Image segmentation method and device and neural network model training method and device

Abstract

The invention provides an image segmentation method and device, a neural network model training method and device, a computer readable storage medium and electronic equipment. The image segmentation method comprises steps that a segmentation mask image of a to-be-segmented pathological image is acquired, and the segmentation mask image comprises boundary information of cell nucleuses in the to-be-segmented pathological image; a distance regression image of the to-be-segmented pathological image is obtained according to the segmentation mask image; according to the segmentation mask image and the distance regression image, the binary cell nucleus segmentation image of the to-be-segmented pathology image is acquired. The cell nucleus binary classification segmentation task can be performed by combining the cell nucleus boundary information and the distance information, the cell nucleus segmentation precision is improved, and small cell nucleuses, adhesion and overlapped cell nucleuses can be better segmented; in addition, the cell nucleuses from different organs are greatly different in appearance, form, shape, color and density, and the technical scheme provided by the embodiment ofthe invention can have good generalization ability.

Description

technical field [0001] The invention relates to the technical field of image segmentation, in particular to an image segmentation method and device, a neural network model training method and device, a computer-readable storage medium and electronic equipment. Background technique [0002] Pathological images contain a large amount of information about cell morphology and tissue structure, and are widely used in clinical practice and disease research. Since the morphology, distribution, nuclear-to-cytoplasmic ratio (the ratio of nucleus to cytoplasm) and structure of the nucleus are the basis for extracting, mining and interpreting subcellular information, they can provide important clues for clinical practice such as tumor diagnosis and prognosis. Therefore, nucleus segmentation is a further step. Fundamental and important tasks for studying and analyzing pathological images. [0003] Traditional methods for cell nucleus segmentation include mathematical morphological oper...

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Problems solved by technology

[0003] Traditional methods for cell nucleus segmentation include mathematical morphological operations, watershed transformation, active contour model, clustering, and graph-based methods, but these algorithms have limited segmentation effects, low precision, and are prone to over-segmentation and under-segmentation. Pathological images of different organs do not have good generalization ability

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