ResUNet++: An Advanced Architecture for Medical Image Segmentation
Chapter, Conference object, Peer reviewed
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Original versionJha D, Smedsrud P, Riegler M, Johansen D, de Lange T, Halvorsen P, Johansen HJ: ResUNet++: An Advanced Architecture for Medical Image Segmentation. In: Ceballos. Proceedings of the 2019 IEEE International Symposium on Multimedia ISM 2019, 2019. IEEE p. 225-230 https://dx.doi.org/10.1109/ISM46123.2019.00049
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.