ResUNet++: An Advanced Architecture for Medical Image Segmentation
This work addresses polyp detection for endoscopists, but it is incremental as it improves an existing architecture.
The paper tackled the problem of automated polyp segmentation in colonoscopy images to aid cancer prevention, and the result was that ResUNet++ outperformed U-Net and ResUNet with dice coefficients of 81.33% and 79.55% on two datasets.
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.