--- a
+++ b/README.md
@@ -0,0 +1,69 @@
+# 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.