WO2020238043A1 - Unet network-based lung lobe segmentation method and apparatus, computer readable storage medium - Google Patents

Abstract

The present invention relates to the field of lung lobe image processing and provides a UNet network-based lung lobe segmentation method and apparatus, and a computer readable storage medium. The lung lobe segmentation method comprises: acquiring lung CT image data from an image input device; normalizing the input lung CT image data; filtering an intra-pulmonary area and an extra-pulmonary area from the processed image data using a 2D UNet network, and using the intra-pulmonary area as a candidate pulmonary area; segmenting the candidate pulmonary area into five lung lobe mask areas using a 3D UNet network to obtain upper left lobe, lower left lobe, upper right lobe, middle right lobe, and lower right lobe areas; performing morphological processing on the five lung lobes mask areas to obtain final lung lobe segmentation results; and separately storing the lung lobe segmentation results in a memory, or outputting and displaying the results on a monitor screen. By means of the present invention, lung lobes can be extracted rapidly and accurately by means of UNet networks to locate the position of lung cancer, so as to provide guidance for doctors in the diagnosis and treatment of lung cancers.

Description

Lung lobe segmentation method, device and computer-readable storage medium based on UNet network Technical field

The present invention relates to the technical field of lung lobe image processing, in particular to a method, device and computer-readable storage medium for lung lobe segmentation based on UNet network.

Background technique

With the popularization of CT applications, it facilitates the early screening of lung cancer. Statistics in recent years have found that the incidence of lung cancer is getting higher and higher, and it is also the primary cause of cancer mortality. Surgical removal of lung lobes is the primary method of treatment for lung cancer. Traditional lung lobes segmentation algorithms generally use gray information to find lung fissures, and then extract lung lobes based on lung fissures and other anatomical information (trachea and blood vessels). This method is not only computationally expensive, but many lung fissures are affected by diseases. , Illegible, it is difficult to find the lung fissures in all the slices, making it difficult to extract the lung lobe. How to extract lung lobes quickly and accurately through UNet network, locate lung cancer location, and provide guidance for doctors' diagnosis and treatment has become the research focus of the industry.

technical problem

The main purpose of the present invention is to provide a lung lobe segmentation method, device and computer readable storage medium based on UNet network, aiming to solve the problem that the existing lung lobe segmentation method is limited by individual lung morphological differences resulting in low efficiency and low accuracy of lung lobe segmentation Technical issues.

Technical solutions

In order to achieve the above objective, the present invention provides a lung lobe segmentation device based on UNet network, including a processor suitable for implementing various computer program instructions and a memory suitable for storing multiple computer program instructions, characterized in that the computer program The instructions are loaded by the processor and execute the following steps: obtain lung CT image data from the image input device; normalize the input lung CT image data; use 2D UNet network to filter the processed lung CT image data Intrapulmonary area and extrapulmonary area, and the intrapulmonary area is regarded as the lung area candidate area; 3D is used for the lung area candidate area The UNet network segmented five lung lobe mask regions to obtain the upper left lobe, lower left lobe, upper right lobe, middle right lobe, and lower right lobe. Morphological processing was performed on the five lung lobe mask regions to obtain the final lung lobe segmentation results; The results of lung lobes segmentation are stored in the memory, or output and displayed on the monitor screen.

Preferably, the step of normalizing the input lung CT image data includes: preprocessing the input lung CT image data to limit the lung CT image data to [-1000, 400] The interval is then normalized to the interval [0,1] to exclude non-lung areas with higher brightness.

Preferably, the step of performing morphological processing on the mask regions of the five lung lobes respectively includes the following steps: removing the area outside the mask regions of the five lung lobes and filling the holes in the mask regions of the five lung lobes.

Preferably, the step of using the 2D UNet network to filter the intra-pulmonary area and the extra-pulmonary area on the processed image data, and using the intra-pulmonary area as the candidate lung area includes the following steps: combining the lung CT image and the lung area gold Standard images are all scaled to the size [256, 256]; input the scaled lung CT image and lung area gold standard image into the 2D UNet network to segment the lung area image including the lung area and the lung area; the 2D The lung area image obtained by UNet output is then scaled to the original image size, and each layer of lung area is expanded with a radius of 5 pixels; the bounding box of the lung area is extracted according to the size of the lung area, and the lungs are extracted from the bounding box Area candidate area.

Preferably, the step of segmenting five lung lobe mask regions using a 3D UNet network for the lung region candidate region includes the following steps: randomly cropping the lung CT images to obtain a size of [128, 128, 64], and After random change, rotation, upside down, and size change for image enhancement processing; the lung area image data is multiplied by the processed lung CT image data and the lung area image data is multiplied by the lung leaf gold standard image data and input into the 3D UNet network. Lung lobe segmentation is performed on the CT image of the lung; the segmented lung lobe image is restored to the original size to obtain 5 different lung lobe mask regions.

On the other hand, the present invention also provides a lung lobe segmentation method based on UNet network, which is applied to a computer device. The lung lobe segmentation method based on UNet network includes the following steps: acquiring lung CT image data from an image input device; Normalize the lung CT image data; use 2D for the processed lung CT image data UNet network screens out the lung area and the lung area, and uses the lung area as the lung area candidate area; 3D is used for the lung area candidate area The UNet network segmented five lung lobe mask regions to obtain the upper left lobe, lower left lobe, upper right lobe, middle right lobe, and lower right lobe. Morphological processing was performed on the five lung lobe mask regions to obtain the final lung lobe segmentation results; The results of lung lobes segmentation are stored in the memory, or output and displayed on the monitor screen.

Preferably, the step of normalizing the input lung CT image data includes the following steps: preprocessing the input lung CT image data to limit the lung CT image data to [-1000, 400 ] This interval is then normalized to the interval [0,1] to exclude non-lung areas with higher brightness.

Preferably, the step of performing morphological processing on the mask regions of the five lung lobes respectively includes the following steps: removing the area outside the mask regions of the five lung lobes and filling the holes in the mask regions of the five lung lobes.

Preferably, the step of using the 2D UNet network to filter the intra-pulmonary area and the extra-pulmonary area on the processed image data, and using the intra-pulmonary area as the candidate lung area includes the following steps: combining the lung CT image and the lung area gold Standard images are all scaled to the size [256, 256]; input the scaled lung CT image and lung area gold standard image into the 2D UNet network to segment the lung area image including the lung area and the lung area; the 2D The lung area image obtained by UNet output is then scaled to the original image size, and each layer of lung area is expanded with a radius of 5 pixels; the bounding box of the lung area is extracted according to the size of the lung area, and the lungs are extracted from the bounding box Area candidate area.

Preferably, the step of segmenting five lung lobe mask regions using a 3D UNet network for the lung region candidate region includes the following steps: randomly cropping the lung CT images to obtain a size of [128, 128, 64], and After random change, rotation, upside down, and size change for image enhancement processing; the lung area image data is multiplied by the processed lung CT image data and the lung area image data is multiplied by the lung leaf gold standard image data and input into the 3D UNet network. Lung lobe segmentation is performed on the CT image of the lung; the segmented lung lobe image is restored to the original size to obtain 5 different lung lobe mask regions.

In another aspect, the present invention also provides a computer-readable storage medium that stores a plurality of computer program instructions that are loaded by a processor of a computer device and execute the UNet network-based lung lobe The method steps of the segmentation method.

Beneficial effect

Compared with the prior art, the lung lobe segmentation method, device and computer-readable storage medium based on UNet network of the present invention can normalize lung CT images and use 2D UNet network to quickly obtain lung CT images. Accurately extract the lung area, remove the influence of noise outside the lung area, and then use the 3D UNet network to segment the lung lobes in the lung CT image to obtain five lung lobes mask areas, thereby efficiently controlling the accuracy and speed of lung lobes segmentation, and improving The efficiency of lung lobes segmentation is not limited to individual lung morphological differences. The invention realizes fast and accurate extraction of lung lobes through UNet network, locates the position of lung cancer, and provides medical guidance for the diagnosis and treatment of lung cancer for doctors.

Description of the drawings

1 is a block diagram of the structure of a preferred embodiment of a lung lobe segmentation device based on UNet network of the present invention;

2 is a method flowchart of a preferred embodiment of a lung lobe segmentation method based on UNet network of the present invention;

Figure 3 is a schematic diagram of extracting lung regions from lung CT images using 2D UNet network;

Figure 4 is a schematic diagram of segmenting five lung lobes from a lung CT image using a 3D UNet network.

The realization of the objectives, functional characteristics and advantages of the present invention will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

Embodiments of the invention

In order to further explain the technical means and effects of the present invention to achieve the intended purpose of the invention, the specific implementation, structure, features and effects of the present invention will be described in detail below with reference to the drawings and preferred embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, but not to limit the present invention.

Referring to Fig. 1, Fig. 1 is a schematic structural diagram of a preferred embodiment of a lung lobe segmentation device based on UNet network of the present invention. In this embodiment, the lung lobe segmentation device 1 based on the UNet network includes, but is not limited to, a memory 11 suitable for storing various computer program instructions, a processor 12 that executes various computer program instructions, and a display 13. Both the memory 11 and the display 13 are electrically connected to the processor 12 through an electrical connection line, and are connected to the processor 12 through a data bus for data transmission. The processor 12 can call the lung lobe segmentation program 10 based on the UNet network stored in the memory 11, and execute the lung CT image data input by the lung lobe segmentation program 10 from the image input device 2, and use the UNet network based on lung Partial CT image data segmented the lung lobes. The lung lobe segmentation device 1 may be a personal computer, a notebook computer, a server, and other computer devices installed with the lung lobe segmentation program 10 based on the UNet network of the present invention.

In this embodiment, the lung lobe segmentation device 1 is connected with an image input device 2. The image input device 2 can be a CT scanner, which can scan the human lungs to obtain lung CT images; the image input device 2 can also be a medical The image database stores CT images of the lungs of the human body. The lung lobe segmentation device 1 can obtain lung CT images from the image input device 2, and execute the lung lobe segmentation program 10 through the processor 12 to process the lung CT images, and use the UNet network to quickly and accurately segment the lung CT images. Lung area.

In this embodiment, the memory 11 includes at least one type of readable storage medium. The readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), and magnetic memory. , Disks, CDs, etc. In some embodiments, the memory 11 may be an internal storage unit of the lung lobe segmentation device 1 based on the UNet network, for example, the hard disk, read-only memory ROM, random access memory RAM, and electronic memory of the lung lobe segmentation device 1 based on UNet network. Erase and write memory EEPROM, flash memory FLASH or CD, etc. In other embodiments, the memory 11 may also be an external storage device of the lung lobe segmentation device 1 based on the UNet network. For example, a plug-in hard disk equipped on the lung lobe segmentation device 1 based on the UNet network, a smart memory card (Smart Media Card, SMC), Secure Digital (Secure Digital, SD) card, flash card (Flash Card), etc. Further, the memory 11 may also include both the internal storage unit of the lung lobe segmentation device 1 based on the UNet network and an external storage device. The memory 11 can be used not only to store application software and various data installed in the lung lobe segmentation device 1 based on the UNet network, for example, to store the program code of the lung lobe segmentation program 10 based on the UNet network, etc., but also to temporarily store Data to be output or to be output.

In this embodiment, the processor 12 may be a central processing unit (Central Processing Unit) in some embodiments. Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips are used to call and run the program code or processing data stored in the memory 11, such as executing the lung lobe segmentation program 10 based on the UNet network. The display 13 may be a touch display screen or a general LED display screen, which can display the results of lung lobe segmentation and the segmented lung lobe regions in different parts.

Optionally, in other embodiments, the lung lobe segmentation program 10 based on the UNet network may also be divided into one or more modules, and one or more modules are stored in the memory 11 and processed by one or more The present invention is executed by the processor (the processor 12 in this embodiment). The module referred to in the present invention refers to a series of computer program instruction segments that can complete specific functions, and is used to describe the lung lobe segmentation program 10 based on the UNet network. The execution process in the lung lobe segmentation device 1 based on UNet network is described.

In this embodiment, the lung lobe segmentation program 10 based on the UNet network is composed of program modules composed of multiple computer program instructions, including, but not limited to, the lung image input module 101, the image data processing module 102, and the lung area The screening module 103, the lung lobe segmentation module 104, the lung lobe processing module 105, and the lung lobe segmentation result output module 106. The module referred to in the present invention refers to a series of computer program instruction segments that can be executed by the processor 12 of the lung lobe segmentation device 1 and can complete fixed functions, and are stored in the memory 11.

The lung image input module 101 is used to obtain lung CT image data from the image input device 2. In this embodiment, the image input device 2 may be a CT scanner, which can scan human lungs to obtain lung CT images; the image input device 2 may also be a medical image database, storing CT images of human lungs to be segmented .

The image data processing module 102 is used for normalizing the input lung CT image data to exclude non-pulmonary regions with higher brightness. In this embodiment, the image data processing module 102 preprocesses the input lung CT image data, limits the image data to the interval [-1000, 400], and then normalizes it to [0, 1] This interval is to exclude non-lung areas with higher brightness.

The intra-pulmonary area screening module 103 is used to segment the intra-pulmonary area and the extra-pulmonary area using a 2D UNet network on the processed image data, and use the intra-pulmonary area as a candidate lung area. In this embodiment, the 2D UNet network uses a multi-layer 2D convolutional layer as the main structure of the encoding-decoding network for detection and output. In order to ensure that the image can be correctly input into the 2D UNet network, 2D The size used for UNet network input is [256, 256]. As shown in Figure 3, the images input by the 2D UNet network are the lung CT image (a1) processed in step S12 and the gold standard image (a2) of the lung area outlined by the corresponding doctor, the lung CT image (a1) and the lung All the regional gold standard images (a2) need to be scaled to the size of [256, 256], and then input to the 2D UNet network to segment the lung region and the lung region segmentation results (a3). In 2D The lung area image obtained during UNet output is scaled back to the original image size, and each layer of lung area is expanded with a radius of 5 pixels, and the bounding box of the lung area is extracted according to the size of the lung area, and extracted from the bounding box The candidate area of the lung area (a3).

The lung lobe segmentation module 104 is configured to use a 3D UNet network to screen and segment five lung lobe mask regions for the lung area candidate area to obtain the upper left lobe, the lower left lobe, the upper right lobe, the middle right lobe, and the lower right lobe area; in this embodiment The 3D UNet network adopts an encoding-decoding network with a multi-layer 3D convolutional layer as the main structure for detection and output. In order to ensure that the image can be correctly input into the 3D UNet network, random cropping is used in the training process to obtain an input image of [128, 128, 64] size, which undergoes random change, rotation, upside down, and scale changes for image enhancement processing. Input to the 3D UNet network for network model training. As shown in Figure 4, the input image of the 3D UNet network uses the lung area image data output in step S23 multiplied by the lung CT image processed in step S12 (b1) and the lung area image data output in step S23 multiplied by the doctor's outline Gold standard image of lung leaf (b2); mixed loss function: Dice loss function and Focal are used during model training Loss function, where the Dice Loss function is used to measure the quality of the result in image segmentation, Focal The loss function is used to deal with category imbalance. In the test phase, a moving sliding window method can be used to obtain an image with a size of [128, 128, 64]. After the trained model is predicted, the original image can be restored to obtain 5 different lung lobe mask regions (b3).

The lung lobe processing module 105 is used to perform morphological processing on the five lung lobes mask regions to obtain the final lung lobe segmentation results; in this embodiment, the five lung lobes mask regions are respectively subjected to morphological processing The steps include: removing the area outside the mask area of the five lung lobes and filling the holes in the mask area of the five lung lobes to obtain the final lung lobe segmentation result.

The lung lobe segmentation result output module 106 is used to store the lung lobe segmentation result in the memory 11 or output and display it on the screen of the display 13. In this embodiment, the segmented images of different lung lobes are displayed on the screen of the display 13 for doctors to provide a more comprehensive reference in disease diagnosis and treatment.

Referring to FIG. 2, it is a flowchart of a preferred embodiment of a lung lobe segmentation method based on UNet network of the present invention. In this embodiment, the various method steps of the lung lobe segmentation method based on the UNet network are implemented by a computer software program, which is stored in a computer-readable storage medium in the form of computer program instructions (for example, in this embodiment) In the memory 11), the computer-readable storage medium may include: read-only memory, random access memory, magnetic or optical disk, etc., and the computer program instructions can be loaded by a processor (for example, the processor 12 in this embodiment) and execute the following steps:

Step S21: Acquire CT image data of the lung from the image input device 2. In this embodiment, the image input device 2 may be a CT scanner, which can scan human lungs to obtain lung CT images; it may also be a medical image database that stores CT images of human lungs to be segmented.

Step S22: Perform normalization processing on the input lung CT image data to exclude non-pulmonary regions with higher brightness. In this embodiment, the image data processing module 102 preprocesses the input lung CT image data, limits the image data to the interval [-1000, 400], and then normalizes it to [0, 1] This interval is to exclude non-lung areas with higher brightness.

Step S23: Use a 2D UNet network to segment the processed image data into the lung area and the lung area, and use the lung area as a candidate lung area. In this embodiment, the 2D UNet network uses a multi-layer 2D convolutional layer as the main structure of the encoding-decoding network for detection and output. In order to ensure that the image can be correctly input into the 2D UNet network, 2D The size used for UNet network input is [256, 256]. As shown in Figure 3, the images input by the 2D UNet network are the lung CT image (a1) processed in step S12 and the gold standard image (a2) of the lung area outlined by the corresponding doctor, the lung CT image (a1) and the lung area All the gold standard images (a2) need to be scaled to the size of [256, 256], and then input into the 2D UNet network to segment the lung region and the lung region segmentation results (a3). In 2D The lung area image obtained during UNet output is scaled back to the original image size, and each layer of lung area is expanded with a radius of 5 pixels, and the bounding box of the lung area is extracted according to the size of the lung area, and extracted from the bounding box The candidate area of the lung area (a3).

Step S24: Use the 3D UNet network to screen and segment the five lung lobe mask regions for the lung area candidate region to obtain the regions of the upper left lobe, the lower left lobe, the upper right lobe, the middle right lobe, and the lower right lobe; in this embodiment, the 3D UNet The network uses a multi-layer 3D convolutional layer as the main structure of the encoding-decoding network for detection and output. In order to ensure that the image can be correctly input into the 3D UNet network, random cropping is used in the training process to obtain an input image of [128, 128, 64] size, which undergoes random change, rotation, upside down, and scale changes for image enhancement processing. Input to the 3D UNet network for network model training. As shown in Figure 4, the input image of the 3D UNet network uses the lung area image data output in step S23 multiplied by the lung CT image processed in step S12 (b1) and the lung area image data output in step S23 multiplied by the doctor's outline Gold standard image of lung leaf (b2); mixed loss function: Dice loss function and Focal are used during model training Loss function, where the Dice Loss function is used to measure the quality of the result in image segmentation, Focal The loss function is used to deal with category imbalance. In the test phase, a moving sliding window method can be used to obtain an image with a size of [128, 128, 64]. After the trained model is predicted, the original image can be restored to obtain 5 different lung lobe mask regions (b3).

Step S25: Perform morphological processing on the five lung lobes mask regions to obtain the final lung lobe segmentation results; in this embodiment, the step of performing morphological processing on the mask regions of the five lung lobes includes: removing five The area outside the mask area of the lung lobes, and the holes that fill the mask area of the five lung lobes, obtain the final lung lobe segmentation result.

Step S26 is to store the result of the lung lobes segmentation in the memory 11 or output and display it on the screen of the display 13. In this embodiment, the segmented images of different lung lobes are displayed on the screen of the display 13 for doctors to provide a more comprehensive reference in disease diagnosis and treatment.

The present invention also provides a computer-readable storage medium that stores a plurality of computer program instructions, and the computer program instructions are loaded by a processor of a computer device and execute the method of lung lobe segmentation based on the UNet network of the present invention. Various steps. Those skilled in the art can understand that all or part of the steps of the various methods in the foregoing embodiments can be completed by related program instructions. The program can be stored in a computer-readable storage medium. The storage medium may include: read-only memory, random access memory, Disk or CD, etc.

The lung lobe segmentation method, device and computer-readable storage medium based on UNet network of the present invention can normalize lung CT images, and use 2D UNet network to quickly and accurately extract lung regions from lung CT images, and remove After the influence of noise outside the lung area, the 3D UNet network is used to segment the lung lobes in the lung CT image to obtain five lung lobes mask regions, thereby efficiently controlling the accuracy and speed of lung lobes segmentation, improving the efficiency of segmentation and not being affected. Limited to differences in individual lung morphology. The invention realizes fast and accurate extraction of lung lobes through UNet network, locates the position of lung cancer, and provides medical guidance for the diagnosis and treatment of lung cancer for doctors.

The above are only preferred embodiments of the present invention, and do not limit the scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the description and drawings of the present invention, or directly or indirectly applied to other related technologies In the same way, all fields are included in the scope of patent protection of the present invention.

Industrial applicability

Compared with the prior art, the lung lobe segmentation method, device and computer-readable storage medium based on UNet network of the present invention can normalize lung CT images and use 2D UNet network to quickly obtain lung CT images. Accurately extract the lung area, remove the influence of noise outside the lung area, and then use the 3D UNet network to segment the lung lobes in the lung CT image to obtain five lung lobes mask areas, thereby efficiently controlling the accuracy and speed of lung lobes segmentation, and improving The efficiency of lung lobes segmentation is not limited to individual lung morphological differences. The invention realizes fast and accurate extraction of lung lobes through UNet network, locates the position of lung cancer, and provides medical guidance for the diagnosis and treatment of lung cancer for doctors.

Claims (10)

A lung lobe segmentation device based on UNet network, comprising a processor suitable for implementing various computer program instructions and a memory suitable for storing multiple computer program instructions, characterized in that the computer program instructions are loaded by the processor and executed as follows step: Obtain lung CT image data from image input equipment; Normalize the input lung CT image data; Use 2D UNet network to screen out the lung area and the lung area on the processed lung CT image data, and use the lung area as the lung area candidate area; Use the 3D UNet network to segment the candidate regions of the lung area into five lung lobe mask regions, and obtain the regions of the upper left lobe, lower left lobe, upper right lobe, middle right lobe and lower right lobe; Perform morphological processing on the five lung lobes mask regions to obtain the final lung lobe segmentation results; The results of lung lobes segmentation are stored in the memory, or output and displayed on the monitor screen. The lung lobe segmentation device based on UNet network according to claim 1, characterized in that: The step of normalizing the input lung CT image data includes the following steps: preprocessing the input lung CT image data, and limiting the lung CT image data to [-1000, 400] The interval is normalized to the interval [0,1] to exclude non-lung areas with higher brightness; The step of performing morphological processing on the mask areas of the five lung lobes respectively includes the following steps: removing the area outside the mask area of the five lung lobes, and filling the holes in the mask area of the five lung lobes. The lung lobe segmentation device based on the UNet network of claim 1, wherein the processed image data uses a 2D UNet network to filter out the lung area and the lung area, and the lung area is used as a lung area candidate The regional steps include the following steps: Both the lung CT image and the lung area gold standard image are scaled to the size of [256, 256]; Input the zoomed lung CT image and lung area gold standard image into the 2D UNet network to segment the lung area image including the lung area and the lung area; Re-scale the lung area image obtained by the 2D UNet output to the original image size, and expand each layer of lung area with a radius of 5 pixels; The bounding box of the lung area is extracted according to the size of the lung area, and the lung area candidate area is extracted from the bounding box. The lung lobe segmentation device based on the UNet network according to claim 1, wherein the step of segmenting five lung lobe mask regions using a 3D UNet network for the candidate lung area includes the following steps: Random cropping method is used for lung CT images to obtain a size of [128, 128, 64], and undergoes random change, rotation, upside down, and size change for image enhancement processing; Multiply the lung area image data by the processed lung CT image data and lung area image data by the lung lobe gold standard image data and input it into the 3D UNet network to segment the lung CT image; Restore the segmented lung lobe image to the original size to obtain 5 different lung lobe mask regions. A method for segmentation of lung lobes based on UNet network, applied to a computer device, is characterized in that the method for segmentation of lung lobes based on UNet network includes the following steps: Obtain lung CT image data from image input equipment; Normalize the input lung CT image data; Use 2D UNet network to screen out the lung area and the lung area on the processed lung CT image data, and use the lung area as the lung area candidate area; Use the 3D UNet network to segment the candidate regions of the lung area into five lung lobe mask regions, and obtain the regions of the upper left lobe, lower left lobe, upper right lobe, middle right lobe and lower right lobe; Perform morphological processing on the five lung lobes mask regions to obtain the final lung lobe segmentation results; The results of lung lobes segmentation are stored in the memory, or output and displayed on the monitor screen. The lung lobe segmentation method based on the UNet network according to claim 5, wherein the step of normalizing the input lung CT image data comprises the following steps: pre-processing the input lung CT image data Process and limit the lung CT image data to the interval [-1000, 400], and then normalize it to the interval [0, 1] to exclude non-lung areas with higher brightness. The lung lobe segmentation method based on the UNet network according to claim 5, wherein the step of performing morphological processing on the mask regions of the five lung lobes respectively comprises the following steps: removing the regions outside the mask regions of the five lung lobes , And fill the holes in the mask area of the five lung lobes. The lung lobe segmentation method based on the UNet network of claim 5, wherein the processed image data uses a 2D UNet network to filter out the lung area and the lung area, and the lung area is used as a lung area candidate The regional steps include the following steps: Both the lung CT image and the lung area gold standard image are scaled to the size of [256, 256]; Input the zoomed lung CT image and lung area gold standard image into the 2D UNet network to segment the lung area image including the lung area and the lung area; Re-scale the lung area image obtained by the 2D UNet output to the original image size, and expand each layer of lung area with a radius of 5 pixels; The bounding box of the lung area is extracted according to the size of the lung area, and the lung area candidate area is extracted from the bounding box. The method for segmentation of lung lobes based on the UNet network of claim 5, wherein the step of segmenting five lung lobes mask regions using a 3D UNet network for the candidate lung region includes the following steps: Random cropping method is used for lung CT images to obtain a size of [128, 128, 64], and undergoes random change, rotation, upside down, and size change for image enhancement processing; Multiply the lung area image data by the processed lung CT image data and lung area image data by the lung lobe gold standard image data and input it into the 3D UNet network to segment the lung CT image; Restore the segmented lung lobe image to the original size to obtain 5 different lung lobe mask regions. A computer-readable storage medium storing multiple computer program instructions, wherein the computer program instructions are loaded by a processor of a computer device and executed as described in any one of claims 5 to 9 The method steps of the lung lobe segmentation method based on UNet network.