Boundary-aware feature and prediction refinement for polyp segmentation

Polyp segmentation from colonoscopy videos is an essential task in medical image processing for detecting early cancer. However, segmenting a precise boundary is still challenging, even with powerful deep neural networks. We consider the difficulty can be caused by: (1) the ambiguity boundary and (2...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Jg. 11; H. 4; S. 1187 - 1196
Hauptverfasser: Qiu, Jie, Hayashi, Yuichiro, Oda, Masahiro, Kitasaka, Takayuki, Mori, Kensaku
Format: Journal Article
Sprache:Englisch
Japanisch
Veröffentlicht: Taylor & Francis 04.07.2023
Informa UK Limited
Schlagworte:
boundary-aware
consistency learning
Polyp segmentation
ISSN:2168-1163, 2168-1171
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Polyp segmentation from colonoscopy videos is an essential task in medical image processing for detecting early cancer. However, segmenting a precise boundary is still challenging, even with powerful deep neural networks. We consider the difficulty can be caused by: (1) the ambiguity boundary and (2) some complicated shape makes polyps hard to segment. To address these problems, we propose the Boundary-aware Feature and Prediction Refinement framework (BaFPR) for polyp segmentation. Specifically, we design a segmentation decoder for representation learning with boundary prior and propose a novel consistency loss to learn clues from the polar coordinate. The decoder mainly consists of a boundary prior module (BPM) and a bi-directional fusion module (BiFM). BPM is designed to learn the boundary prior, while BiFM learns to fuse representations of BPM and multi-scale representations from an encoder. To handle these complicated shapes of polyps, we maintain an extra segmentation network that learns with polar transformations of data to provide extra clues for the main segmentation network by our proposed consistency loss. We evaluated BaFPR with five challenging datasets for polyp segmentation and the results showed that our proposal consistently improves the segmentation performance of polyps. Code available at: https://github.com/MoriLabNU/BaFPR .
ISSN:2168-1163
2168-1171
DOI:10.1080/21681163.2022.2155579