# Comprehensive study of good model training for prostate segmentation in volumetric MRI

arXiv: https://arxiv.org/abs/2208.13671 · HTML: https://mlsanity.com/paper/2208.13671/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 29 Aug 2022.

## Builds on (most cited first, 14 of 14)

- [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)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
- [Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network](https://mlsanity.com/paper/1703.02719/) (2017)
- [Automated Design of Deep Learning Methods for Biomedical Image Segmentation](https://mlsanity.com/paper/1904.08128/) (2019)
- [Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation](https://mlsanity.com/paper/1902.08128/) (2019)
- [Multidimensional Contrast Limited Adaptive Histogram Equalization](https://mlsanity.com/paper/1906.11355/) (2019)
- [Transfer Learning with Edge Attention for Prostate MRI Segmentation](https://mlsanity.com/paper/1912.09847/) (2019)
- [3D Global Convolutional Adversarial Network for Prostate MR Volume Segmentation](https://mlsanity.com/paper/1807.06742/) (2018)

## Abstract (arXiv)

Prostate cancer was the third most common cancer in 2020 internationally, coming after breast cancer and lung cancer. Furthermore, in recent years prostate cancer has shown an increasing trend. According to clinical experience, if this problem is detected and treated early, there can be a high chance of survival for the patient. One task that helps diagnose prostate cancer is prostate segmentation from magnetic resonance imaging. Manual segmentation performed by clinical experts has its drawbacks such as: the high time and concentration required from observers; and inter- and intra-observer variability. This is why in recent years automatic approaches to segment a prostate based on convolutional neural networks have emerged. Many of them have novel proposed architectures. In this paper I make an exhaustive study of several deep learning models by adjusting them to the task of prostate prediction. I do not use novel architectures, but focus my work more on how to train the networks. My approach is based on a ResNext101 3D encoder and a Unet3D decoder. I provide a study of the importance of resolutions in resampling data, something that no one else has done before.