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arXiv · 2105.10556

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

Julio Silva-Rodríguez·Elena Payá-Bosch·Gabriel García·Adrián Colomer·Valery Naranjo
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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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Julio Silva-Rodríguez, Elena Payá-Bosch, Gabriel García, Adrián Colomer, Valery Naranjo. 2021-05-21. Prostate Gland Segmentation in Histology Images via Residual and Multi-Resolution U-Net. https://doi.org/10.1007/978-3-030-62362-3_1

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