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

arXiv: https://arxiv.org/abs/2105.10556 · HTML: https://mlsanity.com/paper/2105.10556/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 21 May 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://mlsanity.com/paper/1902.04049/) (2019)

## Built on by (most cited first, 2 of 2)

- [A review of machine learning approaches, challenges and prospects for computational tumor pathology](https://mlsanity.com/paper/2206.01728/) (2022)
- [A Pathologist-Informed Workflow for Classification of Prostate Glands in Histopathology](https://mlsanity.com/paper/2209.13408/) (2022)

## Abstract (arXiv)

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.