CN105741293A - Method for positioning organs in medical image - Google Patents
Method for positioning organs in medical image Download PDFInfo
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- CN105741293A CN105741293A CN201610068472.XA CN201610068472A CN105741293A CN 105741293 A CN105741293 A CN 105741293A CN 201610068472 A CN201610068472 A CN 201610068472A CN 105741293 A CN105741293 A CN 105741293A
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- 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]
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- 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
- G06T2207/10088—Magnetic resonance imaging [MRI]
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- 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
- G06T2207/30008—Bone
- G06T2207/30012—Spine; Backbone
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- 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
- G06T2207/30016—Brain
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- 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 method for positioning organs in a medical image. The method comprises following steps of step S1, inputting the medical image containing a plurality of slice images; step S2, preprocessing the input images; filtering non-body pixels; step S3, calculating the number of connected domains of each layer of slice image; through judging the numbers and the positions of the connected domains, removing the parts except first organs; confirming that the medical image contains the first organs; calculating the feature value of each layer of slice image, thus obtaining a plurality of feature values; forming a feature curve by the plurality of feature values and the slice number of the slice images; wherein the step of calculating the feature value comprises with respect to each layer of slice image, calculating the proportion of a pixel area of the pixel gray value or CT value relative to the whole body pixel area in a first range; and positioning the slice images in which the organs are located according to the proportions. According to the settings, the ranges in which the organs are located can be positioned rapidly, and the accuracy is high.
Description
Technical field
The present invention relates to the process of medical domain image, particularly relate to the localization method of organ-tissue in three-dimensional CT image.
Background technology
Organ-tissue identification and localization method in existing three-dimensional CT image, for instance, based on the method for machine learning, the method previous work is complicated, relates to a large amount of training image and collects and pretreatment, it is necessary to the grader that design is complicated, identifies and location Calculation complexity is higher.In a lot of image procossing are applied, such as segmentation, registration, automatically identify image locations, image slightly alignment etc., need image locations or organ-tissue are carried out preliminary identification and judgement, now, realize method with greater need for a kind of for the quick, easy of image itself.
Therefore, it is necessary to the localization method of organ-tissue in existing three-dimensional CT image is improved, improve the speed of location.
Summary of the invention
It is an object of the invention to provide and a kind of position the method for organ on medical image, be used for improving locating effect.
In order to realize aforementioned invention purpose, the present invention provides a kind of and positions the method for organ on medical image, comprises the following steps:
Step S1, input include the medical image of some sectioning images；
Step S2, to input image carry out pretreatment, filter out non-body pixel；
Step S3, every layer of sectioning image is calculated spongiosa region connected domain number, by judging connected domain number and position, remove the part outside the first organ, confirm that medical image includes the first organ；Every layer of sectioning image is calculated eigenvalue and obtains some eigenvalues, the number of plies of some eigenvalues and sectioning image forms characteristic curve, calculate eigenvalue and include every layer of sectioning image is calculated the ratio of grey scale pixel value or the CT value total elemental area in specific region, elemental area relative population portion between the first scope, orient the sectioning image at organ place according to ratio.
Preferably, described calculating eigenvalue also includes the ratio of width to height that every layer of sectioning image calculates spongiosa region, draws a ratio of width to height characteristic curve being axis of abscissas with the number of plies of sectioning image；Every layer of sectioning image is calculated spongiosa region area, draws the body area change characteristic curve being axis of abscissas with the number of plies of sectioning image.
Preferably, described first organ includes lower limb, trunk and head and neck, step S3 judging, connected domain number and position include judging that connected domain is three and is positioned at two connected domains of both sides, removes the part outside the first organ and include removing two connected domains being arranged in both sides and namely remove the arm segment of image.
Preferably, in step S3, sectioning image connected domain number is two, then judge that lower limb are at the sectioning image that connected domain number is two.
Preferably, described first ranges for 350HU～3000HU, step S3 includes finding the extreme point of characteristic curve, if the connected domain number of sectioning image corresponding to extreme point is 2, then knee joint position is in the sectioning image that connected domain number is 2 corresponding to extreme point.
Preferably, the total pixel in described specific region is total body pixel, described first ranges for-910HU～-200HU, if the ratio of CT value elemental area relative population portion elemental area between-910HU～-200HU is more than 0.2 on sectioning image, and total body elemental area is more than π * 100cm2, it is determined that chest is positioned at this sectioning image.
Preferably, step S3 includes the maximum that calculates specific curves, it is judged that lung top is positioned at curve along sectioning image corresponding to the extreme point of the right trailing edge of maximum.
Preferably, step S3 includes the ratio calculating the pixel gross area relative population portion elemental area between the-910HU～-200HU of each sectioning image body center region below, form accounting curve, it is judged that the base of lung is positioned at accounting curve along sectioning image corresponding to the extreme point of the left trailing edge of ratio maximum.
Preferably, step S3 includes being partitioned into two lungs in CT image, calculate CT value elemental area between-20HU～70HU between each sectioning image two lung, in the middle of heart between two lungs the CT value sectioning image corresponding to elemental area maximum between-20HU～70HU.
Preferably, step S3 includes number and area that every layer of sectioning image calculates the connected domain in CT value region between-910HU～-200HU, and abdominal part is positioned at connected domain number more than 10, and connected domain average area is less than π * 4cm2Sectioning image on, it is connected domain by removing minimum area and maximum area that average area calculates, and the area of all the other connected domains is averaged.
Preferably, calculating each sectioning image body center and calculate characteristic curve with left region, first ranges for-20HU～70HU, and liver top is positioned at the sectioning image corresponding to point that described characteristic curve variable gradient is maximum.
Preferably, calculating each sectioning image body center and calculate characteristic curve with left region, first ranges for-20HU～70HU, is arranged in characteristic curve at the sectioning image corresponding to the peak of thorax abdomen sectioning image in the middle of liver.
Preferably, the total pixel in described specific region is total body pixel, and described first ranges for 350HU～3000HU, and head is positioned at the ratio of width to height less than 0.8, and total body elemental area is less than π * 100cm2, the ratio of CT value elemental area relative population portion elemental area between the first scope is more than 0.2, and connected domain number is the sectioning image corresponding to 1；Neck is positioned at the ratio of width to height less than 0.8, and total body elemental area is less than π * 100cm2, the ratio of CT value elemental area relative population portion elemental area between the first scope is less than 0.15, and connected domain number is the sectioning image corresponding to 1.
Preferably, within the scope of the sectioning image containing head, the ratio of CT value elemental area relative population portion elemental area between the first scope has two extreme points；Distinguishing said two extreme point by the ratio of CT value elemental area relative population portion elemental area between-20HU～70HU again, head intermediate layer is positioned at the sectioning image that the big extreme point of ratio is corresponding, is positioned at the sectioning image that another extreme point is corresponding at the bottom of skull.
Preferably, described calculating eigenvalue also includes the ratio of width to height that every layer of sectioning image calculates spongiosa region, draws a ratio of width to height characteristic curve being axis of abscissas with the number of plies of sectioning image；Described first ranges for-200HU～-20HU, the total pixel in described specific region is total body pixel, every layer of sectioning image is calculated the accounting of HU value elemental area relative population portion elemental area between-200HU～-20HU, if described accounting is more than 0.45, and described the ratio of width to height is more than 1.5, then corresponding sectioning image is containing pelvis.
The present invention, by step S3: step S3, to every layer of sectioning image calculating connected domain number, by judging connected domain number and position, removes the part outside the first organ, confirms that medical image includes the first organ；Every layer of sectioning image is calculated eigenvalue and obtains some eigenvalues, the number of plies of some eigenvalues and sectioning image forms characteristic curve, calculate eigenvalue and include every layer of sectioning image is calculated grey scale pixel value or CT value elemental area between the first scope ratio relative to the total elemental area in specific region, the sectioning image at organ place is oriented according to ratio, quick and precisely orient the sectioning image scope at organ place, provide important initial position message for next step graphical analysis.
Accompanying drawing explanation
Fig. 1 illustrates that and positions the steps flow chart of the method for organ on medical image in the embodiment of the present invention.
Fig. 2 illustrates that the CT image of input in the embodiment of the present invention.
Fig. 3 illustrates that the CT image only comprising lower limb, trunk and head and neck in the embodiment of the present invention.
Fig. 4 illustrates that at the bottom of head intermediate layer that in the embodiment of the present invention, Coronal CT image is oriented along Z-direction, skull.
Fig. 5 illustrates that chest that in the embodiment of the present invention, Coronal CT image is oriented, abdominal part, hyperpelvic organ along Z-direction.
Characteristic curve in the embodiment of the present invention of Fig. 6 signal.
Detailed description of the invention
Below in conjunction with the drawings and specific embodiments, the present invention is described in further detail.According to the following describes and claims, advantages and features of the invention will be apparent from.It should be noted that, accompanying drawing all adopts the form simplified very much and all uses non-ratio accurately, only in order to convenience, the purpose aiding in illustrating the embodiment of the present invention lucidly.
Refer to shown in Fig. 1, the method for organ on medical image that positions in the embodiment of the present invention is the method for organ on the CT image of location, specifically includes following steps:
Step S1: input includes some medical images arranging sectioning image along Z-direction, and medical image can be CT image or nuclear magnetic resonance image, and CT image includes volume data (volumedata).Z-direction can be head-to-toe direction.
Step S2: CT image is carried out pretreatment, and pretreatment comprises the following steps:
The CT image of input is removed background process, is used for filtering out the non-body part pixel such as bed board, fixture (non-body pixel).
Image is carried out smothing filtering, is mainly used in removing noise.
Step S3: the organ on image is identified and positioning instant judges that organ that CT image comprises and location organ are organized in the position of image Z-direction, comprise the following steps:
Every layer of sectioning image (slice) is calculated spongiosa region connected domain number, by judging connected domain number and position, for Fig. 2, show that this sectioning image connected domain number is 3, remove the connected domain of the corresponding arm regions being positioned at both sides, such as it is shown on figure 3, thus obtaining some sectioning images, make CT image only comprise the first organ: lower limb, thorax abdomen and trunk and head and neck.
Positioning head cervical region
Eigenvalue calculation includes: every layer of slice image 1) calculates the ratio of width to height of spongiosa region, draw one with the number of plies (slicenumber) of section be axis of abscissas, aspect ratio value be the ratio of width to height characteristic curve of axis of ordinates；2) every layer of sectioning image being calculated spongiosa region area, draw with the number of plies of cutting into slices for axis of abscissas, spongiosa region area is the body area change characteristic curve of axis of ordinates；3) every layer of sectioning image calculating the accounting of CT value pixel gross area relative population portion elemental area between 350HU～3000HU, draws with the number of plies of cutting into slices for axis of abscissas, accounting is the accounting characteristic curve of axis of ordinates.
Judging rules: if the ratio of width to height is less than certain value, for instance 0.8, and total body elemental area is less than certain value, for instance π * 10*10cm2, and the elemental area accounting between 350HU～3000HU is more than certain value, for instance and 0.2, connected domain number is 1, then judge the sectioning image containing head.
If the ratio of width to height is less than 0.8, and total body elemental area (bulk area) is less than π * 10*10cm2, and the elemental area accounting between 350HU～3000HU is less than 0.15, connected domain number is that the sectioning image of 1 is containing neck.
Position in the middle of head and basis cranii
Within the scope of the sectioning image containing head, calculate the elemental area accounting between 350HU～3000HU, two extreme points can be drawn, at the bottom of one extreme point correspondence skull, another extreme point correspondence head intermediate layer, then by the elemental area accounting between-20HU～70HU, distinguish the two extreme point, head intermediate layer is positioned at the sectioning image that the big extreme point of accounting is corresponding, then be positioned at the sectioning image that another extreme point is corresponding at the bottom of skull.
Location chest
Eigenvalue calculation includes: every layer of sectioning image 1) calculates the accounting of HU value pixel gross area relative population portion elemental area between-910HU～-200HU, draws the accounting characteristic curve being axis of abscissas with the number of plies of cutting into slices.
Judging rules: if the elemental area accounting on sectioning image between-910HU～-200HU is more than 0.2, and bulk area is more than π * 10*10cm2, then containing chest.
Lung top, location
Calculating the maximum of the accounting characteristic curve of the pixel gross area between aforementioned-910HU～-200HU, curve is lung top along the extreme point of the right trailing edge of maximum.
The location base of lung
Calculating the accounting of pixel gross area relative population portion elemental area between the-910HU～-200HU of each sectioning image body center region below, draw the accounting curve being axis of abscissas with the number of plies of cutting into slices, curve is the base of lung along the extreme point of the left trailing edge of maximum.
In the middle of the heart of location
Be partitioned into two lungs, calculate the elemental area between the m-20HU～70HU of two lungs maximum time, for heart intermediate layer.
Location abdominal part
Eigenvalue calculation includes: every layer of sectioning image 1) calculates number and the area of the connected domain in CT value region between-910HU～-200HU and the center of each connected domain.
Judging rules: if connected domain number is more than 10 on sectioning image, and average area is less than π * 2*2cm2(average area computational methods: remove the connected domain of minimum area and maximum area, all the other connected domain areas are averaged), then containing abdominal part.
Liver top, location
Eigenvalue calculation includes: calculates each section body center accounting with the pixel gross area relative population portion elemental area between the-20HU～70HU in left region, draws the accounting curve being axis of abscissas with the number of plies of cutting into slices, and the maximum point of curvilinear motion gradient is liver top.
In the middle of the liver of location
Eigenvalue calculation includes: 1) calculate each section body center accounting with the pixel gross area relative population portion elemental area between the-20HU～70HU in left region, drawing the accounting curve being axis of abscissas with the number of plies of cutting into slices, curve peak within the scope of trunk sectioning image is liver intermediate layer.
Location pelvis
Eigenvalue calculation includes: every layer of sectioning image 1) calculates the accounting of HU value elemental area relative population portion elemental area between-200HU～-20HU, draws the accounting characteristic curve being axis of abscissas with the number of plies of cutting into slices；2) every layer of sectioning image is calculated the ratio of width to height of spongiosa region, draws a ratio of width to height characteristic curve being coordinate axes with the number of plies of cutting into slices.
Judging rules: if the ratio of width to height is more than 1.5, and the elemental area accounting between-200HU～-20HU is more than 0.45, then corresponding sectioning image is containing pelvis.
Location femur and hip joint
Eigenvalue calculation includes: every layer of sectioning image is calculated CT value elemental area between 350HU～3000HU and accounts for the characteristic curve of total body elemental area ratio.
Judging rules: find curve extreme point more than 0.15 within the scope of pelvis section, if there being two extreme points more than 0.15, it is then femoral joint near last layer of image, another extreme point is corresponding hip joint then, if there being the extreme point of 0.15, then judge this extreme point position respectively with the layer of image foremost layer and last surface layer from, with the layer of foremost layer from more than with the layer of back layer from, it is then hip joint, is otherwise femur.
Location lower limb
Eigenvalue calculation includes: every layer of sectioning image calculates number and the area of spongiosa region connected domain.
Judging rules: on sectioning image, connected domain number is 2, then containing lower limb.
Location knee
Eigenvalue calculation includes: every layer of sectioning image calculates the accounting of CT value elemental area relative population portion elemental area between 350HU～3000HU, draws the accounting characteristic curve being axis of abscissas with the number of plies of cutting into slices；
Judging rules: find accounting characteristic curve extreme point, and the connected domain number of extreme point place sectioning image is 2, then be knee joint place.
On the medical image of location provided by the invention, the method for organ is based on CT gradation of image statistical information, grey scale change rule, and organ-tissue shape, and the position relationship etc. between organ-tissue, method is simple.
On the medical image of location provided by the invention the method for organ propose characteristic curve enrich, can first quickly judge position contained by CT image, can sectioning image scope corresponding to histoorgan in position contained by Quick positioning map picture further, Position location accuracy is high, positioning result can be widely used in the application of other image procossing, such as segmentation, registration etc., the efficiency of raising pictures subsequent process and accuracy rate.
On the location medical image of the above embodiment of the present invention, the method for organ can be carried out in the computer-readable medium of the such as combination of computer software, hardware or computer software and hardware.For implementing for hardware, embodiment described in the present invention can be carried out in one or more special ICs (ASIC), digital signal processor (DSP), digital signal processor (DAPD), PLD (PLD), field programmable gate array (FPGA), processor, controller, microcontroller, microprocessor, selection combination for performing other electronic installation of above-mentioned functions or said apparatus.In some circumstances, this kind of embodiment can be carried out by controller.
Software is implemented, embodiment described in the present invention can be carried out by independent software modules such as such as program module (procedures) and function modules (functions), and each of which module performs one or more functions described herein and operation.Software code can be carried out by the application software write in properly programmed language, it is possible to is stored in internal memory, controller or processor perform.
Although the present invention describes with reference to current specific embodiment, but those of ordinary skill in the art will be appreciated that, above embodiments is intended merely to the explanation present invention, change or the replacement of various equivalence also can be made when without departing from spirit of the present invention, therefore, as long as to the change of above-described embodiment, modification all by the scope dropping on following claims in the spirit of the present invention.
Claims (15)
1. position a method for organ on medical image, comprise the following steps:
Step S1, input include the medical image of some sectioning images；
Step S2, to input image carry out pretreatment, filter out non-body pixel；
Step S3, every layer of sectioning image is calculated connected domain number, by judging connected domain number and position, remove the part outside the first organ, confirm that medical image includes the first organ；Every layer of sectioning image is calculated eigenvalue and obtains some eigenvalues, the number of plies of some eigenvalues and sectioning image forms characteristic curve, calculate eigenvalue to include every layer of sectioning image is calculated grey scale pixel value or CT value elemental area between the first scope relative to the ratio of the total elemental area in specific region, orient the sectioning image at organ place according to ratio.
2. the method for organ on the medical image of location as claimed in claim 1, it is characterized in that, described calculating eigenvalue also includes the ratio of width to height that every layer of sectioning image calculates spongiosa region, draws a ratio of width to height characteristic curve being axis of abscissas with the number of plies of sectioning image；Every layer of sectioning image is calculated spongiosa region area, draws the body area change characteristic curve being axis of abscissas with the number of plies of sectioning image.
3. the method for organ on the medical image of location as claimed in claim 1, it is characterized in that, described first organ includes lower limb, trunk and head and neck, step S3 judging, connected domain number and position include judging that connected domain is three and is positioned at two connected domains of both sides, removes the part outside the first organ and include removing two connected domains being arranged in both sides and namely remove the arm segment of image.
4. the method for organ on the medical image of location as claimed in claim 3, it is characterised in that in step S3, sectioning image connected domain number is two, then judge that lower limb are at the sectioning image that connected domain number is two.
5. the method for organ on the medical image of location as claimed in claim 4, it is characterized in that, described first ranges for 350HU～3000HU, step S3 includes the extreme point finding characteristic curve, if the connected domain number of the sectioning image that extreme point is corresponding is 2, then knee joint position is in the sectioning image that connected domain number is 2 corresponding to extreme point.
6. the method for organ on the medical image of location as claimed in claim 1, the total pixel in described specific region is total body pixel, described first ranges for-910HU～-200HU, if the ratio of CT value elemental area relative population portion elemental area between-910HU～-200HU is more than 0.2 on sectioning image, and total body elemental area is more than π * 100cm2, it is determined that chest is positioned at this sectioning image.
7. the method for organ on the medical image of location as claimed in claim 6, it is characterised in that step S3 includes the maximum calculating specific curves, it is judged that lung top is positioned at curve along sectioning image corresponding to the extreme point of the right trailing edge of maximum.
8. the method for organ on the medical image of location as claimed in claim 6, it is characterized in that, step S3 includes the ratio calculating the pixel gross area relative population portion elemental area between the-910HU～-200HU of each sectioning image body center region below, form accounting curve, it is judged that the base of lung is positioned at accounting curve along sectioning image corresponding to the extreme point of the left trailing edge of ratio maximum.
9. the method for organ on the medical image of location as claimed in claim 6, it is characterized in that, step S3 includes being partitioned into two lungs in CT image, calculate CT value elemental area between-20HU～70HU between each sectioning image two lung, in the middle of heart between two lungs the CT value sectioning image corresponding to elemental area maximum between-20HU～70HU.
10. the method for organ on the medical image of location as claimed in claim 6, it is characterized in that, step S3 includes number and area that every layer of sectioning image calculates the connected domain in CT value region between-910HU～-200HU, abdominal part is positioned at connected domain number more than 10, and connected domain average area is less than π * 4cm2Sectioning image on, it is connected domain by removing minimum area and maximum area that average area calculates, and the area of all the other connected domains is averaged.
11. the method for organ on the medical image of location as claimed in claim 10, it is characterized in that, calculating each sectioning image body center and calculate characteristic curve with left region, first ranges for-20HU～70HU, and liver top is positioned at the sectioning image corresponding to point that described characteristic curve variable gradient is maximum.
12. the method for organ on the medical image of location as claimed in claim 10, it is characterized in that, calculate each sectioning image body center and calculate characteristic curve with left region, first ranges for-20HU～70HU, is arranged in characteristic curve at the sectioning image corresponding to the peak of thorax abdomen sectioning image in the middle of liver.
13. the method for organ on the medical image of location as claimed in claim 2, it is characterized in that, the total pixel in described specific region is total body pixel, and described first ranges for 350HU～3000HU, head is positioned at the ratio of width to height less than 0.8, and total body elemental area is less than π * 100cm2, the ratio of CT value elemental area relative population portion elemental area between the first scope is more than 0.2, and connected domain number is the sectioning image corresponding to 1；Neck is positioned at the ratio of width to height less than 0.8, and total body elemental area is less than π * 100cm2, the ratio of CT value elemental area relative population portion elemental area between the first scope is less than 0.15, and connected domain number is the sectioning image corresponding to 1.
14. the method for organ on the medical image of location as claimed in claim 13, it is characterised in that within the scope of the sectioning image containing head, the ratio of CT value elemental area relative population portion elemental area between the first scope has two extreme points；Distinguishing said two extreme point by the ratio of CT value elemental area relative population portion elemental area between-20HU～70HU again, head intermediate layer is positioned at the sectioning image that the big extreme point of ratio is corresponding, is positioned at the sectioning image that another extreme point is corresponding at the bottom of skull.
15. the method for organ on the medical image of location as claimed in claim 1, it is characterized in that, described calculating eigenvalue also includes the ratio of width to height that every layer of sectioning image calculates spongiosa region, draws a ratio of width to height characteristic curve being axis of abscissas with the number of plies of sectioning image；Described first ranges for-200HU～-20HU, the total pixel in described specific region is total body pixel, every layer of sectioning image is calculated the accounting of HU value elemental area relative population portion elemental area between-200HU～-20HU, if described accounting is more than 0.45, and described the ratio of width to height is more than 1.5, then corresponding sectioning image is containing pelvis.
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| CN106815849A (en) * | 2017-01-18 | 2017-06-09 | 宁波江丰生物信息技术有限公司 | A kind of method for recognizing biopsy tissues |
| CN106898012A (en) * | 2017-01-19 | 2017-06-27 | 浙江大学 | CT images thoracic cavity profile automated detection method |
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| CN110555860A (en) * | 2018-06-04 | 2019-12-10 | 青岛海信医疗设备股份有限公司 | Method, electronic device and storage medium for marking rib region in medical image |
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| CN113469935A (en) * | 2021-04-15 | 2021-10-01 | 卡本(深圳)医疗器械有限公司 | CT image-based posterior superior iliac spine automatic detection and positioning method |
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