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Prostate transrectal ultrasonic image segmentation method based on deep convolutional neural network

A convolutional neural network and image segmentation technology, which is applied in the field of medical image processing and deep learning, can solve problems such as unsatisfactory segmentation results, and achieve the effect of avoiding image processing, increasing semantic information, and restoring details

Pending Publication Date: 2020-05-01
TIANJIN POLYTECHNIC UNIV
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Problems solved by technology

This approach is not ideal for segmenting results without borders

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  • Prostate transrectal ultrasonic image segmentation method based on deep convolutional neural network
  • Prostate transrectal ultrasonic image segmentation method based on deep convolutional neural network
  • Prostate transrectal ultrasonic image segmentation method based on deep convolutional neural network

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Embodiment Construction

[0021] The present invention will be further described in detail below in combination with specific embodiments.

[0022] figure 1 A flow chart of the prostate TRUS image segmentation method based on deep convolutional neural network according to the present invention is given. Such as figure 1 Shown, according to the prostate TRUS image segmentation method based on depth convolutional neural network of the present invention comprises:

[0023] Step 1: Collect prostate TRUS images;

[0024] Step 2: Use resnet-101-based convolutional neural network to extract multi-scale semantic information by applying an encoder composed of expanded spatial pyramid pooling;

[0025] Step 3: Apply 1×1 convolution to reduce the number of channels;

[0026] Step 4: Use 4 times upsampling and feature fusion to extract multi-level features to form super hybrid features to refine the prostate boundary segmentation results;

[0027] Step 5: apply a multi-level upsampling decoder to restore the ...

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Abstract

The invention relates to a prostate transrectal ultrasound (TRUS) image segmentation method based on a convolutional neural network. The method comprises the following steps: 1) extracting multi-scaleconvolution features in a prostate TRUS image by using an extended spatial pyramid pooling (DSPP) module with global feature coding semantic information; 2) in order to process the missing boundary in the segmented prostate shadow region, proposing a super-hybrid feature (SHF) capable of mixing low-level features and high-level features; and 3) a novel network composed of an encoder and a decoderis provided. The result shows that the method can achieve the precise segmentation of the prostate TRUS image, improves the segmentation accuracy and speed compared with the prior art, and successfully solves problems that the manual segmentation efficiency of the prostate TRUS image is low, the accuracy is low, and excessive manpower resources are consumed.

Description

technical field [0001] The invention relates to a prostate TRUS image segmentation method based on a convolutional neural network, which is superior to the prior art in terms of segmentation efficiency, robustness and accuracy, has good segmentation performance, and belongs to the field of medical image processing and deep learning. Background technique [0002] Prostate cancer is one of the greatest threats to men's health around the world. According to the American Cancer Society, there were approximately 180,890 new cases and 26,120 deaths from prostate cancer in 2016. Due to the lack of radiation, low cost and real-time requirement, TRUS has become the main imaging technique for the diagnosis and treatment of prostate cancer. Currently, most modern clinical applications rely on TRUS images, where prostate segmentation is usually obtained manually by professional physicians. [0003] Clinical manual segmentation of the prostate is usually performed manually by experts, ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G06N3/04
CPCG06T7/0012G06T7/11G06T2207/10132G06T2207/30081G06N3/045
Inventor 耿磊汪兆明肖志涛张芳吴骏刘彦北王雯
Owner TIANJIN POLYTECHNIC UNIV
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