Automatic segmentation method for lesion area in digital pathological full slice image

Abstract

The invention discloses an automatic segmentation method for a lesion area in a digital pathological full slice image. The method comprises steps that an offline training phase and an online prediction phase are included, for the training phase, a digital pathological full slice image in a full slice database with the labeled lesion area is sampled to obtain a large number of labeled image blocks,and a classifier is trained; for the prediction stage, an image block matrix is obtained through uniformly sampling an unknown digital pathological full slice image, multiple probability matrixes areobtained by the classifier, the probability matrixes are processed and binarized, and the obtained contour is mapped back to the unknown digital pathological full slice image to obtain the segmentation result. The method is advantaged in that automatic segmentation of the lesion area of the unknown full slice can be achieved simply through the full-slice database with the labeled lesion area, notonly lesion categories of all the possible lesion areas in the full slice are shown, but also the contour and position distribution of each lesion area are further accurately displayed, and comprehensive, intuitive and accurate prediction and analysis of the unknown full-slice lesion status are realized.

Description

technical field [0001] The present invention relates to the technical field of digital image processing and machine learning, and more specifically relates to a method for automatically segmenting lesion regions of digital pathological whole slice images. Background technique [0002] In recent years, with the rapid development of pathology and computer technology, the number of digital pathology whole slide images has grown rapidly. Digital pathological whole slide images are large-size, high-resolution digital images obtained by scanning and collecting traditional glass pathological slides through automatic microscopes or optical magnification systems, and are an important basis for pathologists in diagnosis. [0003] Because the key information such as cell structure and distribution required for the diagnosis of digital pathology full slice images is stored under high-power microscopes, and under this condition, the size of digital pathology full slice images is huge, up...

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Problems solved by technology

[0003] Because the key information such as cell structure and distribution required for the diagnosis of digital pathology full slice images is stored under high-power microscopes, and under this condition, the size of digital pathology full slice images is huge, up to 100,000×100,000 pixels, and full slices cannot be realized with current computer capabilities. overall treatment

In the current application of path

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