---
language:
- en
pipeline_tag: image-segmentation
tags:
- medical
---
# Endometrial cancer segmentation

This repository contains weights and exported learner (encapsulates both the model architecture and its trained parameters) for a deep learning model designed to automate the segmentation of endometrial cancer on MR images. 
Our model utilizes a Residual U-Net architecture, trained on data derived from the study [Automated segmentation of endometrial cancer on MR images using deep learning](https://link.springer.com/content/pdf/10.1038/s41598-020-80068-9.pdf).
The primary objective of this repository is to reproduce the results reported in the study.

## Requirements

Last checked and validated with fastMONAI version 0.3.2. Please ensure that you have the correct version of fastMONAI installed to guarantee the correct operation of the model.

## Usage

The source code for training the model and running inference on your own data is available at: https://github.com/MMIV-ML/fastMONAI/tree/master/research/endometrial_cancer.

## Results

The box plot of the predictions on the validation set: 
![](boxplot.png)

The results from the validation set are also presented in the table below:

| subject_id | tumor_vol | inter_rater | r1_ml    | r2_ml    |
|------------|-----------|-------------|----------|----------|
| 29         | 4.16      | 0.201835    | 0.806382 | 0.006231 |
| 32         | 8.00      | 0.684142    | 0.293306 | 0.209449 |
| 36         | 19.06     | 0.928750    | 0.793611 | 0.785335 |
| 47         | 11.01     | 0.944209    | 0.905159 | 0.902548 |
| 50         | 6.26      | 0.722867    | 0.619272 | 0.631579 |
| 65         | 13.09     | 0.930613    | 0.879279 | 0.850546 |
| 67         | 3.71      | 0.943498    | 0.887189 | 0.878163 |
| 75         | 7.16      | 0.263539    | 0.774411 | 0.266463 |
| 86         | 7.04      | 0.842577    | 0.821208 | 0.798148 |
| 135        | 8.10      | 0.839964    | 0.758176 | 0.680348 |
| 140        | 19.78     | 0.895506    | 0.936177 | 0.874019 |
| 164        | 16.98     | 0.905008    | 0.923559 | 0.887268 |
| 246        | 6.59      | 0.899448    | 0.907503 | 0.871254 |
| 255        | 36.22     | 0.955784    | 0.927517 | 0.921816 |
| 343        | 0.69      | 0.528261    | 0.840237 | 0.600751 |
| 349        | 2.96      | 0.912664    | 0.828181 | 0.778983 |
| 367        | 1.02      | 0.073485    | 0.392027 | 0.117796 |
| 370        | 10.82     | 0.953443    | 0.917094 | 0.908893 |
| 371        | 3.83      | 0.859781    | 0.685033 | 0.618380 |
| 375        | 11.67     | 0.911141    | 0.921345 | 0.910804 |
| 377        | 4.37      | 0.782994    | 0.712791 | 0.680165 |
| 381        | 7.63      | 0.891990    | 0.245768 | 0.237990 |
| 385        | 2.67      | 0.803215    | 0.641916 | 0.601690 |
| 395        | 0.68      | 0.770738    | 0.204418 | 0.242908 

## Support and Contribution
For any issues related to the model or the source code, please open an issue in the corresponding GitHub repository. Contributions to the code or the model are welcome and should be proposed through a pull request.