CN111311612A - Lung segmentation method, device, medium, and electronic apparatus - Google Patents
Lung segmentation method, device, medium, and electronic apparatus Download PDFInfo
- Publication number
- CN111311612A CN111311612A CN202010098021.7A CN202010098021A CN111311612A CN 111311612 A CN111311612 A CN 111311612A CN 202010098021 A CN202010098021 A CN 202010098021A CN 111311612 A CN111311612 A CN 111311612A
- Authority
- CN
- China
- Prior art keywords
- image
- region
- lung
- segmentation
- rib
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20036—Morphological image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30061—Lung
Abstract
The invention discloses a lung segmentation method, a segmentation device, a computer readable storage medium and electronic equipment, wherein a fine segmentation image of a lung region is obtained by obtaining a rib region image in a CT image, simultaneously obtaining a rough segmentation image of the lung region in the CT image, taking the rib region image as the boundary of the lung region, taking the rough segmentation image as a seed region, and expanding the rough segmentation image to the boundary of the lung region from the periphery by a preset step length; the structure of the outer ribs of the lung of a pneumonia patient cannot be affected by diseases to change, and the boundary of the lung region is accurately obtained by the roughly divided image and the rib region by respectively obtaining the roughly divided image of the lung region and the rib region wrapping the lung region, so that the precision of lung division is improved.
Description
Technical Field
The present application relates to the field of image processing, and in particular, to a lung segmentation method based on CT images, a segmentation apparatus, a computer-readable storage medium, and an electronic device.
Background
Computed Tomography (CT) is a three-dimensional radiographic medical image reconstructed by using digital geometry processing. The technology mainly irradiates a human body through the rotation of X-rays with a single axial surface, and because different tissues have different absorption capacities (or called refractive indexes) to the X-rays, a fault surface image can be reconstructed by using a three-dimensional technology of a computer, fault images of corresponding tissues can be obtained through window width and window level processing, and the fault images are stacked layer by layer to form a three-dimensional image.
Whether a person to be detected is a pneumonia patient can be known through the CT image, particularly, the detection of the pneumonia patient caused by the novel coronavirus can be solved, and the CT image detection is one of the most important and accurate methods. However, since the pneumonia patient often has a large movement noise of the image due to cough and insufficient inspiration, and the density in the lung is increased, the segmentation of the lung region is difficult, and meanwhile, the lung shape and structure are affected by the large region of the pneumonia focus, so that the segmentation difficulty of the lung is further increased. In order to improve the detection accuracy of patients with pneumonia, it is necessary to improve the accuracy of lung segmentation, and therefore, a high-accuracy lung segmentation method is demanded.
Disclosure of Invention
In order to solve the above technical problem, the present application provides a lung segmentation method, a segmentation apparatus, a computer-readable storage medium, and an electronic device, wherein a rib region image in a CT image is acquired, a rough segmentation image of a lung region in the CT image is acquired at the same time, the rib region image is used as a boundary of the lung region, the rough segmentation image is used as a seed region, and the rough segmentation image is expanded to the boundary of the lung region from the periphery by a preset step length to obtain a fine segmentation image of the lung region; the structure of the outer ribs of the lung of a pneumonia patient cannot be affected by diseases to change, and the boundary of the lung region is accurately obtained by the roughly divided image and the rib region by respectively obtaining the roughly divided image of the lung region and the rib region wrapping the lung region, so that the precision of lung division is improved.
According to an aspect of the present application, there is provided a lung segmentation method, comprising: acquiring a rib region image in the CT image; acquiring a rough segmentation image of a lung region in a CT image; and expanding the image of the rib region to the boundary of the lung region in a preset step by taking the image of the rib region as the boundary of the lung region and the rough segmentation image as a seed region to obtain a fine segmentation image of the lung region.
In one embodiment, the acquiring the coarsely segmented image of the lung region in the CT image includes: and inputting the CT image into a neural network model to obtain the rough segmentation image of the lung region.
In one embodiment, the acquiring the coarsely segmented image of the lung region in the CT image includes: and selecting a connected region with the CT value within the CT value range of the lung region as the rough segmentation image according to the CT value range of the lung region.
In one embodiment, after the acquiring the roughly segmented image of the lung region in the CT image, the method further includes: carrying out corrosion operation on the roughly-segmented image to obtain a corroded roughly-segmented image; the obtaining of the fine segmented image of the lung region by using the rib region image as the boundary of the lung region and the coarse segmented image as a seed region and expanding the coarse segmented image to the boundary of the lung region circumferentially by a preset step length includes: and taking the rib region image as the boundary of the lung region, taking the corroded rough segmentation image as a seed region for expansion, and expanding the rough segmentation image to the boundary of the lung region from the periphery by a preset step length to obtain a fine segmentation image of the lung region.
In an embodiment, the expanding the rib region image as the boundary of the lung region and the rough segmentation image as a seed region to the boundary of the lung region circumferentially by a preset step length to obtain the fine segmentation image of the lung region includes: and taking the rib region image as a boundary and the roughly segmented image as an interested region, and segmenting the CT image by a preset step length through an active contour model to obtain a finely segmented image of the lung region.
In an embodiment, the active contour model comprises a LevelSet model or a Snake model.
In an embodiment, after the expanding the rib region image as the boundary of the lung region and the rough segmented image as the seed region to the boundary of the lung region circumferentially by a preset step length to obtain the fine segmented image of the lung region, the method further includes: and smoothing the boundary of the fine segmentation image.
In one embodiment, the acquiring the rib region image in the CT image includes: acquiring a bone region image in the CT image based on the CT value of the bone; and segmenting the rib region image in the bone region image based on the characteristics of the ribs.
In one embodiment, the acquiring the image of the bone region in the CT image based on the CT value of the bone includes: setting a first CT value threshold value based on the CT value of the bone, and acquiring a connected region of which the CT value is greater than or equal to the first CT value threshold value in the CT image as the bone region image.
In an embodiment, after the acquiring a connected region of the CT image whose CT value is greater than or equal to the first CT value threshold, the method further includes: and removing the area of the connected area which is smaller than a preset area threshold value.
In an embodiment, after the acquiring a connected region of the CT image whose CT value is greater than or equal to the first CT value threshold, the method further includes: and removing the connected regions in the roughly segmented image.
In an embodiment, the segmenting the rib region image of the bone region images based on the rib characteristics comprises: and comparing the standard rib image with the bone region image, and selecting the bone region with the similarity greater than the preset similarity with the standard rib image in the bone region image as the rib region image.
In one embodiment, before the acquiring the roughly segmented image of the lung region in the CT image, the method further includes: preprocessing the CT image, wherein the preprocessing comprises any one or combination of more of the following operations: removing background, removing white noise, cutting image, and transforming window width and level.
According to another aspect of the present application, there is provided a lung segmentation apparatus comprising: the acquisition module is used for acquiring a rib region image in the CT image; the rough segmentation module is used for acquiring a rough segmentation image of the lung region in the CT image; and the fine segmentation module is used for expanding the rib region image as the boundary of the lung region and the rough segmentation image as a seed region to the boundary of the lung region in a preset step length manner to obtain a fine segmentation image of the lung region.
According to another aspect of the application, a computer-readable storage medium is provided, the storage medium having stored thereon a computer program for performing any of the above described lung segmentation methods.
According to another aspect of the present application, there is provided an electronic apparatus including: a processor; a memory for storing the processor-executable instructions; the processor is configured to perform any one of the above lung segmentation methods.
According to the lung segmentation method, the segmentation device, the computer-readable storage medium and the electronic device, a rib region image in a CT image is obtained, a rough segmentation image of a lung region in the CT image is obtained at the same time, the rib region image is used as a boundary of the lung region, the rough segmentation image is used as a seed region, and the rough segmentation image is expanded to the boundary of the lung region from the periphery by a preset step length to obtain a fine segmentation image of the lung region; the structure of the outer ribs of the lung of a pneumonia patient cannot be affected by diseases to change, and the boundary of the lung region is accurately obtained by the roughly divided image and the rib region by respectively obtaining the roughly divided image of the lung region and the rib region wrapping the lung region, so that the precision of lung division is improved.
Drawings
The above and other objects, features and advantages of the present application will become more apparent by describing in more detail embodiments of the present application with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings, like reference numbers generally represent like parts or steps.
Fig. 1 is a flowchart illustrating a lung segmentation method according to an exemplary embodiment of the present application.
Fig. 2 is a flowchart illustrating a lung segmentation method according to another exemplary embodiment of the present application.
Fig. 3 is a flowchart illustrating a method for acquiring an image of a rib region according to an exemplary embodiment of the present application.
Fig. 4 is a flowchart illustrating a lung segmentation method according to another exemplary embodiment of the present application.
Fig. 5 is a flowchart illustrating a lung segmentation method according to another exemplary embodiment of the present application.
Fig. 6 is a schematic structural diagram of a lung segmentation apparatus according to an exemplary embodiment of the present application.
Fig. 7 is a block diagram of an electronic device provided in an exemplary embodiment of the present application.
Detailed Description
Hereinafter, example embodiments according to the present application will be described in detail 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.
Summary of the application
As described above, CT image detection is one of the most important and accurate methods for determining whether a subject is a pneumonia patient, and particularly for a pneumonia patient caused by a novel coronavirus. Generally, after a CT image is obtained, a lung region in the CT image needs to be segmented to obtain a lung region image, and then a doctor judges whether a person to be detected has pneumonia according to a representation of the lung region image, the existing lung region segmentation work is generally manually realized by professional medical staff, which obviously has low efficiency, and particularly, the detection of pneumonia caused by a novel coronavirus with strong infectivity is solved, because the detection has high infectivity, a large number of people have infection risks, so that CT image detection needs to be performed on a large number of people, and relatively limited or even lacking medical staff obviously cannot meet the requirement of the large number of CT image detection.
With the rapid development of artificial intelligence, artificial intelligence has begun to be applied in various industries, including the medical field, and it is possible to greatly reduce the workload of medical staff by using artificial intelligence instead of human labor to perform a large amount of and highly repetitive work, so that the medical staff can be more attentive to work more professionally or have to manually handle (for example, diagnosis and treatment of diseases). However, for the segmentation of the lung region in the CT image, the pneumonia patient often has a large motion noise of the image due to cough and insufficient inspiration, and the density in the lung is increased, so that a large difficulty exists in the segmentation of the lung region, and meanwhile, the large region of the pneumonia focus affects the shape and structure of the lung, so that the difficulty in the segmentation of the lung is further increased, which affects the precision of the artificial intelligence in segmenting the lung region, generates a certain error, and the transmission and accumulation of the error may ultimately affect the judgment of the doctor, resulting in an immeasurable effect.
In order to solve the above problems, the present application provides a lung segmentation method, a segmentation apparatus, a computer-readable storage medium, and an electronic device, in which a rib region image in a CT image is obtained, a rough segmentation image of a lung region in the CT image is obtained at the same time, the rib region image is used as a boundary of the lung region, the rough segmentation image is used as a seed region, and the rough segmentation image is expanded to the boundary of the lung region from the periphery by a preset step length to obtain a fine segmentation image of the lung region; the structure of the outer ribs of the lung of a pneumonia patient cannot be affected by diseases to change, and the boundary of the lung region is accurately obtained by the roughly divided image and the rib region by respectively obtaining the roughly divided image of the lung region and the rib region wrapping the lung region, so that the precision of lung division is improved.
Exemplary method
Fig. 1 is a flowchart illustrating a lung segmentation method according to an exemplary embodiment of the present application. As shown in fig. 1, the method comprises the steps of:
step 110: the rib region image in the CT image is acquired.
Because the ribs tightly wrap the lung region, the imaging of the CT image can be influenced by the shape of the lung and the characteristics of partial region of the pneumonia patient, and the ribs of the pneumonia patient cannot be changed due to the illness, the outer boundary of the lung region can be obtained by acquiring the rib region in the CT image, and the accuracy of segmenting the lung region is improved.
Step 120: a coarsely segmented image of the lung region in the CT image is acquired.
In an embodiment, the specific implementation manner of step 120 may be: and inputting the CT image into a neural network model to obtain a rough segmentation image of the lung region. Through the trained neural network model, the lung region in the CT image can be identified by the neural network model, wherein the neural network model may be a neural network model such as a uet network, and the training mode of the neural network model may be: the CT image of the lung region which has been identified and marked by medical professionals is selected as a training sample of the neural network model to train the neural network model. Because only the rough segmentation image of the lung region in the CT image is obtained in the step, but not the accurate segmentation image of the lung region, a proper number of training samples can be selected to train the neural network model, so that the efficiency of the whole lung segmentation is improved.
In another embodiment, the specific implementation manner of step 120 may also be: and selecting a region with the CT value within the CT value range of the lung region as a coarse segmentation image according to the CT value of the lung region. The CT number is a measure of the density of a local tissue or organ in the human body, and is commonly called Hounsfield Unit (HU), wherein the CT number of air is-1000 and the CT number of dense bone is + 1000. In fact, the CT value is a corresponding value of each tissue in the CT image corresponding to the X-ray attenuation coefficient, and the CT value is not an absolutely invariant value, and is related to not only internal factors of the human body such as respiration and blood flow, but also external factors such as X-ray tube voltage, CT apparatus, indoor temperature, and the like. The CT values of other tissues except bones in human tissues are below 300 and above-80, wherein the CT value of calcifications is 80-300, and the CT value of fat is-20 to-80. Because the lung area is basically air, the CT value of the lung area is lower than that of other tissues, the CT value range can be set, and the connected area with the CT value in the CT value range is selected as a rough segmentation image of the lung area.
Step 130: and taking the rib region image as the boundary of the lung region, taking the coarse segmentation image as a seed region, and expanding the coarse segmentation image to the boundary of the lung region from the periphery by a preset step length to obtain a fine segmentation image of the lung region.
In an embodiment, the specific implementation manner of step 130 may be: and taking the rib region image as a boundary and the roughly segmented image as an interested region, and segmenting the CT image by a preset step length through the active contour model to obtain a fine segmented image of the lung region. Since the lung is tightly wrapped by the rib, that is, the rib region is the outer boundary of the lung region, the roughly segmented image can be used as a seed region or an interested region, and the rib region image is used as a boundary, the CT image is segmented by the active contour model and the preset step length, that is, the CT image is expanded from the interested region to the periphery by the preset step length until the CT image is expanded to the rib region, so as to obtain an image of the accurately segmented lung region, thereby providing accurate basic image data for subsequent lung lobe segmentation, pneumonia judgment and the like. The preset step length can be adjusted according to actual requirements, and can be properly reduced for obtaining higher precision. In further embodiments, the active contour model may comprise a LevelSet model or a Snake model. It should be understood that different active contour models may be selected according to requirements of an actual application scenario in the embodiment of the present application, as long as the selected active contour model can obtain an accurate image of a lung region by using the rib region image as a boundary and the roughly segmented image as a seed region, and the specific structure of the active contour model is not limited in the embodiment of the present application.
According to the lung segmentation method, a rib region image in a CT image is obtained, a rough segmentation image of a lung region in the CT image is obtained at the same time, the rib region image is used as the boundary of the lung region, the rough segmentation image is used as a seed region, and the rough segmentation image is expanded to the boundary of the lung region from the periphery by a preset step length to obtain a fine segmentation image of the lung region; the structure of the outer ribs of the lung of a pneumonia patient cannot be affected by diseases to change, and the boundary of the lung region is accurately obtained by the roughly divided image and the rib region by respectively obtaining the roughly divided image of the lung region and the rib region wrapping the lung region, so that the precision of lung division is improved.
Fig. 2 is a flowchart illustrating a lung segmentation method according to another exemplary embodiment of the present application. As shown in fig. 2, after step 120, the above embodiment may further include:
step 140: and carrying out corrosion operation on the roughly-segmented image to obtain a corroded roughly-segmented image.
The etching operation is a morphological operation, and the specific operation process is as follows: pixels are removed along the boundary of the object in the image and the object is reduced in size, i.e., the boundary of the object is reduced to remove noise from the object in the image. Since the roughly segmented image of the lung region is usually not an accurate image of the lung region, for example, the roughly segmented image may include images of other regions other than the lung region, that is, noise regions in the roughly segmented image, and the interfering noise regions in the roughly segmented image may be removed by a erosion operation, so as to ensure that the eroded roughly segmented image is a part of the image of the lung region and does not include images of regions other than the lung region.
Meanwhile, step 130 adjusts to: and taking the rib region image as the boundary of the lung region, taking the corroded rough segmentation image as a seed region, and expanding the rough segmentation image to the boundary of the lung region from the periphery by a preset step length to obtain a fine segmentation image of the lung region.
Because the seed region only needs to be a part of the lung region and does not need to be the whole lung region, the seed region can be ensured to only contain the lung region through corrosion operation, so that the seed region is prevented from being fused into more non-lung regions in the expanding process, and the seed region cannot remove the region existing in the seed region in the expanding process, so that the condition that the seed region only contains the lung region is the precondition for ensuring the precision of segmenting the lung region.
Fig. 3 is a flowchart illustrating a method for acquiring an image of a rib region according to an exemplary embodiment of the present application. As shown in fig. 3, the acquiring method may include the steps of:
step 111: and acquiring a bone region image in the CT image based on the CT value of the bone.
And obtaining the bone region image with the maximum CT value according to the CT value of each region in the CT image. In one embodiment, a first CT value threshold may be set based on CT values of bones, and connected regions in the CT image having CT values greater than or equal to the first CT value threshold are acquired as the bone region image. And acquiring a connected region of which the CT value is greater than or equal to the first CT value threshold value in the CT image by setting the first CT value threshold value, so as to obtain the bone region image.
In an embodiment, after acquiring a connected region in the CT image whose CT value is greater than or equal to the first CT value threshold, the method may further include: and removing the area of the connected area which is smaller than a preset area threshold value. When the first CT threshold is set too large, a part of the bone region may be missed, and when the first CT threshold is set too small, due to the large CT value of the calcifications, certain calcifications may exist in the lung or heart region to become interference noise in the image of the bone region, and therefore, the calcifications need to be removed. Generally, the area of the calcified points is small, so that the interference of the calcified points on the image of the bone region can be eliminated by removing the area of the communicating area which is smaller than the preset area threshold value, wherein the area threshold value can be preset according to the practical application.
In an embodiment, after acquiring a connected region in the CT image whose CT value is greater than or equal to the first CT value threshold, the method may further include: connected regions located within the coarsely segmented image are removed. By removing the communication region in the roughly segmented image, the influence of calcification points in the lung or heart region on the final segmentation result can be avoided, and the subsequent segmentation precision can be improved.
Step 112: based on the characteristics of the ribs, the rib region image in the bone region image is segmented.
In one embodiment, the implementation of step 112 may be: and comparing the standard rib image with the bone region image, and selecting the bone region with the similarity greater than the preset similarity with the standard rib image in the bone region image as the rib region image. The ribs are regularly arranged outside the lung and are in a certain arc shape and are in a bilateral symmetry structure relative to other bones, so that the difference between the ribs and other bones can be distinguished in a multi-plane reconstructed three-dimensional view, and particularly, the characteristics of the ribs can be clearly known in a sagittal position, namely, the arrangement and the shape of the ribs have the particularity, so that the bone regions with the similarity meeting certain requirements (greater than the preset similarity) in the bone region image can be selected through comparison of the standard rib image, and the rib region image in the bone region image is segmented. It should be understood that, in the embodiment of the present application, other manners for acquiring the rib region image may also be selected according to requirements of an actual application scene, for example, whether the radian of the rib is within a preset radian range may be determined, whether a gap between the ribs is within a preset distance range (because the arrangement of the ribs has a certain rule, and vertical distances at various places between adjacent ribs are within a certain range) may also be determined, and the rib region image may also be acquired directly through a neural network model, as long as the selected manner for acquiring the rib region image meets the accuracy requirement, and the specific manner for acquiring the rib region image is not limited in the embodiment of the present application.
Fig. 4 is a flowchart illustrating a lung segmentation method according to another exemplary embodiment of the present application. As shown in fig. 4, before step 120, the above embodiment may further include:
step 150: and preprocessing the CT image.
In an embodiment, the pre-processing may include any one or combination of the following operations: removing background, removing white noise, cutting image, and transforming window width and level. The specific implementation manner of removing the background may be: through setting the CT value range, the connected region in the CT value range is obtained, only the connected region with the largest area in the connected region is reserved, and other regions are set as background regions, so that the interference of other regions is eliminated. The specific implementation manner of removing the white noise may be: white noise caused in the process of taking the CT image is removed through a Gaussian filter. The specific implementation manner of cutting the image may be: and removing the background, and only reserving an effective area to reduce the complexity of subsequent image processing. The specific implementation manner of changing the window width and the window level may be: the region of interest is emphasized by setting values of the window width and the window level, so that interference of the region of no interest on subsequent processing is avoided, in the embodiment of the application, the window level may be selected to be-500, and the window width may be 1500, although it should be understood that the setting values of the window level and the window width may be adjusted according to actual situations.
It should be appreciated that step 150 may be disposed before step 110, and the preprocessing is used to exclude background and other interference factors from the CT image, which may effectively reduce the complexity of the subsequent steps and provide the efficiency of lung segmentation.
Fig. 5 is a flowchart illustrating a lung segmentation method according to another exemplary embodiment of the present application. As shown in fig. 5, after step 130, the above embodiment may further include:
step 160: and smoothing the boundary of the fine segmentation image.
Because only part of the boundary of the lung region can be determined according to the rib region image and the rib region image is expanded by the active contour model in the same preset step length, the boundary of the obtained fine segmentation image may not be smooth, and after the fine segmentation image is obtained, the boundary of the fine segmentation image is subjected to smoothing treatment, so that a more accurate lung region image can be obtained.
Exemplary devices
Fig. 6 is a schematic structural diagram of a lung segmentation apparatus according to an exemplary embodiment of the present application. As shown in fig. 6, the lung segmentation apparatus 60 includes the following modules:
an obtaining module 61, configured to obtain a rib region image in a CT image; a rough segmentation module 62, configured to obtain a rough segmented image of the lung region in the CT image; and a fine segmentation module 63, configured to use the rib region image as a boundary of the lung region, use the coarse segmentation image as a seed region, and expand the coarse segmentation image to the boundary of the lung region in a preset step length to obtain a fine segmentation image of the lung region.
According to the lung segmentation device provided by the application, the rib region image in the CT image is obtained through the obtaining module 61, meanwhile, the rough segmentation module 62 obtains the rough segmentation image of the lung region in the CT image, the fine segmentation module 63 takes the rib region image as the boundary of the lung region, takes the rough segmentation image as the seed region, and expands to the boundary of the lung region from the periphery by a preset step length to obtain the fine segmentation image of the lung region; the structure of the outer ribs of the lung of a pneumonia patient cannot be affected by diseases to change, and the boundary of the lung region is accurately obtained by the roughly divided image and the rib region by respectively obtaining the roughly divided image of the lung region and the rib region wrapping the lung region, so that the precision of lung division is improved.
In an embodiment, the rough segmentation module 62 may be further configured to: and inputting the CT image into a neural network model to obtain a rough segmentation image of the lung region.
In an embodiment, the rough segmentation module 62 may be further configured to: and selecting a region with the CT value within the CT value range of the lung region as a coarse segmentation image according to the CT value of the lung region.
In an embodiment, the fine segmentation module 63 may be further configured to: and taking the rib region image as a boundary and the roughly segmented image as an interested region, and segmenting the CT image by a preset step length through the active contour model to obtain a fine segmented image of the lung region. The active contour model may include a LevelSet model or a Snake model.
In one embodiment, as shown in fig. 6, the lung segmentation apparatus 60 may further include: and the erosion module 64 is configured to perform an erosion operation on the roughly-segmented image to obtain an eroded roughly-segmented image. And the fine segmentation module 63 is configured to: and taking the rib region image as the boundary of the lung region, taking the corroded rough segmentation image as a seed region, and expanding the rough segmentation image to the boundary of the lung region from the periphery by a preset step length to obtain a fine segmentation image of the lung region.
In one embodiment, as shown in fig. 6, the obtaining module 61 may include: a bone acquisition unit 611 for acquiring a bone region image in the CT image based on the CT value of the bone; a rib obtaining unit 612, configured to segment a rib region image in the bone region image based on the characteristics of the rib.
In an embodiment, the bone obtaining unit 611 may be further configured to: and setting a first CT value threshold value based on the CT value of the bone, and acquiring a connected region of which the CT value is greater than or equal to the first CT value threshold value in the CT image as a bone region image.
In an embodiment, the obtaining module 61 may be further configured to: and after a connected region of which the CT value is greater than or equal to the first CT value threshold value in the CT image is obtained, removing a region of which the area is smaller than a preset area threshold value in the connected region.
In an embodiment, the obtaining module 61 may be further configured to: and removing the connected region in the roughly segmented image after the connected region of which the CT value is greater than or equal to the first CT value threshold value in the CT image is obtained.
In an embodiment, the rib acquiring unit 612 may be further configured to: and comparing the standard rib image with the bone region image, and selecting the bone region with the similarity greater than the preset similarity with the standard rib image in the bone region image as the rib region image.
In one embodiment, as shown in fig. 6, the lung segmentation apparatus 60 may further include: and a preprocessing module 65 for preprocessing the CT image. In an embodiment, the pre-processing may include any one or combination of the following operations: removing background, removing white noise, cutting image, and transforming window width and level.
In one embodiment, as shown in fig. 6, the lung segmentation apparatus 60 may further include: and a smoothing module 66, configured to smooth the boundary of the fine segmented image.
Exemplary electronic device
Next, an electronic apparatus according to an embodiment of the present application is described with reference to fig. 7. The electronic device may be either or both of the first device and the second device, or a stand-alone device separate from them, which stand-alone device may communicate with the first device and the second device to receive the acquired input signals therefrom.
FIG. 7 illustrates a block diagram of an electronic device in accordance with an embodiment of the present application.
As shown in fig. 7, the electronic device 10 includes one or more processors 11 and memory 12.
The processor 11 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.
Memory 12 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer readable storage medium and executed by the processor 11 to implement the lung segmentation methods of the various embodiments of the present application described above and/or other desired functions. Various contents such as an input signal, a signal component, a noise component, etc. may also be stored in the computer-readable storage medium.
In one example, the electronic device 10 may further include: an input device 13 and an output device 14, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
For example, when the electronic device is a first device or a second device, the input device 13 may be a camera for capturing an input signal of an image. When the electronic device is a stand-alone device, the input means 13 may be a communication network connector for receiving the acquired input signals from the first device and the second device.
The input device 13 may also include, for example, a keyboard, a mouse, and the like.
The output device 14 may output various information including the determined distance information, direction information, and the like to the outside. The output devices 14 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.
Of course, for simplicity, only some of the components of the electronic device 10 relevant to the present application are shown in fig. 7, and components such as buses, input/output interfaces, and the like are omitted. In addition, the electronic device 10 may include any other suitable components depending on the particular application.
Exemplary computer program product and computer-readable storage Medium
In addition to the above-described methods and apparatus, embodiments of the present application may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps in the lung segmentation method according to various embodiments of the present application described in the "exemplary methods" section above of this specification.
The computer program product may be written with program code for performing the operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, cause the processor to perform the steps in the lung segmentation method according to various embodiments of the present application described in the "exemplary methods" section above in this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing describes the general principles of the present application in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present application are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present application. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the foregoing disclosure is not intended to be exhaustive or to limit the disclosure to the precise details disclosed.
The block diagrams of devices, apparatuses, systems referred to in this application are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
It should also be noted that in the devices, apparatuses, and methods of the present application, the components or steps may be decomposed and/or recombined. These decompositions and/or recombinations are to be considered as equivalents of the present application.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the application to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
Claims (16)
1. A method of lung segmentation, comprising:
acquiring a rib region image in a CT image;
acquiring a rough segmentation image of a lung region in the CT image; and
and expanding the rib region image as the boundary of the lung region and the rough segmentation image as a seed region to the boundary of the lung region peripherally by a preset step length to obtain a fine segmentation image of the lung region.
2. The segmentation method according to claim 1, wherein the obtaining of the coarsely segmented image of the lung region in the CT image comprises:
and inputting the CT image into a neural network model to obtain the rough segmentation image of the lung region.
3. The segmentation method according to claim 1, wherein the obtaining of the coarsely segmented image of the lung region in the CT image comprises:
and selecting a connected region with the CT value within the CT value range of the lung region as the rough segmentation image according to the CT value range of the lung region.
4. The segmentation method according to claim 2 or 3, further comprising, after the obtaining of the coarsely segmented image of the lung region in the CT image:
carrying out corrosion operation on the roughly-segmented image to obtain a corroded roughly-segmented image;
the obtaining of the fine segmented image of the lung region by using the rib region image as the boundary of the lung region and the coarse segmented image as a seed region and expanding the coarse segmented image to the boundary of the lung region circumferentially by a preset step length includes:
and taking the rib region image as the boundary of the lung region, taking the corroded rough segmentation image as a seed region for expansion, and expanding the rough segmentation image to the boundary of the lung region from the periphery by a preset step length to obtain a fine segmentation image of the lung region.
5. The segmentation method according to claim 1, wherein the expanding to the boundary of the lung region circumferentially by a preset step size with the rib region image as the boundary of the lung region and the coarse segmentation image as a seed region, and obtaining the fine segmentation image of the lung region comprises:
and taking the rib region image as a boundary and the roughly segmented image as an interested region, and segmenting the CT image by a preset step length through an active contour model to obtain a finely segmented image of the lung region.
6. The segmentation method according to claim 5, wherein the active contour model comprises a LevelSet model or a Snake model.
7. The segmentation method according to claim 1, further comprising, after the obtaining of the fine segmented image of the lung region by using the rib region image as the boundary of the lung region and using the coarse segmented image as a seed region and expanding the coarse segmented image to the boundary of the lung region by a preset step, the method further comprising:
and smoothing the boundary of the fine segmentation image.
8. The segmentation method according to claim 1, wherein the acquiring of the rib region image in the CT image includes:
acquiring a bone region image in the CT image based on the CT value of the bone; and
segmenting the rib region image out of the bone region images based on the characteristics of the ribs.
9. The segmentation method according to claim 8, wherein the acquiring of the image of the bone region in the CT image based on the CT value of the bone comprises:
setting a first CT value threshold value based on the CT value of the bone, and acquiring a connected region of which the CT value is greater than or equal to the first CT value threshold value in the CT image as the bone region image.
10. The segmentation method according to claim 9, further comprising, after the acquiring of the connected region in the CT image having the CT value greater than or equal to the first CT value threshold:
and removing the area of the connected area which is smaller than a preset area threshold value.
11. The segmentation method according to claim 9, further comprising, after the acquiring of the connected region in the CT image having the CT value greater than or equal to the first CT value threshold:
and removing the connected regions in the roughly segmented image.
12. The segmentation method according to claim 8, wherein the segmenting the rib region image out of the bone region images based on the rib characteristics comprises:
and comparing the standard rib image with the bone region image, and selecting the bone region with the similarity greater than the preset similarity with the standard rib image in the bone region image as the rib region image.
13. The segmentation method according to claim 1, further comprising, before the obtaining the coarsely segmented image of the lung region in the CT image:
preprocessing the CT image, wherein the preprocessing comprises any one or combination of more of the following operations: removing background, removing white noise, cutting image, and transforming window width and level.
14. A lung segmentation apparatus, comprising:
the acquisition module is used for acquiring a rib region image in the CT image;
the rough segmentation module is used for acquiring a rough segmentation image of the lung region in the CT image; and
and the fine segmentation module is used for expanding the rib region image as the boundary of the lung region and the rough segmentation image as a seed region to the boundary of the lung region in a preset step length manner to obtain a fine segmentation image of the lung region.
15. A computer-readable storage medium, in which a computer program is stored, the computer program being adapted to perform the lung segmentation method according to any one of the preceding claims 1 to 13.
16. An electronic device, the electronic device comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor for performing the method of lung segmentation of any one of the preceding claims 1-13.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010098021.7A CN111311612A (en) | 2020-02-17 | 2020-02-17 | Lung segmentation method, device, medium, and electronic apparatus |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010098021.7A CN111311612A (en) | 2020-02-17 | 2020-02-17 | Lung segmentation method, device, medium, and electronic apparatus |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111311612A true CN111311612A (en) | 2020-06-19 |
Family
ID=71160018
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010098021.7A Pending CN111311612A (en) | 2020-02-17 | 2020-02-17 | Lung segmentation method, device, medium, and electronic apparatus |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111311612A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111951215A (en) * | 2020-06-30 | 2020-11-17 | 数坤（北京）网络科技有限公司 | Image detection method and device and computer readable storage medium |
| CN113344897A (en) * | 2021-06-24 | 2021-09-03 | 推想医疗科技股份有限公司 | Lung image caliber measuring method and device and image processing method and device |
| CN113538414A (en) * | 2021-08-13 | 2021-10-22 | 推想医疗科技股份有限公司 | Lung image registration method and lung image registration device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1912927A (en) * | 2006-08-25 | 2007-02-14 | 西安理工大学 | Semi-automatic partition method of lung CT image focus |
| CN102460471A (en) * | 2009-06-19 | 2012-05-16 | 美国医软科技公司 | Systems for computer aided lung nodule detection in chest tomosynthesis imaging |
| CN104252708A (en) * | 2013-06-28 | 2014-12-31 | 深圳先进技术研究院 | X-ray chest radiographic image processing method and X-ray chest radiographic image processing system |
| CN108389210A (en) * | 2018-02-28 | 2018-08-10 | 深圳天琴医疗科技有限公司 | A kind of medical image cutting method and device |
| CN110766713A (en) * | 2019-10-30 | 2020-02-07 | 上海微创医疗器械（集团）有限公司 | Lung image segmentation method and device and lung lesion region identification equipment |
-
2020
- 2020-02-17 CN CN202010098021.7A patent/CN111311612A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1912927A (en) * | 2006-08-25 | 2007-02-14 | 西安理工大学 | Semi-automatic partition method of lung CT image focus |
| CN102460471A (en) * | 2009-06-19 | 2012-05-16 | 美国医软科技公司 | Systems for computer aided lung nodule detection in chest tomosynthesis imaging |
| CN104252708A (en) * | 2013-06-28 | 2014-12-31 | 深圳先进技术研究院 | X-ray chest radiographic image processing method and X-ray chest radiographic image processing system |
| CN108389210A (en) * | 2018-02-28 | 2018-08-10 | 深圳天琴医疗科技有限公司 | A kind of medical image cutting method and device |
| CN110766713A (en) * | 2019-10-30 | 2020-02-07 | 上海微创医疗器械（集团）有限公司 | Lung image segmentation method and device and lung lesion region identification equipment |
Non-Patent Citations (5)
| Title |
|---|
| JYOTI ISLAM ET AL.: "Towards Robust Lung Segmentation in Chest Radiographs with Deep Learning", 《ARXIV》 * |
| P. ANNANGI ET AL.: "A REGION BASED ACTIVE CONTOUR METHOD FOR X-RAY LUNG SEGMENTATION USING PRIOR SHAPE AND LOW LEVEL FEATURES", 《2010 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING:FROM NANO TO MACRO》 * |
| SHANHUI SUN ET AL.: "Automated 3-D Segmentation of Lungs With Lung Cancer in CT Data Using a Novel Robust Active Shape Model Approach", 《IEEE TRANSACTIONS ON MEDICAL IMAGING》 * |
| 周茂 等: "肺部 CT 图像分割方法研究", 《CT 理论与应用研究》 * |
| 孟琭: "基于活动轮廓模型的医学图像分割关键技术研究", 《中国博士学位论文全文数据库 信息科技辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111951215A (en) * | 2020-06-30 | 2020-11-17 | 数坤（北京）网络科技有限公司 | Image detection method and device and computer readable storage medium |
| CN113344897A (en) * | 2021-06-24 | 2021-09-03 | 推想医疗科技股份有限公司 | Lung image caliber measuring method and device and image processing method and device |
| CN113538414A (en) * | 2021-08-13 | 2021-10-22 | 推想医疗科技股份有限公司 | Lung image registration method and lung image registration device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111415743B (en) | Pneumonia classification method, pneumonia classification device, pneumonia classification medium and electronic equipment | |
| JP5307331B2 (en) | Method and system for automatically determining an area within a scanned object | |
| JP5643304B2 (en) | Computer-aided lung nodule detection system and method and chest image segmentation system and method in chest tomosynthesis imaging | |
| JP4728627B2 (en) | Method and apparatus for segmenting structures in CT angiography | |
| Xie et al. | Automated aorta segmentation in low-dose chest CT images | |
| US8559689B2 (en) | Medical image processing apparatus, method, and program | |
| CN111311612A (en) | Lung segmentation method, device, medium, and electronic apparatus | |
| CN111415356B (en) | Pneumonia symptom segmentation method, pneumonia symptom segmentation device, pneumonia symptom segmentation medium and electronic equipment | |
| CN111415404B (en) | Positioning method and device for intraoperative preset area, storage medium and electronic equipment | |
| CN111260669A (en) | Lung lobe segmentation method and device based on CT image | |
| JP2005199057A (en) | Method and apparatus for registration of lung image data | |
| US8189894B2 (en) | Method to detect the aortic arch in CT datasets for defining a heart window | |
| CN113327225B (en) | Method for providing airway information | |
| JP2007526033A (en) | Apparatus and method for registering an image of a structured object | |
| Rebouças Filho et al. | 3D segmentation and visualization of lung and its structures using CT images of the thorax | |
| WO2020235461A1 (en) | Abnormality detection method, abnormality detection program, abnormality detection device, server device, and information processing method | |
| CN111415341A (en) | Pneumonia stage evaluation method, pneumonia stage evaluation device, pneumonia stage evaluation medium and electronic equipment | |
| Thodberg | Hands-on experience with active appearance models | |
| EP3665643B1 (en) | X-ray image processing method and system and computer storage medium | |
| Cunha et al. | A method for segmentation of dental implants and crestal bone | |
| JP2005198887A (en) | Method, apparatus and program for detecting anatomical structure, structure removal picture generation apparatus, and abnormal shadow detector | |
| US20220092745A1 (en) | Information processing apparatus, information processing method, and non-transitory recording medium | |
| Varghese et al. | Segmentation and three dimensional visualization of mandible using active contour and visualization toolkit in craniofacial computed tomography images | |
| CN112288708B (en) | Method, device, medium, and electronic device for detecting lymph node in CT image | |
| CN113658172B (en) | Image processing method and device, computer readable storage medium and electronic device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: Room B401, floor 4, building 1, No. 12, Shangdi Information Road, Haidian District, Beijing 100085 Applicant after: Tuxiang Medical Technology Co., Ltd Address before: Room B401, floor 4, building 1, No. 12, Shangdi Information Road, Haidian District, Beijing 100085 Applicant before: Beijing Tuoxiang Technology Co.,Ltd. |
|
| CB02 | Change of applicant information | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200619 |
|
| RJ01 | Rejection of invention patent application after publication |