Unveiling camouflaged and partially occluded colorectal polyps : Introducing CPSNet for accurate colon polyp segmentation
Copyright © 2024 Elsevier Ltd. All rights reserved..
BACKGROUND: Segmenting colorectal polyps presents a significant challenge due to the diverse variations in their size, shape, texture, and intricate backgrounds. Particularly demanding are the so-called "camouflaged" polyps, which are partially concealed by surrounding tissues or fluids, adding complexity to their detection.
METHODS: We present CPSNet, an innovative model designed for camouflaged polyp segmentation. CPSNet incorporates three key modules: the Deep Multi-Scale-Feature Fusion Module, the Camouflaged Object Detection Module, and the Multi-Scale Feature Enhancement Module. These modules work collaboratively to improve the segmentation process, enhancing both robustness and accuracy.
RESULTS: Our experiments confirm the effectiveness of CPSNet. When compared to state-of-the-art methods in colon polyp segmentation, CPSNet consistently outperforms the competition. Particularly noteworthy is its performance on the ETIS-LaribPolypDB dataset, where CPSNet achieved a remarkable 2.3% increase in the Dice coefficient compared to the Polyp-PVT model.
CONCLUSION: In summary, CPSNet marks a significant advancement in the field of colorectal polyp segmentation. Its innovative approach, encompassing multi-scale feature fusion, camouflaged object detection, and feature enhancement, holds considerable promise for clinical applications.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:171 |
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| Enthalten in: |
Computers in biology and medicine - 171(2024) vom: 21. März, Seite 108186 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Huafeng [VerfasserIn] |
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| Links: |
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| Themen: |
Camouflaged polyps |
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| Anmerkungen: |
Date Completed 21.03.2024 Date Revised 21.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.compbiomed.2024.108186 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 245 | 1 | 0 | |a Unveiling camouflaged and partially occluded colorectal polyps |b Introducing CPSNet for accurate colon polyp segmentation |
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| 500 | |a Date Revised 21.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024 Elsevier Ltd. All rights reserved. | ||
| 520 | |a BACKGROUND: Segmenting colorectal polyps presents a significant challenge due to the diverse variations in their size, shape, texture, and intricate backgrounds. Particularly demanding are the so-called "camouflaged" polyps, which are partially concealed by surrounding tissues or fluids, adding complexity to their detection | ||
| 520 | |a METHODS: We present CPSNet, an innovative model designed for camouflaged polyp segmentation. CPSNet incorporates three key modules: the Deep Multi-Scale-Feature Fusion Module, the Camouflaged Object Detection Module, and the Multi-Scale Feature Enhancement Module. These modules work collaboratively to improve the segmentation process, enhancing both robustness and accuracy | ||
| 520 | |a RESULTS: Our experiments confirm the effectiveness of CPSNet. When compared to state-of-the-art methods in colon polyp segmentation, CPSNet consistently outperforms the competition. Particularly noteworthy is its performance on the ETIS-LaribPolypDB dataset, where CPSNet achieved a remarkable 2.3% increase in the Dice coefficient compared to the Polyp-PVT model | ||
| 520 | |a CONCLUSION: In summary, CPSNet marks a significant advancement in the field of colorectal polyp segmentation. Its innovative approach, encompassing multi-scale feature fusion, camouflaged object detection, and feature enhancement, holds considerable promise for clinical applications | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Camouflaged polyps | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Feature enhancement | |
| 650 | 4 | |a Feature fusion | |
| 650 | 4 | |a Segmentation | |
| 700 | 1 | |a Hu, Tianyu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Yanan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Haodu |e verfasserin |4 aut | |
| 700 | 1 | |a Qi, Yong |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Longzhen |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Jianhua |e verfasserin |4 aut | |
| 700 | 1 | |a Du, Minghua |e verfasserin |4 aut | |
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