Two path gland segmentation algorithm of colon pathological image based on local semantic guidance

• Published in: IEEE journal of biomedical and health informatics Vol. 27; no. 4; pp. 1 - 8
• Main Authors: Ding, Songtao, Wang, Hongyu, Lu, Hu, Nappi, Michele, Wan, Shaohua
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adenocarcinoma; Algorithms; Cancer; Candidate region; Carcinogenesis; Carcinogens; Colon; Colon - diagnostic imaging; Colonic adenocarcinoma; Computer networks; Datasets; Feature extraction; Gland segmentation; Glands; Humans; Image processing; Image segmentation; Medical imaging; Metric space; Pathology; Receptive field; Search algorithms; Semantics; Semantics guided; Shape
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2022.3207874

Colonic adenocarcinoma is a disease severely endangering human life caused by mucosal epidermal carcinogenesis. The segmentation of potentially cancerous glands is the key in the detection and diagnosis of colonic adenocarcinoma. The appearance of cancerous tissue is different in gland segmentation in colon pathological images, and it is impossible to accurately segment the changes of glands from benign to malignant using a single network. Given these issues, a two-path gland segmentation algorithm of colon pathological image based on local semantic guidance is proposed in this paper. The improved candidate region search algorithm is adopted to expand the original image data set and generate sub-datasets sensitive to specific features. Then, the semantic feature-guided model is employed to extract the local adenocarcinoma features and acts on the backbone network together with context feature extraction based on the attention mechanism. In this way, a larger receptive field and more local feature information are obtained, the learning ability of the network to the morphological features of glands is enhanced, and the performance of automatic gland segmentation is finally improved. The algorithm is verified on Warwick Qu-Dataset. Compared with the current popular segmentation algorithms, our algorithm has good performance in Dice coefficient, F1 score, and Hausdorff distance on different types of test sets.