APRNet: Alternative Prediction Refinement Network for Polyp Segmentation

Shen，Yutian1; Jia，Xiao2; Pan，Jin1; Meng，Max Q.H.3,4,5

10.1109/EMBC46164.2021.9630525

2021

43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

1557-170X

1558-4615

978-1-7281-1180-3

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

3114-3117

NOV 01-05, 2021

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Colorectal cancer has become the second leading cause of cancer-related death, attracting considerable interest for automatic polyp segmentation in polyp screening system. Accurate segmentation of polyps from colonoscopy is a challenging task as the polyps diverse in color, size and texture while the boundary between polyp and background is sometimes ambiguous. We propose a novel alternative prediction refinement network (APRNet) to more accurately segment polyps. Based on the UNet architecture, our APRNet aims at exploiting all-level features by alternatively leveraging features from encoder and decoder branch. Specifically, a series of prediction residual refinement modules (PRR) learn the residual and progressively refine the segmentation at various resolution. The proposed APRNet is evaluated on two benchmark datasets and achieves new state-of-the-art performance with a dice of 91.33% and an accuracy of 97.31% on the Kvasir-SEG dataset, and a dice of 86.33% and an accuracy of 97.12% on the EndoScene dataset.Clinical relevance - This work proposes an automatic and accurate polyp segmentation algorithm that achieves new state- of-the-art performance, which can potentially act as an observer pointing out polyps in colonoscopy procedure.

Image segmentation Image color analysis Colonoscopy Benchmark testing Observers Prediction algorithms Biology

其他

英语

CPCI-S ; EI

National Key R&D program of China[2019YFB1312400]

Engineering

Engineering, Biomedical ; Engineering, Electrical & Electronic

WOS:000760910503011

20220811670507

2-s2.0-85122543696

Scopus

1.The Chinese University of Hong Kong,N.T. Department of Electronic Engineering,Hong Kong
2.Department of Radiation Oncology,Stanford University,Stanford,United States
3.Department of Electronic and Electrical Engineering,Southern University of Science and Technology,Shenzhen,China
4.Department of Electronic Engineering,The Chinese University of Hong Kong,Hong Kong
5.Shenzhen Research Institute,Chinese University of Hong Kong,Shenzhen,China

Shen，Yutian,Jia，Xiao,Pan，Jin,et al. APRNet: Alternative Prediction Refinement Network for Polyp Segmentation[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:3114-3117.