Prostate cancer is one of the most prevalent cancers worldwide. One of the key factors in reducing its mortality is based on early detection. The computer-aided diagnosis systems for this task are based on the glandular structural analysis in histology images. Hence, accurate gland detection and segmentation is crucial for a successful prediction. The methodological basis of this work is a prostate gland segmentation based on U-Net convolutional neural network architectures modified with residual and multi-resolution blocks, trained using data augmentation techniques. The residual configuration outperforms in the test subset the previous state-of-the-art approaches in an image-level comparison, reaching an average Dice Index of 0.77.
@article{arxiv.2105.10556,
title = {Prostate Gland Segmentation in Histology Images via Residual and Multi-Resolution U-Net},
author = {Julio Silva-Rodríguez and Elena Payá-Bosch and Gabriel García and Adrián Colomer and Valery Naranjo},
journal= {arXiv preprint arXiv:2105.10556},
year = {2021}
}