Colon polyp segmentation based on transformer and uncertainty guidance

Kun Peng; Guimei Zhang; Li Li; Huiqun Liu

doi:10.1117/12.3029470

13 May 2024 Colon polyp segmentation based on transformer and uncertainty guidance

Kun Peng, Guimei Zhang, Li Li, Huiqun Liu

Proceedings Volume 13158, Seventh International Conference on Computer Graphics and Virtuality (ICCGV 2024); 131580D (2024) https://doi.org/10.1117/12.3029470
Event: Seventh International Conference on Computer Graphics and Virtuality (ICCGV24), 2024, Hangzhou, China

Abstract

The boundaries of colonoscopy-acquired images are often blurred due to reflections and low contrast, and existing methods for colon polyp segmentation fail to effectively represent global contextual information and long-range dependencies, resulting in sub-optimal accuracy in segmenting polyp. To address this problem, we propose a novel approach which introduces uncertainty-guided cross-entropy loss into a Transformer model to achieve precise segmentation of colon polyps. Regarding the boundary blurred, we incorporate an uncertainty estimation module into the decoding process. This module assigns lower weights to pixels with higher boundary uncertainty so as to mitigate the influence of erroneous pixels, and a boundary attention module is employed between encoding and decoding to guide the network to capture polyp edges more effectively, thereby improving its ability for precise boundary localization. To enhance the contextual modeling capabilities of the model, we employ a Pyramid Vision Transformer v2 (PVTv2) encoder to extract semantic information and capture long-range dependencies in the lesion regions. Furthermore, we utilize a feature refinement module to capture local detailed information. Additionally, a low-level feature enhancement module is applied to highlight the region of interest (ROI) of polyps, thereby facilitating improved discrimination between normal tissues and polyps. Extensive experiments conducted on five publicly datasets demonstrate the superior accuracy and generalization performance of the proposed model. Furthermore, with minor refinements, this model can be extended to other tumor segmentation tasks.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kun Peng, Guimei Zhang, Li Li, and Huiqun Liu "Colon polyp segmentation based on transformer and uncertainty guidance", Proc. SPIE 13158, Seventh International Conference on Computer Graphics and Virtuality (ICCGV 2024), 131580D (13 May 2024); https://doi.org/10.1117/12.3029470

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KEYWORDS

Polyps

Image segmentation

Data modeling

Transformers

Convolution

Feature extraction

Education and training

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