A medical image segmentation method based on a one-way multi-task convolution neural network

Abstract

The invention discloses a medical image segmentation method based on a one-way multi-task convolution neural network. Firstly, the segmentation task is divided into three different but related sub-tasks by using the network. A first sub-task carries out rough segmentation on that image to determine a rough area of the whole organization; a second subtask dilates and fine-segments the area to getthe exact tissue type and exact position of the whole tissue. Subtask 3 uses refined segmentation to scan voxels in the intact tissue and determine the location of the enhanced tissue. Then the threesub-tasks are trained in stages with course learning strategy from easy to difficult to realize data and parameter sharing among sub-tasks. Finally, the post-processing method is used to improve the classification error problem and output the final segmentation results. The invention synthesizes the associated sub-tasks into a single network for simultaneous training, improves the shortcomings ofcascade model training sub-tasks one by one and better solves the category imbalance problem, realizes the segmentation of the image from coarse to fine by one-way operation, and improves the segmentation effect while reducing the complexity of the system.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a medical image segmentation method based on a single-pass multi-task convolutional neural network. Background technique [0002] With the development of science and technology, modern medical treatment such as computed tomography (CT), magnetic resonance imaging (Magnetic Resonance Imaging, MRI), positron emission tomography (PET) and electronic endoscopy (Endoscopy) Imaging technology and equipment have been rapidly developed and popularized. After these technologies and equipment are put into use, we can easily obtain a large amount of medical imaging data, and process, observe, and analyze the patient's diseased tissues or organs through computers and other equipment, so that medical personnel can understand the condition more clearly and greatly improve the quality of life. Shorten the diagnosis time and improve the diagnostic accuracy. It is worth noting that medical image...

AI Technical Summary

Problems solved by technology

However, the model structure used in deep learning is often very complex, and an extremely large amount of parameters will be generated during the training process, which requires very high computer hardware configuration.

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