Dynamic spectrum-driven hierarchical learning network for polyp segmentation

Accurate automatic polyp segmentation in colonoscopy is crucial for the prompt prevention of colorectal cancer. However, the heterogeneous nature of polyps and differences in lighting and visibility conditions present significant challenges in achieving reliable and consistent segmentation across di...

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Veröffentlicht in:Medical image analysis Jg. 101; S. 103449
Hauptverfasser: Wang, Haolin, Wang, Kai-Ni, Hua, Jie, Tang, Yi, Chen, Yang, Zhou, Guang-Quan, Li, Shuo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier B.V 01.04.2025
Schlagworte:
Algorithms
Colonic Polyps - diagnostic imaging
Colonoscopy - methods
Dynamic Convolution
Frequency Learning
Humans
Image Interpretation, Computer-Assisted - methods
Machine Learning
Polyp Segmentation
Region-level Saliency Modeling
Dynamic Convolution
Polyp Segmentation
Region-level Saliency Modeling
Frequency Learning
41A10
65D05
65D17
41A05
ISSN:1361-8415, 1361-8423, 1361-8423
Online-Zugang:Volltext
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Zusammenfassung:Accurate automatic polyp segmentation in colonoscopy is crucial for the prompt prevention of colorectal cancer. However, the heterogeneous nature of polyps and differences in lighting and visibility conditions present significant challenges in achieving reliable and consistent segmentation across different cases. Therefore, this study proposes a novel dynamic spectrum-driven hierarchical learning model (DSHNet), the first to specifically leverage image frequency domain information to explore region-level salience differences among and within polyps for precise segmentation. A novel spectral decoupler is advanced to separate low-frequency and high-frequency components, leveraging their distinct characteristics to guide the model in learning valuable frequency features without bias through automatic masking. The low-frequency driven region-level saliency modeling then generates dynamic convolution kernels with individual frequency-aware features, which regulate region-level saliency modeling together with the supervision of the hierarchy of labels, thus enabling adaptation to polyp heterogeneous and illumination variation simultaneously. Meanwhile, the high-frequency attention module is designed to preserve the detailed information at the skip connections, which complements the focus on spatial features at various stages. Experimental results demonstrate that the proposed method outperforms other state-of-the-art polyp segmentation techniques, achieving robust and superior results on five diverse datasets. Codes are available at https://github.com/gardnerzhou/DSHNet. •Synergizing spectrum learning and dynamic hierarchical modeling for polyp segmentation.•Low-frequency driven region-level saliency modeling to adapt the heterogeneous images.•Frequency-aware kernel generation for adaptation to polyp variation.•Perceiving salient information under the regulation of a high-frequency attention module.
Bibliographie:ObjectType-Article-1
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ISSN:1361-8415
1361-8423
1361-8423
DOI:10.1016/j.media.2024.103449