SALI: Short-term Alignment and Long-term Interaction Network for Colonoscopy Video Polyp Segmentation
This work addresses the problem of accurate polyp segmentation for rectal cancer diagnosis, which is incremental as it builds on existing video segmentation methods with specific modules for short-term alignment and long-term interaction.
The paper tackles robust polyp segmentation in colonoscopy videos by enhancing adjacent feature consistency and rebuilding reliable polyp representations, achieving Dice improvements of 2.1% to 4.1% over state-of-the-art methods on the SUNSEG benchmark.
Colonoscopy videos provide richer information in polyp segmentation for rectal cancer diagnosis. However, the endoscope's fast moving and close-up observing make the current methods suffer from large spatial incoherence and continuous low-quality frames, and thus yield limited segmentation accuracy. In this context, we focus on robust video polyp segmentation by enhancing the adjacent feature consistency and rebuilding the reliable polyp representation. To achieve this goal, we in this paper propose SALI network, a hybrid of Short-term Alignment Module (SAM) and Long-term Interaction Module (LIM). The SAM learns spatial-aligned features of adjacent frames via deformable convolution and further harmonizes them to capture more stable short-term polyp representation. In case of low-quality frames, the LIM stores the historical polyp representations as a long-term memory bank, and explores the retrospective relations to interactively rebuild more reliable polyp features for the current segmentation. Combing SAM and LIM, the SALI network of video segmentation shows a great robustness to the spatial variations and low-visual cues. Benchmark on the large-scale SUNSEG verifies the superiority of SALI over the current state-of-the-arts by improving Dice by 2.1%, 2.5%, 4.1% and 1.9%, for the four test sub-sets, respectively. Codes are at https://github.com/Scatteredrain/SALI.