Method and system for medical image automatic segmentation, apparatus and storage medium
A medical image and automatic segmentation technology, applied in the application field of computer analysis technology, can solve the problems of increasing segmentation difficulty, lack of universality and robustness, and influence of image data, reducing information processing capacity, improving classification performance, The effect of accurate segmentation
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Example Embodiment
[0048] Example 1
[0049] figure 1 This is a schematic flow chart of the automatic segmentation method for medical images provided by Embodiment 1 of the present invention. The execution subject of the automatic segmentation method provided by the embodiment of the present invention may be the automatic segmentation system provided by the embodiment of the present invention, which can be integrated in a mobile terminal. Devices (for example, smart phones, tablet computers, notebooks, etc.) can also be integrated in a server, and the automatic segmentation system can be implemented by hardware or software. The automatic segmentation method provided by the embodiment of the present invention is particularly suitable for the case of computer-aided diagnosis of cardiac images based on nuclear magnetic images, which will be described below in conjunction with the embodiments.
[0050] Such as figure 1 As shown, the automatic segmentation method specifically includes:
[0051] S101, using...
Example Embodiment
[0061] Example 2
[0062] figure 2 This is a schematic structural diagram of an automatic segmentation system for medical images provided by Embodiment 2 of the present invention. The system can be integrated in a mobile terminal device (for example, a smart phone, a tablet computer, a notebook, etc.) or a server. The positioning device can Implemented by hardware or software.
[0063] Such as figure 2 As shown, the system specifically includes a saliency map generation module 201, a training module 202, an initial segmentation module 203, a contour construction and optimization module 204, and a contour generation module 205;
[0064] The saliency map generation module 201 adopts the visual attention model to obtain the saliency map of the medical image to be trained;
[0065] The training module 202 is used to input the saliency map of the medical image to be trained into the deep learning neural network, so as to train the parameters of the deep learning neural network;
[0066] T...
Example Embodiment
[0150] Example 3
[0151] Picture 11 This is a schematic structural diagram of a device provided in Embodiment 3 of the present invention, and the device can be used to implement the automatic segmentation method of medical images according to an embodiment of the present invention.
[0152] in Picture 11 , A central processing unit (CPU) 601 executes various processes in accordance with a program stored in a read only memory (ROM) 602 or a program loaded from the storage section 608 to a random access memory (RAM) 603. In the RAM 603, data required when the CPU 601 performs various processing and the like is also stored as necessary. The CPU 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The input / output interface 605 is also connected to the bus 604.
[0153] The following components are also connected to the input / output interface 605: input part 606 (including keyboard, mouse, etc.), output part 607 (including display, such as cathode ray tube (C...
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