PolypSeg: An Efficient Context-Aware Network for Polyp Segmentation from Colonoscopy Videos

Zhong, Jiafu; Wang, Wei; Wu, Huisi; Wen, Zhenkun; Qin, Jing

doi:10.1007/978-3-030-59725-2_28

Cited by 26 publications

(10 citation statements)

References 16 publications

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“…In recent years, attention has also been widely used in the field of computer vision, especially for semantic segmentation which requires detailed edge information at the pixel level. Examples include PraNet [13], PolypSeg [35] and ABC-Net [14]. After adding different context modules, they all get good results in medical imaging segmentation.…”

Section: Related Workmentioning

confidence: 99%

HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS

Huang,

Wu,

Lin

2021

Preprint

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Section: Related Workmentioning

confidence: 99%

HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS

Huang,

Wu,

Lin

2021

Preprint

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“…Later, Akbari et al [33] introduced a modified FCN to improve the segmentation accuracy. Inspired by the vast success of UNet [34] in biomedical image segmentation, UNet++ [35] and ResUNet [36] were employed for polyp segmentation for improved performance. Furthermore, PolypSeg [37] , ACS [38] , ColonSegNet [39] , and SCR-Net [40] explore the effectiveness of UNet-enhanced architecture on adaptively learning semantic contexts.…”

Section: Image Polyp Segmentation (Ips)mentioning

confidence: 99%

Video Polyp Segmentation: A Deep Learning Perspective

Xiao

Chou

et al. 2022

Mach. Intell. Res.

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We present the first comprehensive video polyp segmentation (VPS) study in the deep learning era. Over the years, developments in VPS are not moving forward with ease due to the lack of a large-scale dataset with fine-grained segmentation annotations. To address this issue, we first introduce a high-quality frame-by-frame annotated VPS dataset, named SUN-SEG, which contains 158 690 colonoscopy video frames from the well-known SUN-database. We provide additional annotation covering diverse types, i.e., attribute, object mask, boundary, scribble, and polygon. Second, we design a simple but efficient baseline, named PNS+, which consists of a global encoder, a local encoder, and normalized self-attention (NS) blocks. The global and local encoders receive an anchor frame and multiple successive frames to extract long-term and short-term spatial-temporal representations, which are then progressively refined by two NS blocks. Extensive experiments show that PNS+ achieves the best performance and real-time inference speed (170 fps), making it a promising solution for the VPS task. Third, we extensively evaluate 13 representative polyp/object segmentation models on our SUN-SEG dataset and provide attribute-based comparisons. Finally, we discuss several open issues and suggest possible research directions for the VPS community. Our project and dataset are publicly available at https://github.com/GewelsJI/VPS.

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“…The literature also revealed that majority of the existing works evaluated their models on test sets, which were derived from the same datasets. [28][29][30][31] An exception to this trend was the recently published work of Jha et al 5 They performed inter-dataset evaluation to prove the generalizing capability of their proposed model. However, they performed one-fold cross validation for all their experiments.…”

Section: Related Workmentioning

confidence: 99%

Squeeze and multi‐context attention for polyp segmentation

Bhattacharya

Eggert²,

Betz³

et al. 2022

Int J Imaging Syst Tech

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Artificial Intelligence-based Computer Aided Diagnostics (AI-CADx) have been proposed to help physicians in reducing misdetection of polyps in colonoscopy examination. The heterogeneity of a polyp's appearance makes detection challenging for physicians and AI-CADx. Towards building better AI-CADx, we propose an attention module called Squeeze and Multi-Context Attention (SMCA) that re-calibrates a feature map by providing channel and spatial attention by taking into consideration highly activated features and context of the features at multiple receptive fields simultaneously. We test the effectiveness of SMCA by incorporating it into the encoder of five popular segmentation models. We use five public datasets and construct intra-dataset and inter-dataset test sets to evaluate the generalizing capability of models with SMCA. Our intra-dataset evaluation shows that U-Net with SMCA and without SMCA has a precision of 0.86 ± 0.01 and 0.76 ± 0.02 respectively on CVC-Clin-icDB. Our inter-dataset evaluation reveals that U-Net with SMCA and without SMCA has a precision of 0.62 ± 0.01 and 0.55 ± 0.09 respectively when trained on Kvasir-SEG and tested on CVC-ColonDB. Similar results are observed using other segmentation models and other public datasets. In conclusion, we demonstrate that incorporating SMCA into the segmentation models leads to an increase in generalizing capability of the segmentation models.

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PolypSeg: An Efficient Context-Aware Network for Polyp Segmentation from Colonoscopy Videos

Cited by 26 publications

References 16 publications

HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS

HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS

Video Polyp Segmentation: A Deep Learning Perspective

Squeeze and multi‐context attention for polyp segmentation

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