Video Polyp Segmentation: A Deep Learning Perspective

27 Mar 2022  ·  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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Datasets


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Video Polyp Segmentation SUN-SEG-Easy (Unseen) PNS+ S measure 0.806 # 1
mean E-measure 0.798 # 1
weighted F-measure 0.676 # 1
mean F-measure 0.730 # 1
Dice 0.756 # 1
Sensitivity 0.630 # 1
Video Polyp Segmentation SUN-SEG-Hard (Unseen) PNS+ S-Measure 0.797 # 1
mean E-measure 0.793 # 1
weighted F-measure 0.653 # 1
mean F-measure 0.709 # 1
Dice 0.737 # 1
Sensitivity 0.623 # 1

Methods