；
在步骤S320，对所述超声回波信号进行处理，得到所述胎儿颜面部的容积图像数据；
在步骤S330，对所述胎儿颜面部的容积图像数据进行渲染，得到所述胎 儿颜面部的初始渲染图像；
在步骤S340，基于深度学习算法和/或图像修复算法对所述胎儿颜面部的初始渲染图像进行修复处理；
在步骤S350，显示修复处理后的所述渲染图像。
根据本申请实施例的胎儿颜面部容积图像修复方法300中的步骤S310、步骤S320以及步骤S350与参考图2描述的胎儿颜面部容积图像修复方法200中的步骤S210、步骤S220以及步骤S250大体上类似，为了简洁，此处不再赘述相同的细节内容。
其中，步骤S330和步骤S340与步骤S230和步骤S240的区别在于步骤S230和步骤S240先对该胎儿颜面部的容积图像数据进行修复处理，然后将修复后的该胎儿颜面部的容积图像数据进行渲染，进而得到该胎儿颜面部的渲染图像，而在步骤S330和步骤S340中则是先对该胎儿颜面部的容积图像数据进行渲染，得到该胎儿颜面部的初始渲染图像，然后再对初始渲染图像进行修复处理，因此区别在于渲染和修复处理的先后顺序不同。
其中，步骤S330中的渲染方法可以参照步骤S240，其中，步骤S340中的修复处理可以参照步骤S240中的修复方法，只是修复的数据类型不同，但方法类似，在此不再赘述。
现在重新参照图1，本申请实施例所提供的超声成像系统100可以用于实现上述胎儿颜面部容积图像修复方法300。如上所述，超声成像系统100可以包括超声探头110、发射/接收电路112、处理器114以及显示器116，各个部件的相关描述可以参照上文。
当用于实现超声成像方法300时，发射/接收电路112用于激励该超声探头110向胎儿颜面部发射超声波，并接收该超声波的回波，以获得超声回波信号；处理器114用于：对该胎儿颜面部的容积图像数据进行渲染，得到该胎儿颜面部的初始渲染图像；基于深度学习算法和/或图像修复算法对该胎儿颜面部的初始渲染图像进行修复处理；显示器116用于显示修复处理后的渲染图像。
以上仅描述了超声成像系统100各部件的主要功能，更多细节参见对胎儿颜面部容积图像修复方法300进行的相关描述。
本申请实施例的胎儿颜面部容积图像修复方法300以及超声成像系统在获取胎儿颜面部的容积图像数据之后，对缺失、遮挡或质量较差的容积图像 数据进行修复，提高胎儿颜面部渲染成像的质量，以降低医生采集胎儿颜面部超声三维渲染图时对胎儿姿态等成像条件的依赖，提升医生的工作效率，实现更高效的临床工作流。
下面，将参考图4描述根据本申请另一实施例的胎儿颜面部容积图像修复方法。图4是本申请实施例的胎儿颜面部容积图像修复方法400的一个示意性流程图。
如图4所示，本申请一个实施例的胎儿颜面部容积图像修复方法400包括如下步骤：
在步骤S410，获取胎儿颜面部的容积图像数据；
在步骤S420，对所述胎儿颜面部的容积图像数据进行修复处理；
在步骤S430，对修复处理后的所述胎儿颜面部的容积图像数据进行渲染，得到所述胎儿颜面部的渲染图像；
在步骤S440，显示所述渲染图像。
根据本申请实施例的胎儿颜面部容积图像修复方法400中的步骤S430以及步骤S440与参考图2描述的胎儿颜面部容积图像修复方法200中的步骤S240以及步骤S250大体上类似，为了简洁，此处不再赘述相同的细节内容，以下主要对步骤S410和步骤S420进行详细描述。
在胎儿颜面部容积图像修复方法400中，步骤S410获取胎儿颜面部的容积图像数据的方法并不局限于某一种，可以选用图2描述的胎儿颜面部容积图像修复方法200中的步骤S210、步骤S220，具体步骤可以参照前文相关内容，在此不再赘述，当然还可以选用步骤S210和步骤S220之外的方法，在此不做限定。
步骤S420中对该胎儿颜面部的容积图像数据进行修复处理，以得到完整的胎儿颜面部的容积图像数据，其中，修复方法可以基于深度学习算法和/或图像修复算法，如图2描述的胎儿颜面部容积图像修复方法200中的步骤S230，具体步骤可以参照前文相关内容，在此不再赘述，当然还可以选用步骤S230之外的方法，在此不做限定。
现在重新参照图1，本申请实施例所提供的超声成像系统100可以用于实现上述胎儿颜面部容积图像修复方法400。如上所述，超声成像系统100可以包括超声探头110、发射/接收电路112、处理器114以及显示器116，各 个部件的相关描述可以参照上文。
当用于实现超声成像方法400时，发射/接收电路112用于激励该超声探头110向胎儿颜面部发射超声波，并接收该超声波的回波，以获得超声回波信号；处理器114，用于：对该胎儿颜面部的容积图像数据进行修复处理；对修复处理后的该胎儿颜面部的容积图像数据进行渲染，得到该胎儿颜面部的渲染图像；显示器116用于显示该渲染图像。
以上仅描述了超声成像系统100各部件的主要功能，更多细节参见对胎儿颜面部容积图像修复方法400进行的相关描述。
本申请实施例的胎儿颜面部容积图像修复方法400以及超声成像系统在获取胎儿颜面部的容积图像数据之后，对缺失、遮挡或质量较差的容积图像数据进行修复，提高胎儿颜面部渲染成像的质量，以降低医生采集胎儿颜面部超声三维渲染图时对胎儿姿态等成像条件的依赖，提升医生的工作效率，实现更高效的临床工作流。
下面，将参考图5描述根据本申请另一实施例的胎儿颜面部容积图像修复方法。图5是本申请实施例的胎儿颜面部容积图像修复方法500的一个示意性流程图。
如图5所示，本申请一个实施例的胎儿颜面部容积图像修复方法500包括如下步骤：
在步骤S510，获取胎儿颜面部的容积图像数据；
在步骤S520，对所述胎儿颜面部的容积图像数据进行渲染，得到所述胎儿颜面部的渲染图像；
在步骤S530，对所述胎儿颜面部的渲染图像进行修复处理；
在步骤S540，显示修复处理后的所述渲染图像。
根据本申请实施例的胎儿颜面部容积图像修复方法500中的步骤S540与参考图2描述的胎儿颜面部容积图像修复方法200中的步骤S250大体上类似，为了简洁，此处不再赘述相同的细节内容。
在胎儿颜面部容积图像修复方法500中，步骤S510获取胎儿颜面部的容积图像数据的方法并不局限于某一种，可以选用图2描述的胎儿颜面部容积图像修复方法200中的步骤S210、步骤S220，具体步骤可以参照前文相关内容，在此不再赘述，当然还可以选用步骤S210和步骤S220之外的方法，在 此不做限定。
其中，步骤S520和步骤S530与步骤S230和步骤S240的区别在于步骤S230和步骤S240先对该胎儿颜面部的容积图像数据进行修复处理，然后将修复后的该胎儿颜面部的容积图像数据进行渲染，进而得到该胎儿颜面部的渲染图像，而在步骤S520和步骤S530中则是先对该胎儿颜面部的容积图像数据进行渲染，得到该胎儿颜面部的初始渲染图像，然后再对初始渲染图像进行修复处理，因此区别在于渲染和修复处理的先后顺序不同。
其中，步骤S520中的渲染方法可以参照步骤S240，其中，步骤S520中的修复处理可以参照步骤S240中的修复方法，只是修复的数据类型不同，但方法类似，在此不再赘述。
步骤S530中对该胎儿颜面部的容积图像数据进行修复处理，以得到完整的胎儿颜面部的容积图像数据，其中，修复方法可以基于深度学习算法和/或图像修复算法，如图2描述的胎儿颜面部容积图像修复方法200中的步骤S230，具体步骤可以参照前文相关内容，在此不再赘述，当然还可以选用步骤S230之外的方法，在此不做限定。
现在重新参照图1，本申请实施例所提供的超声成像系统100可以用于实现上述胎儿颜面部容积图像修复方法500。如上所述，超声成像系统100可以包括超声探头110、发射/接收电路112、处理器114以及显示器116，各个部件的相关描述可以参照上文。
当用于实现超声成像方法500时，发射/接收电路112用于激励该超声探头110向胎儿颜面部发射超声波，并接收该超声波的回波，以获得超声回波信号；处理器114用于：对该胎儿颜面部的容积图像数据进行渲染，得到该胎儿颜面部的初始渲染图像；对该胎儿颜面部的初始渲染图像进行修复处理；显示器116用于显示修复处理后的渲染图像。
以上仅描述了超声成像系统100各部件的主要功能，更多细节参见对胎儿颜面部容积图像修复方法500进行的相关描述。
本申请实施例的胎儿颜面部容积图像修复方法500以及超声成像系统在获取胎儿颜面部的容积图像数据之后，对缺失、遮挡或质量较差的容积图像数据进行修复，提高胎儿颜面部渲染成像的质量，以降低医生采集胎儿颜面部超声三维渲染图时对胎儿姿态等成像条件的依赖，提升医生的工作效率，实现更高效的临床工作流。
尽管这里已经参考附图描述了示例实施例，应理解上述示例实施例仅仅是示例性的，并且不意图将本申请的范围限制于此。本领域普通技术人员可以在其中进行各种改变和修改，而不偏离本申请的范围和精神。所有这些改变和修改意在被包括在所附权利要求所要求的本申请的范围之内。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
在本申请所提供的几个实施例中，应该理解到，所揭露的设备和方法，可以通过其它的方式实现。例如，以上所描述的设备实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个设备，或一些特征可以忽略，或不执行。
在此处所提供的说明书中，说明了大量具体细节。然而，能够理解，本申请的实施例可以在没有这些具体细节的情况下实践。在一些实例中，并未详细示出公知的方法、结构和技术，以便不模糊对本说明书的理解。
类似地，应当理解，为了精简本申请并帮助理解各个发明方面中的一个或多个，在对本申请的示例性实施例的描述中，本申请的各个特征有时被一起分组到单个实施例、图、或者对其的描述中。然而，并不应将该本申请的方法解释成反映如下意图：即所要求保护的本申请要求比在每个权利要求中所明确记载的特征更多的特征。更确切地说，如相应的权利要求书所反映的那样，其发明点在于可以用少于某个公开的单个实施例的所有特征的特征来解决相应的技术问题。因此，遵循具体实施方式的权利要求书由此明确地并入该具体实施方式，其中每个权利要求本身都作为本申请的单独实施例。
本领域的技术人员可以理解，除了特征之间相互排斥之外，可以采用任何组合对本说明书(包括伴随的权利要求、摘要和附图)中公开的所有特征以及如此公开的任何方法或者设备的所有过程或单元进行组合。除非另外明确陈述，本说明书(包括伴随的权利要求、摘要和附图)中公开的每个特征可以由提供相同、等同或相似目的的替代特征来代替。
此外，本领域的技术人员能够理解，尽管在此所述的一些实施例包括其它实施例中所包括的某些特征而不是其它特征，但是不同实施例的特征的组合意味着处于本申请的范围之内并且形成不同的实施例。例如，在权利要求书中，所要求保护的实施例的任意之一都可以以任意的组合方式来使用。
本申请的各个部件实施例可以以硬件实现，或者以在一个或者多个处理器上运行的软件模块实现，或者以它们的组合实现。本领域的技术人员应当理解，可以在实践中使用微处理器或者数字信号处理器(DSP)来实现根据本申请实施例的一些模块的一些或者全部功能。本申请还可以实现为用于执行这里所描述的方法的一部分或者全部的装置程序(例如，计算机程序和计算机程序产品)。这样的实现本申请的程序可以存储在计算机可读介质上，或者可以具有一个或者多个信号的形式。这样的信号可以从因特网网站上下载得到，或者在载体信号上提供，或者以任何其他形式提供。
应该注意的是上述实施例对本申请进行说明而不是对本申请进行限制，并且本领域技术人员在不脱离所附权利要求的范围的情况下可设计出替换实施例。在权利要求中，不应将位于括号之间的任何参考符号构造成对权利要求的限制。本申请可以借助于包括有若干不同元件的硬件以及借助于适当编程的计算机来实现。在列举了若干装置的单元权利要求中，这些装置中的若干个可以是通过同一个硬件项来具体体现。单词第一、第二、以及第三等的使用不表示任何顺序。可将这些单词解释为名称。
以上所述，仅为本申请的具体实施方式或对具体实施方式的说明，本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。本申请的保护范围应以权利要求的保护范围为准。

Claims (21)

  1. 一种胎儿颜面部容积图像修复方法，其特征在于，包括：
    通过超声探头向胎儿颜面部发射超声波，并接收所述超声波的回波，以获得超声回波信号；
    对所述超声回波信号进行处理，得到所述胎儿颜面部的容积图像数据；
    基于深度学习算法和/或图像修复算法对所述胎儿颜面部的容积图像数据进行修复处理；
    对修复处理后的所述胎儿颜面部的容积图像数据进行渲染，得到所述胎儿颜面部的渲染图像；
    显示所述渲染图像。
  2. 根据权利要求1所述的胎儿颜面部容积图像修复方法，其特征在于，所述修复处理包括对所述胎儿颜面部缺失的部分数据进行填补，和/或去除对胎儿颜面部形成遮挡的数据，以露出胎儿颜面部被遮挡的区域。
  3. 根据权利要求2所述的胎儿颜面部容积图像修复方法，其特征在于，基于深度学习算法对所述胎儿颜面部的容积图像数据进行修复处理包括：
    建立数据库，所述数据库包括胎儿颜面部能完整显示的数据以及胎儿颜面部缺失的数据和/或胎儿颜面部被遮挡的数据；
    基于所述数据库中建立具有修复处理功能的生成模型；
    输入胎儿颜面部的容积图像数据；
    根据所述生成模型对输入胎儿颜面部的容积图像数据进行修复处理并输出修复处理后的所述胎儿颜面部的容积图像数据。
  4. 根据权利要求3所述的胎儿颜面部容积图像修复方法，其特征在于，直接使用胎儿颜面部容积图像的三维体数据作为输入进行所述修复处理；或逐帧地对胎儿颜面部容积图像的三维体数据中的多个二维切面进行所述修复处理。
  5. 根据权利要求2所述的胎儿颜面部容积图像修复方法，其特征在于，基于图像修复算法对所述胎儿颜面部的容积图像数据进行修复处理包括：
    将获取的胎儿颜面部容积图像分为多个图像块；
    计算胎儿颜面部容积图像中数据缺失区域的图像特征；
    选取与所述图像特征匹配的图像块；
    将选取的所述图像块填充于数据缺失区域，以实现胎儿颜面部容积图像 的修复。
  6. 根据权利要求1所述的胎儿颜面部容积图像修复方法，其特征在于，获取胎儿颜面部容积图像数据之后，指定所述胎儿颜面部容积图像数据中数据缺失或被遮挡的区域，对数据缺失或被遮挡的区域进行所述修复处理。
  7. 根据权利要求1所述的胎儿颜面部容积图像修复方法，其特征在于，获取的胎儿颜面部容积图像数据包括同一胎儿在不同角度的多卷体数据，所述多卷体数据中包括在第一角度下所述胎儿颜面部容积图像数据具有数据缺失或被遮挡的第一区域，以及在第二角度下所述胎儿颜面部容积图像数据中所述第一区域能完整显示；
    所述基于深度学习算法和/或图像修复算法对所述胎儿颜面部的容积图像数据进行修复处理包括：
    将所述第一角度和第二角度下的所述胎儿颜面部容积图像数据相互融合，以将所述第一角度下数据缺失或被遮挡的第一区域修复。
  8. 根据权利要求1所述胎儿颜面部容积图像修复方法，其特征在于，所述方法还包括：
    对所述胎儿颜面部的容积图像数据进行渲染，以得到胎儿颜面部的初始渲染图像；
    修复处理后渲染得到的所述渲染图像与所述初始渲染图像的显示能通过切换指令自由切换。
  9. 根据权利要求8所述胎儿颜面部容积图像修复方法，其特征在于，当显示图像切换至修复处理后渲染得到的所述渲染图像时，输出并显示该图像经修复得到的提示信息。
  10. 一种胎儿颜面部容积图像修复方法，其特征在于，包括：
    通过超声探头向胎儿颜面部发射超声波，并接收所述超声波的回波，以获得超声回波信号；
    对所述超声回波信号进行处理，得到所述胎儿颜面部的容积图像数据；
    对所述胎儿颜面部的容积图像数据进行渲染，得到所述胎儿颜面部的初始渲染图像；
    基于深度学习算法和/或图像修复算法对所述胎儿颜面部的初始渲染图像进行修复处理；
    显示修复处理后的所述渲染图像。
  11. 根据权利要求10所述的胎儿颜面部容积图像修复方法，其特征在于，所述修复处理包括对所述胎儿颜面部缺失的部分数据进行填补，和/或去除对胎儿颜面部形成遮挡的数据，以露出胎儿颜面部被遮挡的区域。
  12. 根据权利要求11所述的胎儿颜面部容积图像修复方法，其特征在于，基于深度学习算法对所述胎儿颜面部的渲染图像进行修复处理包括：
    建立数据库，所述数据库包括胎儿颜面部能完整显示的数据以及胎儿颜面部缺失的数据和/或胎儿颜面部被遮挡的数据；
    基于所述数据库中的数据建立具有修复处理功能的生成模型；
    输入胎儿颜面部的渲染图像；
    根据所述生成模型对输入的胎儿颜面部的渲染图像进行修复处理并输出修复处理后的所述胎儿颜面部的渲染图像。
  13. 根据权利要求12所述的胎儿颜面部容积图像修复方法，其特征在于，基于图像修复算法对所述胎儿颜面部的渲染图像进行修复处理包括：
    将胎儿颜面部的渲染图像分为多个图像块；
    计算胎儿颜面部的渲染图像中数据缺失区域的图像特征；
    选取与所述图像特征匹配的图像块；
    将选取的所述图像块填充于数据缺失区域，以实现胎儿颜面部的渲染图像的修复。
  14. 根据权利要求10所述的胎儿颜面部容积图像修复方法，其特征在于，获取的胎儿颜面部容积图像数据包括同一胎儿在不同角度的多卷体数据，所述多卷体数据中包括在第一角度下所述胎儿颜面部容积图像数据具有数据缺失或被遮挡的第一区域，以及在第二角度下所述胎儿颜面部容积图像数据中所述第一区域能完整显示；
    所述基于深度学习算法和/或图像修复算法对所述胎儿颜面部的初始渲染图像进行修复处理包括：
    将所述第一角度和第二角度下的所述胎儿颜面部的渲染图像相互融合，以将所述第一角度下数据缺失或被遮挡的第一区域修复。
  15. 根据权利要求10所述胎儿颜面部容积图像修复方法，其特征在于，所述方法还包括：
    修复处理后的所述渲染图像与所述初始渲染图像的显示能通过切换指令自由切换。
  16. 根据权利要求15所述胎儿颜面部容积图像修复方法，其特征在于，当显示图像切换至修复处理后的所述渲染图像时，输出并显示该图像经修复得到的提示信息。
  17. 一种胎儿颜面部容积图像修复方法，其特征在于，包括：
    获取胎儿颜面部的容积图像数据；
    对所述胎儿颜面部的容积图像数据进行修复处理；
    对修复处理后的所述胎儿颜面部的容积图像数据进行渲染，得到所述胎儿颜面部的渲染图像；
    显示所述渲染图像。
  18. 根据权利要求17所述的胎儿颜面部容积图像修复方法，其特征在于，所述修复处理包括对所述胎儿颜面部缺失的部分数据进行填补，和/或去除对胎儿颜面部形成遮挡的数据，以露出胎儿颜面部被遮挡的区域。
  19. 一种胎儿颜面部容积图像修复方法，其特征在于，包括：
    获取胎儿颜面部的容积图像数据；
    对所述胎儿颜面部的容积图像数据进行渲染，得到所述胎儿颜面部的初始渲染图像；
    对所述胎儿颜面部的初始渲染图像进行修复处理；
    显示修复处理后的所述渲染图像。
  20. 根据权利要求19所述的胎儿颜面部容积图像修复方法，其特征在于，所述修复处理包括对所述胎儿颜面部缺失的部分数据进行填补，和/或去除对胎儿颜面部形成遮挡的数据，以露出胎儿颜面部被遮挡的区域。
  21. 一种超声成像系统，其特征在于，包括：
    超声探头；
    发射电路，用于激励所述超声探头发射超声波；
    接收电路，用于通过所述超声探头接收所述超声波的回波，以获得超声回波信号；
    显示器，用于输出可视化信息；
    处理器，用于执行如权利要求1至20任一项所述方法的步骤。
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