Papers › Video Polyp Segmentation: A Deep Learning Perspective

Video Polyp Segmentation: A Deep Learning Perspective

27 Mar 2022arXiv:2203.14291archive 2025-07-28

Ge-Peng Ji, Guobao Xiao, Yu-Cheng Chou, Deng-Ping Fan, Kai Zhao, Geng Chen, Luc van Gool

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 large-scale 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 frames from the well-known SUN-database. We provide additional annotations with diverse types, i.e., attribute, object mask, boundary, scribble, and polygon. Second, we design a simple but efficient baseline, dubbed PNS+, consisting 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 updated by two NS blocks. Extensive experiments show that PNS+ achieves the best performance and real-time inference speed (170fps), 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.

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DengPingFan/PraNet officialmentioned in papermentioned on GitHubpytorch report
GewelsJI/PNS-Net officialmentioned in papermentioned on GitHubpytorchApache-2.0 report
gewelsji/vps officialmentioned in papermentioned on GitHubpytorchApache-2.0 report
DengPingFan/Inf-Net mentioned on GitHubpytorch report

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adjust_lr GewelsJI/PNS-Net/utils/utils.py official repository unverified Apache-2.0 (permissive) · 1a4b7ff9d3c48bd7 · report
evaluator gewelsji/vps/eval/vps_evaluator.py official repository unverified Apache-2.0 (permissive) · 88d6754357faca2d · report
res2net101_v1b GewelsJI/PNS-Net/lib/Res2Net_v1b.py official repository unverified Apache-2.0 (permissive) · e970716f1c7c523e · report
res2net101_v1b gewelsji/vps/lib/module/Res2Net_v1b.py official repository unverified Apache-2.0 (permissive) · 0c631c0d1156ff0d · report
res2net50_v1b GewelsJI/PNS-Net/lib/Res2Net_v1b.py official repository unverified Apache-2.0 (permissive) · 10fe097f4b54b650 · report
res2net50_v1b gewelsji/vps/lib/module/Res2Net_v1b.py official repository unverified Apache-2.0 (permissive) · dbf78049da52da17 · report
res2net50_v1b_26w_4s GewelsJI/PNS-Net/lib/Res2Net_v1b.py official repository unverified Apache-2.0 (permissive) · fe08d7c36a1419e8 · report
res2net50_v1b_26w_4s gewelsji/vps/lib/module/Res2Net_v1b.py official repository unverified Apache-2.0 (permissive) · cf6cf296d2002189 · report

Tasks

AttributeDeep LearningSegmentationSemantic SegmentationVideo Object SegmentationVideo Object TrackingVideo Polyp Segmentation

Datasets

Introduced by this paper, per the archive.

SUN-SEG-Easy (Unseen)SUN-SEG-Hard (Unseen)

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Video Polyp Segmentation SUN-SEG-Easy (Unseen) PNS+ Dice 0.756 #4 of 18 Archive leaderboard report
Video Polyp Segmentation SUN-SEG-Easy (Unseen) PNS+ S measure 0.806 #4 of 18 Archive leaderboard report
Video Polyp Segmentation SUN-SEG-Easy (Unseen) PNS+ Sensitivity 0.630 #4 of 18 Archive leaderboard report
Video Polyp Segmentation SUN-SEG-Easy (Unseen) PNS+ mean E-measure 0.798 #4 of 18 Archive leaderboard report
Video Polyp Segmentation SUN-SEG-Easy (Unseen) PNS+ mean F-measure 0.730 #4 of 18 Archive leaderboard report
Video Polyp Segmentation SUN-SEG-Easy (Unseen) PNS+ weighted F-measure 0.676 #4 of 18 Archive leaderboard report
Video Polyp Segmentation SUN-SEG-Hard (Unseen) PNS+ Dice 0.737 #5 of 18 Archive leaderboard report
Video Polyp Segmentation SUN-SEG-Hard (Unseen) PNS+ S-Measure 0.797 #5 of 18 Archive leaderboard report
Video Polyp Segmentation SUN-SEG-Hard (Unseen) PNS+ Sensitivity 0.623 #5 of 18 Archive leaderboard report
Video Polyp Segmentation SUN-SEG-Hard (Unseen) PNS+ mean E-measure 0.793 #5 of 18 Archive leaderboard report
Video Polyp Segmentation SUN-SEG-Hard (Unseen) PNS+ mean F-measure 0.709 #5 of 18 Archive leaderboard report
Video Polyp Segmentation SUN-SEG-Hard (Unseen) PNS+ weighted F-measure 0.653 #5 of 18 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

SPEED

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