Prostate Gland Segmentation in Histology Images via Residual and Multi-Resolution U-Net

• URL: http://arxiv.org/abs/2105.10556v1
• Date: Fri, 21 May 2021 20:11:36 GMT
• Title: Prostate Gland Segmentation in Histology Images via Residual and Multi-Resolution U-Net
• Authors: Julio Silva-Rodr\'iguez, Elena Pay\'a-Bosch, Gabriel Garc\'ia, Adri\'an Colomer and Valery Naranjo
• Abstract summary: 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.
• Score: 1.244681179922733
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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