A lymph node detection method for improving a SegNet segmentation network

Abstract

The invention discloses a lymph node detection method based on an improved SegNet segmentation network. The method comprises the following steps: dividing a lymph node image data set into a training set and a test set; Constructing a SegNet segmentation network based on a cavity convolution operation; Training a SegNet segmentation network by using the training set, minimizing a sine and cosine cross entropy loss function as a network optimization target function, and optimizing the SegNet segmentation network; And identifying and segmenting the lymph nodes in the lymph image to be identifiedby using the trained SegNet segmentation network. According to the method, the characteristics are extracted by using hole convolution, the receptive field area is increased under the condition that the additional calculation amount is not increased, the loss of down-sampling information is avoided, and the problem that the resolution of the sampled image is reduced is solved. And through the sineand cosine cross entropy loss function, a weight smaller than that of the cross entropy loss function is given to a sample with a small prediction error, and the problem of unbalanced training of positive and negative samples is solved. And a Markov random field is used to carry out post-processing on the segmentation result, thereby realizing further refinement of the edge part of the segmentation object.

Description

technical field [0001] The invention belongs to the technical field of image segmentation, and more particularly relates to a lymph node detection method based on an improved SegNet segmentation network. Background technique [0002] Traditional medical image segmentation techniques can be divided into three categories: (1) segmentation based entirely on images, where all the information required for segmentation comes from the image itself; (2) methods based on target models, which add the required segmentation The prior information of the object (eg, the shape information of the object). Commonly, there are segmentation methods based on graphs (Atlas); (3) hybrid methods, first use image-based information for preliminary segmentation, and then achieve further segmentation of the target based on prior constraint information. The semantic segmentation network based on deep learning technology belongs to the first category of segmentation technology, that is, the information...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the defects of the existing technology, the purpose of the present invention is to solve the technical problem that the existing technology does not fully consider the imbalance of positive and negative samples in the training network, and fails to segment the target from the perspective of multi-scale and multi-resolution

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