Automatic ROI (Regions of Interest) detection method of digital pathologic full slice image

Abstract

The invention discloses an automatic ROI detection method of a digital pathologic full slice image. The method is applied to a digital pathologic full slice image database, and comprises an offline training stage and an online detection stage. A doctor observes full slices to set an ROI viewport in each full slice in a standard extraction database, the ROI viewpoints are spliced to obtain a complete ROI, an overlapped slide window method is used to extract a rectangular area from the ROI, the rectangular area serves as a positive sample, a rectangular area collected from a non ROI serves as a negative sample, and the samples are used to train a classifier; and the classifier is used to determine a rectangular area extracted from unknown full slices and return a result. According to the invention, rectangular area samples can be obtained by only recording a slice observing operation of the doctor, the sample obtaining difficulty can be greatly reduced on the premise that the correctness is ensured, automatic detection for full slice ROI is easy to realize, time and labor are saved, and detection is rapid and accurate.

Description

technical field [0001] The invention relates to the fields of digital image processing and machine learning, in particular to an automatic detection method for a region of interest in a digital pathological full slice image. Background technique [0002] Digital pathological full slide image (hereinafter referred to as full slide) is a large-size, high-resolution digital image obtained by scanning and collecting traditional glass pathological slides through a fully automatic microscope or an optical magnification system. It is an important basis for pathologists in diagnosis. In recent years, with the rapid development of pathology and computer technology, the number of whole slides has increased rapidly. Automatic detection of regions of interest in unknown full slices through machine learning through a large number of known full slices is of great reference value for rapid diagnosis by pathologists and automatic computer diagnosis. [0003] In this process, the acquisitio...

AI Technical Summary

Problems solved by technology

For this link, domestic and foreign scholars mainly adopt the method of asking pathologists to mark all the regions of interest in a large number of full slices, using the marked regions as positive samples and the unmarked regions as negative samples for learning. This method has accurate results, but requires It takes a lot of manpower and time for experts, which cannot be realized in large-scale databases

Images