ResUNet++: An Advanced Architecture for Medical Image Segmentation

Code

• github.com/DebeshJha/ResUNetplusplus
  Officialtf★ 0
• github.com/DebeshJha/ResUNet-with-CRF-and-TTA
  tf★ 126
• github.com/GewelsJI/PNS-Net
  pytorch★ 121
• github.com/JanPalasek/resunet-pp-tensorflow
  tf★ 0
• github.com/debeshjha/resunetplusplus-with-crf-and-tta
  tf★ 0
• github.com/rishikksh20/ResUnet
  pytorch★ 0

Abstract

Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer. Towards developing a fully automated model for pixel-wise polyp segmentation, we propose ResUNet++, which is an improved ResUNet architecture for colonoscopic image segmentation. Our experimental evaluations show that the suggested architecture produces good segmentation results on publicly available datasets. Furthermore, ResUNet++ significantly outperforms U-Net and ResUNet, two key state-of-the-art deep learning architectures, by achieving high evaluation scores with a dice coefficient of 81.33%, and a mean Intersection over Union (mIoU) of 79.27% for the Kvasir-SEG dataset and a dice coefficient of 79.55%, and a mIoU of 79.62% with CVC-612 dataset.

Tasks

Benchmark Results

Reproductions