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PolypNextLSTM: a lightweight and fast polyp video segmentation network using ConvNext and ConvLSTM
Citation Link: https://doi.org/10.15480/882.13576
Publikationstyp
Journal Article
Date Issued
2024-08-08
Sprache
English
Author(s)
TORE-DOI
Volume
19
Issue
10
Start Page
2111
End Page
2119
Citation
International Journal of Computer Assisted Radiology and Surgery: 19 (10): 2111-2119 (2024)
Publisher DOI
Scopus ID
Publisher
Springer
Purpose: Commonly employed in polyp segmentation, single-image UNet architectures lack the temporal insight clinicians gain from video data in diagnosing polyps. To mirror clinical practices more faithfully, our proposed solution, PolypNextLSTM, leverages video-based deep learning, harnessing temporal information for superior segmentation performance with least parameter overhead, making it possibly suitable for edge devices. Methods: PolypNextLSTM employs a UNet-like structure with ConvNext-Tiny as its backbone, strategically omitting the last two layers to reduce parameter overhead. Our temporal fusion module, a Convolutional Long Short Term Memory (ConvLSTM), effectively exploits temporal features. Our primary novelty lies in PolypNextLSTM, which stands out as the leanest in parameters and the fastest model, surpassing the performance of five state-of-the-art image and video-based deep learning models. The evaluation of the SUN-SEG dataset spans easy-to-detect and hard-to-detect polyp scenarios, along with videos containing challenging artefacts like fast motion and occlusion. Results: Comparison against 5 image-based and 5 video-based models demonstrates PolypNextLSTM’s superiority, achieving a Dice score of 0.7898 on the hard-to-detect polyp test set, surpassing image-based PraNet (0.7519) and video-based PNS+ (0.7486). Notably, our model excels in videos featuring complex artefacts such as ghosting and occlusion. Conclusion: PolypNextLSTM, integrating pruned ConvNext-Tiny with ConvLSTM for temporal fusion, not only exhibits superior segmentation performance but also maintains the highest frames per speed among evaluated models. Code can be found here: https://github.com/mtec-tuhh/PolypNextLSTM.
Subjects
CNN
Polyp
Segmentation
Video
MLE@TUHH
DDC Class
004: Computer Sciences
610: Medicine, Health
519: Applied Mathematics, Probabilities
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s11548-024-03244-6.pdf
Type
Main Article
Size
1.36 MB
Format
Adobe PDF