Method for automatically segmenting whole prostate gland based on deep learning convolutional neural network

Abstract

The invention relates to a method for automatically segmenting a whole prostate gland based on a deep learning convolutional neural network. Comprising the steps of obtaining a prostate MR image of a sample; inputting the prostate MR image into a prostate whole gland segmentation network to obtain a prostate whole gland image, the prostate whole gland segmentation network comprising a prostate tip segmentation network, a prostate middle segmentation network and a prostate bottom segmentation network, the prostate tip segmentation network is used for segmenting a prostate MR image to obtain a prostate tip image, the prostate middle segmentation network is used for segmenting the prostate MR image to obtain a prostate middle image, and the prostate bottom segmentation network is used for segmenting the prostate MR image to obtain a prostate bottom image. And combining the prostate tip image, the prostate middle image and the prostate bottom image to obtain a prostate whole gland segmentation image. According to the prostate anatomical structure characteristics, the better prostate whole gland segmentation method is provided.

Description

technical field [0001] The present invention relates to the field of intelligent medical technology, and more specifically, to a method, device, system and storage medium for automatically segmenting whole prostate glands based on a deep learning convolutional neural network. Background technique [0002] Accurate prostate whole-gland segmentation on MR images plays an important role in the management of prostate cancer and even benign prostatic hyperplasia. For malignant prostate cancer, accurate and efficient prostate contour recognition is crucial for cancer staging and radiotherapy planning. Advances in deep learning have yielded outstanding results in medical image analysis, and the recently proposed U-Net architecture has been successfully applied to prostate segmentation. However, most of these studies are based on public datasets or single images, and some segmentation models have poor generalization, which is largely due to the lack of in-depth consideration of the ...

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Problems solved by technology

However, most of these studies are based on public datasets or single images, and some segmentation models have poor generalization, which is largely due to the lack of in-depth consideration of the resolution of various images in prostate MR images. Anatomical structure to solve these clinical imaging realities

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