BFT‐Net: A transformer‐based boundary feedback network for kidney tumour segmentation

Autor: Tianyu Zheng, Chao Xu, Zhengping Li, Chao Nie, Rubin Xu, Minpeng Jiang, Leilei Li
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IET Communications, Vol 18, Iss 16, Pp 966-977 (2024)
Druh dokumentu: article
ISSN: 1751-8636
1751-8628
DOI: 10.1049/cmu2.12802
Popis: Abstract Kidney tumours are among the top ten most common tumours, the automatic segmentation of medical images can help locate tumour locations. However, the segmentation of kidney tumour images still faces several challenges: firstly, there is a lack of renal tumour endoscopic datasets and no segmentation techniques for renal tumour endoscopic images; secondly, the intra‐class inconsistency of tumours caused by variations in size, location, and shape of renal tumours; thirdly, difficulty in semantic fusion during decoding; and finally, the issue of boundary blurring in the localization of lesions. To address the aforementioned issues, a new dataset called Re‐TMRS is proposed, and for this dataset, the transformer‐based boundary feedback network for kidney tumour segmentation (BFT‐Net) is proposed. This network incorporates an adaptive context extract module (ACE) to emphasize local contextual information, reduces the semantic gap through the mixed feature capture module (MFC), and ultimately improves boundary extraction capability through end‐to‐end optimization learning in the boundary assist module (BA). Through numerous experiments, it is demonstrated that the proposed model exhibits excellent segmentation ability and generalization performance. The mDice and mIoU on the Re‐TMRS dataset reach 91.1% and 91.8%, respectively.
Databáze: Directory of Open Access Journals
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