CN113139968A - Medical image segmentation device and method - Google Patents
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Abstract
The invention provides a medical image segmentation device and a medical image segmentation method. The medical image segmentation device comprises: a medical image acquisition module for acquiring a medical image comprising at least a portion of a pulmonary trachea of a patient; the first segmentation module is connected with the medical image acquisition module and used for segmenting the medical image to acquire a first segmentation result of the lung trachea; the first local image acquisition module is connected with the medical image acquisition module and used for acquiring at least one primary local image according to the medical image; the second segmentation module is connected with the first local image acquisition module and used for segmenting the first-level local image to acquire a second segmentation result of the lung trachea; and the trachea tree acquisition module is connected with the first segmentation module and the second segmentation module and is used for acquiring a trachea tree model. The medical image segmentation device can automatically achieve acquisition of the trachea tree model, and is high in efficiency and accuracy.
Description
Technical Field
The present invention relates to an image processing device, and more particularly, to a medical image segmentation device and method.
Background
Clinically, the diagnosis of diseases such as pulmonary bronchoconstriction, chronic obstructive pulmonary disease, obliterative bronchiolitis and the like depends on quantitative analysis of lung trachea, and the construction of a trachea tree model is helpful for quantitative analysis of lung trachea morphological changes. In addition to this, the tracheal tree model can also be applied to surgical bronchial navigation. However, the inventor finds that in practical application, the prior art mainly relies on medical staff to segment medical images by manual means to obtain a tracheal tree model, which is inefficient.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a medical image segmentation apparatus and method for solving the problem of low efficiency of manual segmentation in the prior art.
To achieve the above and other related objects, a first aspect of the present invention provides a medical image segmentation apparatus, comprising: a medical image acquisition module for acquiring a medical image comprising at least a portion of a pulmonary trachea of a patient; the first segmentation module is connected with the medical image acquisition module and used for segmenting the medical image to acquire a first segmentation result of the lung trachea; the first local image acquisition module is connected with the medical image acquisition module and used for acquiring at least one primary local image according to the medical image; the second segmentation module is connected with the first local image acquisition module and used for segmenting the first-level local image to acquire a second segmentation result of the lung trachea; and the trachea tree acquisition module is connected with the first segmentation module and the second segmentation module and is used for acquiring a trachea tree model, wherein the trachea tree model is obtained by fusing at least the first segmentation result and the second segmentation result of the lung trachea.
In an embodiment of the first aspect, the medical image segmentation apparatus further includes: the second local image acquisition module is connected with the first local image acquisition module and used for acquiring at least one secondary local image according to the primary local image; the third segmentation module is connected with the second local image acquisition module and used for segmenting the second-level local image to acquire a third segmentation result of the lung trachea; the trachea tree acquisition module is further connected with the third segmentation module, and the trachea tree model is obtained by fusing at least the first segmentation result, the second segmentation result and the third segmentation result of the lung trachea.
In an embodiment of the first aspect, the first local image capturing module includes: the trachea branch point acquisition unit is connected with the medical image acquisition module and is used for acquiring a trachea branch point of the lung trachea in the medical image; the first local image acquisition unit is connected with the trachea branch point acquisition unit and is used for acquiring the first-level local image according to the trachea branch point and the medical image.
In an embodiment of the first aspect, the first local image capturing module includes: the trachea terminal point acquisition unit is connected with the medical image acquisition module and is used for acquiring the trachea terminal point of the lung trachea in the medical image; and the second local image acquisition unit is connected with the trachea terminal point acquisition unit and is used for acquiring the primary local image according to the trachea terminal point and the medical image.
In an embodiment of the first aspect, the trachea distal point obtaining unit is further configured to obtain a centerline of the pulmonary trachea, and obtain the trachea distal point according to the centerline of the pulmonary trachea.
In an embodiment of the first aspect, the second local image obtaining unit is further configured to cluster the tracheal end points, and obtain the first-level local image based on a clustering result.
In an embodiment of the first aspect, the medical image segmentation apparatus further includes: the fracture area acquisition module is connected with the trachea tree acquisition module and used for acquiring a fracture area in the trachea tree model; and the fracture area repairing module is connected with the fracture area acquiring module and is used for repairing the fracture area.
In an embodiment of the first aspect, the fracture area repairing module includes: a seed point obtaining unit, configured to obtain a seed point, where the seed point is located in the fracture region; and the trachea repairing unit is connected with the seed point acquiring unit and used for expanding to acquire the trachea of the fracture area based on the seed point.
In an embodiment of the first aspect, the tracheal tree acquisition module is further configured to prune the tracheal tree model.
A second aspect of the present invention provides a medical image segmentation method, including: acquiring a medical image comprising at least part of the pulmonary trachea of a patient; segmenting the medical image to obtain a first segmentation result of the lung trachea; acquiring at least one primary local image according to the medical image; segmenting the first-level local image to obtain a second segmentation result of the lung trachea; obtaining a tracheal tree model, wherein the tracheal tree model is obtained by fusing at least the first segmentation result and the second segmentation result of the pulmonary trachea.
As described above, one technical solution of the medical image segmentation apparatus and method according to the present invention has the following beneficial effects:
the medical image segmentation device can obtain a first segmentation result of a lung trachea according to a medical image and obtain a second segmentation result of the lung trachea according to a first-level local image, and based on the first segmentation result and the second segmentation result, the medical image segmentation device is fused at least according to the first segmentation result and the second segmentation result of the lung trachea to obtain a trachea tree model. The process can be automatically completed through electronic equipment, manual participation is basically not needed, and the efficiency and the accuracy are high.
Drawings
Fig. 1A is a schematic structural diagram of a medical image segmentation apparatus according to an embodiment of the present invention.
Fig. 1B is a diagram illustrating an example of a medical image acquired by the medical image segmentation apparatus according to an embodiment of the present invention.
Fig. 1C is a diagram illustrating an example of a first-level local image acquired by the medical image segmentation apparatus according to an embodiment of the present invention.
Fig. 1D is a schematic structural diagram of a medical image segmentation apparatus according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a first local image acquisition module according to an embodiment of the medical image segmentation apparatus of the present invention.
Fig. 3A is a schematic structural diagram of a first partial image capturing module according to an embodiment of the medical image segmentation apparatus of the present invention.
Fig. 3B is a flowchart illustrating a process of repairing a fractured trachea according to the medical image segmentation apparatus of the present invention in an embodiment.
Fig. 4A and 4B are diagrams illustrating an exemplary trachea segmentation result obtained by the medical image acquisition device according to an embodiment of the present invention.
FIG. 5 is a flowchart illustrating a medical image segmentation method according to an embodiment of the present invention.
Description of the element reference numerals
1 medical image segmentation device
11 medical image acquisition module
12 first segmentation module
13 first local image acquisition module
131 trachea branch point acquisition unit
132 first local image acquisition unit
133 trachea distal point acquisition unit
134 second partial image obtaining unit
14 second segmentation module
15 trachea tree acquisition module
16 second local image acquisition module
17 third segmentation module
S11-S14
S21-S25
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the drawings only show the components related to the present invention rather than being drawn according to the number, shape and size of the components in actual implementation, and the type, number and proportion of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated. Moreover, in this document, relational terms such as "first," "second," and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
In the prior art, medical staff are mainly relied on to segment medical images in a manual mode to obtain a tracheal tree model, and the mode is low in efficiency. In view of the above problem, the present invention provides a medical image segmentation apparatus, which is capable of obtaining a first segmentation result of a pulmonary trachea from a medical image and obtaining a second segmentation result of the pulmonary trachea from a primary local image, and based on this, the medical image segmentation apparatus performs fusion to obtain a tracheal tree model at least according to the first segmentation result and the second segmentation result of the pulmonary trachea. The process can be automatically completed through electronic equipment, manual participation is basically not needed, and the efficiency and the accuracy are high.
Referring to fig. 1A, in an embodiment of the present invention, the medical image segmentation apparatus 1 includes a medical image acquisition module 11, a first segmentation module 12, a first partial image acquisition module 13, a second segmentation module 14, and a tracheal tree acquisition module 15.
The medical image acquisition module 11 is configured to acquire a medical image including at least a portion of the trachea of the lungs of a patient, such as a thoracic cavity image. In this embodiment, an example of the medical image is shown in fig. 1B.
The first segmentation module 12 is connected to the medical image acquisition module 11, and is configured to segment the medical image to acquire a first segmentation result of the lung trachea. The first segmentation result is obtained by the first segmentation module 12 by segmenting the medical image itself, and belongs to the whole segmentation result of the lung trachea. The first segmentation result comprises segmentation results of 1-4 grades of air pipes.
The first local image obtaining module 13 is connected to the medical image obtaining module 11, and is configured to obtain at least one primary local image according to the medical image, where a range of each primary local image is smaller than a range of the medical image, and the primary local image includes more details of a lung trachea than the medical image. In this embodiment, an example of the primary local image is shown in fig. 1C. The first local image acquisition module 13 may obtain the primary local image by segmenting the medical image, for example.
The second segmentation module 14 is connected to the first local image obtaining module 13, and is configured to segment the primary local image to obtain a second segmentation result of the lung trachea. The second segmentation result is obtained by segmenting the primary local image by the second segmentation module 14, and is a segmentation result of a finer lung trachea than the first segmentation result. The second segmentation result comprises segmentation results of 4-6 grades of air pipes.
The trachea tree obtaining module 15 is connected to the first segmentation module 12 and the second segmentation module 14, and is configured to obtain a trachea tree model. Wherein the tracheal tree model is fused from at least a first segmentation result and a second segmentation result of the pulmonary trachea.
Optionally, referring to fig. 1D, the medical image segmentation apparatus 1 further includes a second local image acquisition module 16 and a third segmentation module 17.
The second local image obtaining module 16 is connected to the first local image obtaining module 13, and is configured to obtain at least one secondary local image according to the primary local image, where a range of each secondary local image is smaller than a range of the primary local image, and the secondary local image includes further details of the lung trachea than the primary local image. The second local image obtaining module 16 may obtain the second-level local image by segmenting the first-level local image, for example.
The third segmentation module 17 is connected to the second local image acquisition module 16, and is configured to segment the second-level local image to acquire a third segmentation result of the pulmonary trachea. The third segmentation result is obtained by segmenting the second-level partial image by the third segmentation module 17, and is a segmentation result of a finer lung trachea than the second segmentation result. The third segmentation result comprises segmentation results of 6-8 grades of tracheas.
The trachea tree obtaining module 15 is further connected to the third segmentation module 17, and at this time, the trachea tree model is obtained by fusing at least the first segmentation result, the second segmentation result and the third segmentation result of the lung trachea.
In the present embodiment, the medical image segmentation apparatus 1 is not limited to the above configuration. The medical image segmentation device may further include another module to further segment the second local image step by step, so as to obtain multiple levels of local images including more lung trachea details, and further obtain multiple levels of segmentation results, and the trachea tree acquisition module may acquire a trachea tree model including more branches based on the multiple levels of segmentation results. For example, the medical image segmentation apparatus may further segment the second-level local image to obtain a third-level local image, and segment the third-level local image to obtain a fourth-level local image … …; moreover, the medical image segmentation apparatus may obtain a fourth segmentation result according to the three-level local image, and obtain a fifth segmentation result … … according to the four-level local image; the trachea tree obtaining module may further fuse the fourth segmentation result and the fifth segmentation result … … to form the trachea tree model.
As can be seen from the above description, the medical image segmentation apparatus 1 according to the present embodiment is capable of obtaining a first segmentation result of the pulmonary trachea from the medical image, and obtaining a second segmentation result of the pulmonary trachea from the primary local image, based on which the medical image segmentation apparatus performs fusion to obtain the tracheal tree model at least according to the first segmentation result and the second segmentation result of the pulmonary trachea. The process can be automatically completed through electronic equipment, manual participation is basically not needed, and the efficiency and the accuracy are high.
In addition, the medical image segmentation apparatus 1 of the present embodiment can obtain the segmentation results of lung tracheas of different levels and fuse the segmentation results into the tracheal tree model. For example, the trachea tree model may include 1-8 levels of trachea, and in the related art, the trachea tree model is limited by the display range and resolution of the medical image, and only can realize automatic segmentation of 4-6 levels of trachea, so that compared with the related art, the trachea tree model acquired by the medical image segmentation device 1 in the present embodiment can include more levels of trachea, thereby providing more detailed trachea information for medical staff.
Referring to fig. 2, in an embodiment of the invention, the first local image capturing module 13 includes an airway branch point capturing unit 131 and a first local image capturing unit 132. The trachea branch point obtaining unit 131 is connected to the medical image obtaining module 11, and is configured to obtain a trachea branch point of the pulmonary trachea in the medical image, where a mode of obtaining the trachea branch point may be implemented by using an existing image recognition technology. The first local image acquiring unit 132 is connected to the air pipe branch point acquiring unit 131, and is configured to acquire the primary local image according to the air pipe branch point and the medical image.
For example, the trachea branch point acquisition unit 131 may acquire left and right lung lobe branch points from a lung trachea mask through an image recognition technique, and the first partial image acquisition unit 132 may divide the medical image into a first-order partial image including a left lung bronchus and a first-order partial image including a right lung bronchus according to the left and right lung lobe branch points.
It can be understood that, the corresponding modules in the medical image segmentation apparatus may employ the similar techniques described above, segment the first-level local image into a second-level local image according to the trachea branch point, and segment the second-level local image into a third-level local image … …, which is not described herein in detail.
Referring to fig. 3A, in an embodiment of the invention, the first local image capturing module 13 includes a tracheal end point capturing unit 133 and a second local image capturing unit 134.
The tracheal end point acquiring unit 133 is connected to the medical image acquiring module 11, and is configured to acquire a tracheal end point of the pulmonary trachea in the medical image, where the tracheal end point may be acquired by using an existing image recognition technology.
Optionally, the tracheal end point obtaining unit 133 is further configured to obtain a central line of the pulmonary trachea in the medical image, and obtain the tracheal end point according to the central line of the pulmonary trachea. For example, the trachea end point acquisition unit 133 may acquire an end point of a center line of the pulmonary trachea as the trachea end point.
The second local image obtaining unit 134 is connected to the tracheal end point obtaining unit 133, and is configured to obtain the first-level local image according to the tracheal end point and the medical image. Specifically, the second local image obtaining unit 134 may obtain, as the primary local image, an image in a certain range around each tracheal end point, where the size and shape of the range may be set according to actual requirements.
Alternatively, in view of the fact that there may be some closer points among the tracheal end points acquired by the tracheal end point acquiring unit 133, and if acquiring a primary local image for each tracheal end point may increase unnecessary computation, the second local image acquiring unit 134 may be further configured to cluster the tracheal end points to acquire a clustering result, and acquire the primary local image based on the clustering result. Specifically, after clustering, the second local image obtaining unit 134 can obtain a plurality of sets of tracheal end points, and obtain a plurality of primary local images based on the plurality of sets of tracheal end points, where each primary local image includes all tracheal end points in one set of tracheal end points.
It can be understood that, the corresponding modules in the medical image segmentation apparatus may employ the similar techniques described above, segment the first-level local image into a second-level local image according to the end point of the trachea, and segment the second-level local image into a third-level local image … …, which is not described herein in detail.
Considering the problems of limited resolution of the radiological image, low contrast of the small trachea, blurring and the like, the trachea segmentation is easy to break. In an embodiment of the invention, the medical image segmentation apparatus further includes a fracture region acquisition module and a fracture region restoration module.
And the fracture area acquisition module is connected with the trachea tree acquisition module and is used for acquiring a fracture area in the trachea tree model. Specifically, the fracture region obtaining module may obtain a start point and a terminal point of each bronchus by extracting a center line of each bronchus in the lung trachea, and may obtain the fracture region in the tracheal tree model based on the start point and the terminal point of each bronchus.
And the fracture area repairing module is connected with the fracture area repairing module and is used for repairing the fracture area.
Optionally, the fracture region repairing module includes a seed point obtaining unit and an air pipe repairing unit, where the seed point obtaining unit is configured to obtain one or more seed points in the fracture region. The trachea repairing unit is connected with the seed point acquiring unit and used for performing expansion growth based on the seed points to acquire the trachea of the fracture area.
Optionally, referring to fig. 3B, a specific implementation method of the trachea repair unit to repair a trachea includes:
s11, obtaining an output probability map (probability map) and a multi-scale hessian-based filter map (multi-scale hessian-based filter map) of the trachea tree model.
And S12, overlapping the probability map and the multi-scale Hessian filter map to obtain a trachea similarity probability map.
S13, using the trachea terminal point in the trachea tree model as a seed point, based on the trachea similarity probability map, using a region growing algorithm to generate connection paths among all the seed points, and obtaining the distance of each connection path.
And S14, connecting the seed points corresponding to the connection path with the minimum distance to realize the repair of the fracture area.
In addition, in consideration of the fact that the tracheal wall and the boundary thereof are blurred and broken, which may easily cause leakage in tracheal segmentation, in an embodiment of the present invention, the tracheal tree acquisition module is further configured to prune the tracheal tree model. Here, the leak refers to a portion of the segmented airway tree model that has a significantly large difference in radius from the actual airway, for example, the leak portion shown in fig. 4A. The trachea tree acquisition module may prune the trachea centerline based on the trachea centerline, morphological connectivity of the trachea, and the trachea radius, and eliminate a leakage portion in the trachea tree model based on a pruning result.
Specifically, the trachea tree acquisition module calculates the radius of the trachea branch based on the trachea centerline to obtain the minimum radius of each branch, and positions a part of the trachea branch with a radius larger than a times of the minimum radius of the branch, namely a part with leakage, wherein a is a positive number larger than 1. Based on the method, the trachea tree acquisition module generates a new trachea by adopting morphological expansion according to the central line of the trachea and the radiuses of the upper and lower trachea of the leaking part, and filtering of the leaking part of the trachea can be realized. For example, please refer to fig. 4B, which is an exemplary diagram of a result obtained by pruning the airway tree model by the airway tree obtaining module in this embodiment.
Based on the above description of the medical image segmentation apparatus, the invention also provides a medical image segmentation method. Specifically, referring to fig. 5, in an embodiment of the present invention, the medical image segmentation method can be implemented by the medical image segmentation apparatus 1 shown in fig. 1A or fig. 1D, and specifically includes the following steps:
s21, a medical image is acquired, the medical image including at least a portion of a pulmonary trachea of the patient.
S22, the medical image is segmented to obtain a first segmentation result of the lung trachea.
S23, acquiring at least one primary local image according to the medical image.
S24, the first-level local image is segmented to obtain a second segmentation result of the lung trachea.
S25, obtaining a tracheal tree model, wherein the tracheal tree model is obtained by fusing at least the first segmentation result and the second segmentation result of the lung trachea.
The above steps S11 to S15 correspond to the functions of the corresponding modules in the medical image segmentation apparatus 1 shown in fig. 1A or fig. 1D one by one, and are not described herein for saving the description space.
The protection scope of the medical image segmentation method according to the present invention is not limited to the execution sequence of the steps illustrated in the embodiment, and all the solutions of the prior art, including the steps addition, subtraction, and step replacement according to the principles of the present invention, are included in the protection scope of the present invention.
The present invention also provides a medical image segmentation apparatus, which can implement the medical image segmentation method of the present invention, but the implementation apparatus of the medical image segmentation method of the present invention includes, but is not limited to, the structure of the medical image segmentation apparatus as illustrated in this embodiment, and all structural modifications and substitutions of the prior art made according to the principles of the present invention are included in the scope of the present invention.
The medical image segmentation device can obtain a first segmentation result of the lung trachea according to the medical image and obtain a second segmentation result of the lung trachea according to the first-level local image, and based on the first segmentation result and the second segmentation result, the medical image segmentation device performs fusion at least according to the first segmentation result and the second segmentation result of the lung trachea to obtain the trachea tree model. The process can be automatically completed through electronic equipment, manual participation is basically not needed, and the efficiency and the accuracy are high.
Furthermore, the medical image segmentation apparatus can be configured to extract a tracheal centerline based on the tracheal mask and extract tracheal branch points and end points based on the tracheal centerline, thereby classifying the pulmonary trachea. Based on this, the medical image segmentation device can realize the segmentation of main trachea models (corresponding to 1-4 levels of tracheas), branch trachea models (corresponding to 4-6 levels of tracheas) and local small trachea models (corresponding to 6-8 levels of tracheas) by carrying out hierarchical segmentation on the trachea of the lung. By adopting the thought of grading segmentation, the medical image segmentation device can effectively improve the sensitivity of trachea segmentation below 2 mm.
In conclusion, the present invention effectively overcomes various disadvantages of the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A medical image segmentation apparatus, characterized in that the medical image segmentation apparatus comprises:
a medical image acquisition module for acquiring a medical image comprising at least a portion of a pulmonary trachea of a patient;
the first segmentation module is connected with the medical image acquisition module and used for segmenting the medical image to acquire a first segmentation result of the lung trachea;
the first local image acquisition module is connected with the medical image acquisition module and used for acquiring at least one primary local image according to the medical image;
the second segmentation module is connected with the first local image acquisition module and used for segmenting the first-level local image to acquire a second segmentation result of the lung trachea;
and the trachea tree acquisition module is connected with the first segmentation module and the second segmentation module and is used for acquiring a trachea tree model, wherein the trachea tree model is obtained by fusing at least the first segmentation result and the second segmentation result of the lung trachea.
2. The medical image segmentation apparatus according to claim 1, further comprising:
the second local image acquisition module is connected with the first local image acquisition module and used for acquiring at least one secondary local image according to the primary local image;
the third segmentation module is connected with the second local image acquisition module and used for segmenting the second-level local image to acquire a third segmentation result of the lung trachea;
the trachea tree acquisition module is further connected with the third segmentation module, and the trachea tree model is obtained by fusing at least the first segmentation result, the second segmentation result and the third segmentation result of the lung trachea.
3. The medical image segmentation apparatus according to claim 1, wherein the first local image acquisition module includes:
the trachea branch point acquisition unit is connected with the medical image acquisition module and is used for acquiring a trachea branch point of the lung trachea in the medical image;
the first local image acquisition unit is connected with the trachea branch point acquisition unit and is used for acquiring the first-level local image according to the trachea branch point and the medical image.
4. The medical image segmentation apparatus according to claim 1, wherein the first local image acquisition module includes:
the trachea terminal point acquisition unit is connected with the medical image acquisition module and is used for acquiring the trachea terminal point of the lung trachea in the medical image;
and the second local image acquisition unit is connected with the trachea terminal point acquisition unit and is used for acquiring the primary local image according to the trachea terminal point and the medical image.
5. The medical image segmentation apparatus according to claim 4, wherein: the trachea terminal point acquisition unit is further used for acquiring the central line of the lung trachea and acquiring the trachea terminal point according to the central line of the lung trachea.
6. The medical image segmentation apparatus according to claim 4, wherein: the second local image acquisition unit is further configured to cluster the tracheal end points and acquire the first-level local image based on a clustering result.
7. The medical image segmentation apparatus according to claim 1, further comprising:
the fracture area acquisition module is connected with the trachea tree acquisition module and used for acquiring a fracture area in the trachea tree model;
and the fracture area repairing module is connected with the fracture area acquiring module and is used for repairing the fracture area.
8. The medical image segmentation device according to claim 7, wherein the fracture region restoration module comprises:
a seed point obtaining unit, configured to obtain a seed point, where the seed point is located in the fracture region;
and the trachea repairing unit is connected with the seed point acquiring unit and used for expanding to acquire the trachea of the fracture area based on the seed point.
9. The medical image segmentation apparatus according to claim 1, wherein: the trachea tree acquisition module is also used for pruning the trachea tree model.
10. A medical image segmentation method, characterized in that the medical image segmentation method comprises:
acquiring a medical image comprising at least part of the pulmonary trachea of a patient;
segmenting the medical image to obtain a first segmentation result of the lung trachea;
acquiring at least one primary local image according to the medical image;
segmenting the first-level local image to obtain a second segmentation result of the lung trachea;
obtaining a tracheal tree model, wherein the tracheal tree model is obtained by fusing at least the first segmentation result and the second segmentation result of the pulmonary trachea.
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