HRENet: A Hard Region Enhancement Network for Polyp Segmentation

Shen，Yutian1; Jia，Xiao2; Meng，Max Q.H.1,3

10.1007/978-3-030-87193-2_53

2021

0302-9743

1611-3349

LECT NOTES ARTIF INT

12901 LNCS

559-568

Automatic polyp segmentation in the screening system is of great practical significance for the diagnosis and treatment of colorectal cancer. However, accurate segmentation in the colonoscopy images still remains a challenge. In this paper, we propose a hard region enhancement network (HRENet) based on an encoder-decoder framework. Specifically, we design an informative context enhancement (ICE) module to explore and intensify the features from the lower-level encoder with explicit attention on hard regions. We also develop an adaptive feature aggregation (AFA) module to select and aggregate the features from multiple semantic levels. In addition, we train the model with a proposed edge and structure consistency aware loss (ESCLoss) to further boost the performance. Extensive experiments on three public datasets show that our proposed algorithm outperforms the state-of-the-art approaches in terms of both learning ability and generalization capability. In particular, our HRENet achieves a mIoU of 92.11% and a Dice of 92.56% on Kvasir-SEG dataset. And the model trained with Kvasir-SEG and CVC-Clinic DB retains a high inference performance on the unseen dataset CVC-Colon DB with a mIoU of 88.42% and a Dice of 85.26%. The code is available at: https://github.com/CathySH/HRENet.

Colonoscopy Deep learning Feature enhancement Hard region Polyp segmentation

其他

英语

EI

20214110990397

Deep learning ; Diseases ; Image segmentation ; Medical imaging ; Semantics ; Signal encoding

Biomedical Engineering:461.1 ; Ergonomics and Human Factors Engineering:461.4 ; Medicine and Pharmacology:461.6 ; Information Theory and Signal Processing:716.1 ; Imaging Techniques:746

2-s2.0-85116436998

Scopus

1.Department of Electronic Engineering,The Chinese University of Hong Kong,Sha Tin,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

Shen，Yutian,Jia，Xiao,Meng，Max Q.H.. HRENet: A Hard Region Enhancement Network for Polyp Segmentation[C],2021:559-568.