@inproceedings{79fb5f1786a245b99d2a9cbca51229b9,
title = "Triple-Branch Deep Network for Polyp Image Segmentation",
abstract = "Medical image segmentation is essential for accurately extracting tissue structures or pathological regions from medical images.However, medical image segmentation methods are often influenced by factors such as image noise and irregular shapes, making precise segmentation challenging.To tackle these challenges, this paper proposes a triple-branch medical image segmentation network (TB-Net) that incorporates implicit boundary priors.The boundary map, acquired through a boundary detection algorithm, is used to restrict the results of the boundary branch.Extensive experiments indicate that TB-Net achieves state-of-the-art performance on publicly available polyp datasets.",
keywords = "boundary prior, medical image segmentation, polyp segmentation",
author = "Muwei Jian and Yanjie Zhong and Lam, \{Kin Man\}",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; 2025 International Workshop on Advanced Imaging Technology, IWAIT 2025 ; Conference date: 06-01-2025 Through 08-01-2025",
year = "2025",
month = feb,
doi = "10.1117/12.3057381",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
pages = "1--6",
editor = "Masayuki Nakajima and Chuan-Yu Chang and Chia-Hung Yeh and Jae-Gon Kim and Kemao Qian and Lau, \{Phooi Yee\}",
booktitle = "International Workshop on Advanced Imaging Technology, IWAIT 2025",
address = "United States",
}