Video Polyp Segmentation: A Deep Learning Perspective | |
Ge-Peng Ji5; Guobao Xiao4; Yu-Cheng Chou3; Deng-Ping Fan1; Kai Zhao2; Geng Chen6; Luc Van Gool1 | |
刊名 | Machine Intelligence Research
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2022 | |
卷号 | 19期号:6页码:531-549 |
关键词 | Video polyp segmentation (VPS) dataset self-attention colonoscopy abdomen |
ISSN号 | 2731-538X |
DOI | 10.1007/s11633-022-1371-y |
英文摘要 | We present the first comprehensive video polyp segmentation (VPS) study in the deep learning era. Over the years, devel- opments 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 (170fps), making it a prom- ising 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. |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/55960] ![]() |
专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
作者单位 | 1.Computer Vision Laboratory, ETH Zòrich, Zòrich 8092, Switzerland 2.Department of Radiological Sciences, University of California, Los Angeles 90095, USA 3.Department of Computer Science, Johns Hopkins University, Baltimore 21218, USA 4.College of Computer and Control Engineering, Minjiang University, Fuzhou 350108, China 5.Research School of Engineering, Australian National University, Canberra 2601, Australia 6.School of Computer Science and Engineering, Northwestern Polytechnical University, Xi/an 710072, China |
推荐引用方式 GB/T 7714 | Ge-Peng Ji,Guobao Xiao,Yu-Cheng Chou,et al. Video Polyp Segmentation: A Deep Learning Perspective[J]. Machine Intelligence Research,2022,19(6):531-549. |
APA | Ge-Peng Ji.,Guobao Xiao.,Yu-Cheng Chou.,Deng-Ping Fan.,Kai Zhao.,...&Luc Van Gool.(2022).Video Polyp Segmentation: A Deep Learning Perspective.Machine Intelligence Research,19(6),531-549. |
MLA | Ge-Peng Ji,et al."Video Polyp Segmentation: A Deep Learning Perspective".Machine Intelligence Research 19.6(2022):531-549. |
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