A Glandular Cell Segmentation Method Based on Multi-level Feature Fusion Network

Abstract

The invention discloses a glandular cell segmentation method based on a multi-level feature fusion network, which belongs to the technical field of medical image processing. The glandular cell segmentation network model provided by the present invention includes an encoder and a decoder. In the encoder stage, the feature maps at the input end are down-sampled to generate feature maps of different scales, and then they are combined with the maximum pooling generated in the encoder. The feature maps of corresponding proportions are spliced ​​to realize multi-level feature input and strengthen the propagation of image features; The feature maps of corresponding sizes are spliced, and the shallow image features are combined again to compensate for the loss of pixel positions, reduce the error in predicting and locating the pixel positions of the feature maps during the decoding process, and achieve efficient glandular cell image segmentation tasks.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, and in particular relates to a segmentation method for glandular cell images. Background technique [0002] A normal gland is composed of a tubular structure with a luminal area and an epithelial nucleus surrounding the cytoplasm. Malignancies arising from glandular epithelial cells are called adenocarcinomas. Conventional treatment plans often depend on the grade and stage of the adenocarcinoma. Annotating and segmenting gland morphology in histopathological images is an important step and means for medical experts to judge the grading of cancers such as colon, breast and prostate. This work is very important for the treatment of patients' conditions, because accurate gland segmentation helps targeted and personalized treatment design, thereby improving the cure rate of patients. However, manual annotation and segmentation of glandular cells by medical experts requires a high ...

AI Technical Summary

Problems solved by technology

[0004] (1) The shape of benign and malignant glands is very different. The benign glands generally have a circular structure, while the malignant glands have an irregular structure. Larger, which increases the difficulty of accurately delineating individual glandular cells from tissue;

[0005] (2) The gap between adjacent gland cells is narrow, and a higher resolution is required for the division of the boundaries of each gland cell, which increases the difficulty of accurately dividing each gland cell;

Regardless of the aspect, it is a great challenge to achieve the desired effect.

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