Multiple organ segmentation method based on deep convolutional neural network and regional competition model

Abstract

The invention relates to medical image processing and aims to provide a multiple organ segmentation method based on a deep convolutional neural network and a regional competition model. The multiple organ segmentation method based on the deep convolutional neural network and the regional competition model comprises processes of training a three-dimensional convolutional neural network; using the trained three-dimensional convolutional neural network to learn prior probability images of liver, spleen, kidney and background in CTA volume data; determining an initial segmentation region of each tissue according to the prior probability image of each tissue; determining the probability of each pixel point belonging to each of the four tissues in the image; establishing a multiple region segmentation model based on regional competition; solving the model using the convex optimization method; and performing post-processing to obtain the contour of each organ. The invention uses the convolutional neural network to automatically and rapidly detect positions of liver, spleen and kidney at the belly, thereby obtaining the prior probability image of each organ is obtained. Then the invention uses the regional competition model, so that the contours of liver, spleen and kidney can be accurately segmented.

Description

Technical field [0001] The invention relates to the field of medical image processing, in particular to a multi-organ segmentation method based on a deep convolutional neural network and a regional competition model. Background technique [0002] The segmentation of abdominal organs has important research significance and clinical value. In clinical practice, doctors often use CT machines, that is, computed tomography scanners, to obtain a series of flat gray-scale tomographic images of the human abdomen, and to judge the size, position, and relationship of various organs by continuously viewing these images. Fast and automatic segmentation of organs from CT images is the first step in visualization. More important than visualization is that determining the location and area of ​​the organ of interest is of great significance in radiotherapy surgery. Only according to the spatial geometry and volume of the organs can an accurate radiotherapy plan and radiotherapy dose be formul...

AI Technical Summary

Problems solved by technology

The latest algorithm uses a convolutional neural network to detect multiple organs in the abdomen, but does not get accurate segmentation results

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