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

This repository contains a model trained based on the data 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) with the objective of reproducing the reported results.

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