# 3D Prostate Segmentation from MR Images using FCNN

<img src="https://img.shields.io/github/stars/amanbasu/3d-prostate-segmentation?color=0088ff"/> <img src="https://img.shields.io/github/forks/amanbasu/3d-prostate-segmentation?color=ff8800"/> <img src="https://img.shields.io/badge/version-tensorflow==1.10.0-green?logo=tensorflow"/>

Collaborator: [Aman Agarwal](https://amanbasu.github.io), [Aditya Mishra](https://aditya985.github.io)

This repository contains files related to **Volumetric Segmentation of Prostate from MR

Images Using FCNN with Increased Receptive Field**, presented at **Nvidia GTC 2019** ([link](https://github.com/amanbasu/3d-prostate-segmentation/blob/master/images/Deep%20Learning%20Research_20_P9190_Aman_Agarwal_1920x1607.png)). The dataset is provided by [PROMISE12 challenge](https://promise12.grand-challenge.org).

DOI [10.1134/S1054661821020024](https://doi.org/10.1134/S1054661821020024)

# About the files

- [resizing.py](https://github.com/amanbasu/3d-prostate-segmentation/blob/master/resizing.py): Converts volume of different sizes to same (128x128x64).

- [DataGenerator.py](https://github.com/amanbasu/3d-prostate-segmentation/blob/master/DataGenerator.py): For reading the images and performing various augmentations.

- [train.py](https://github.com/amanbasu/3d-prostate-segmentation/blob/master/train.py): File used for training the model.

- [predict.py](https://github.com/amanbasu/3d-prostate-segmentation/blob/master/predict.py): File used for inferencing of trained model.

- [metric_eval.py](https://github.com/amanbasu/3d-prostate-segmentation/blob/master/metric_eval.py): File for evaluating various metrics using predictions and actual labels. Metrics include Hausdorff distance, dice, boundary distance, volume difference, precision, recall and many more.

# Introduction

- Prostate cancer is among the most commonly diagnosed and leading causes of cancer related death in developed countries.

- The early detection of prostate cancer plays a significant role in the success of treatment and outcome.

- Radiologists first segment the prostate image from ultrasound image and then identify the hypoechoic regions which are more likely to exhibit cancer and should be considered for biopsy.

- Manual segmentation consumes considerable time and effort and is not only operator-dependent, but also tedious and repetitious.

- In this work we present an appraoch to segment prostate using Fully Convolutional Neural Networks without requiring the involvement of an expert.

- We were able to obtain results comparable to the state-of-the-art, with the average Dice loss of 0.92 and 0.80 on train and validataion set respectively.

# Data

- In MRI images, the voxel intensities and therefore appearance characteristics of the prostate can greatly differ between acquisition protocols, field strengths and scanners.

- Therefore a segmentation algorithm designed for use in clinical practice needs to deal with these issues.

- We decided to use the data from PROMISE12 challenge which included the scans from four different centers 

    - Haukeland University Hospital (HK), Norway

    - Beth Israel Deaconess Medical Center (BIDMC), United States

    - University College London (UCL), United Kingdom

    - Radboud University Nijmegen Medical Centre (RUNMC), Netherlands. 

- Each of the centers provided <b>25 transverse T2-weighted MR images</b>. This resulted in a total of <b>100</b> MR images. 

- Details pertaining to the acquisition can be found in the table below.

![](images/data_info.png)

![](images/data_demo.png)

# Implementation

We used a modified V-net architecture for segmentation shown in the figure below.

![](images/model_detail.jpg)

# Training

We trained our model on different GPUs and got the following speedups.

| GPU configuration | Batch Size    | Average Time per Epoch (s) |

| ----------------- | ------------- | -------------------------- |

| Single K80        | 2             | 147                        |

| Dual k80          | 2 (1 per GPU) | 102                        |

| Single P100       | 2             | 48                         |

| Single P100       | 5             | 27                         |

## Evaluation Metrics

The metrics used in this study are widely used for the evaluation of segmentation algorithms:

1. <b>Dice coefficient</b>: To measure the similarity between output volumes.

2. <b>Absolute relative volume difference</b>: the percentage of the absolute difference between the volumes.

3. <b>Average boundary distance</b>: the average over the shortest distances between the boundary points of the volumes.

4. <b>95% Hausdorff distance</b>: the maximum of the shortest distances between the boundary points of the volumes. 95% percentile makes the metric less sensitive to outliers.

# Results

After training for 5700 epochs we got a dice loss of 0.94 and 0.87 on training and validation set. The results were then submitted to the MICCAI PROMISE12 challenge, and we received a score of <b>84.61</b> on the test set.