language:
- en
pipeline_tag: image-segmentation
tags:
- medical
library_name: fastMONAI
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 VIBE 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. The primary objective of this repository is to reproduce the results reported in the study. In addition, we have looked at improving the segmentation performance using multi-sequence MR images (T2w, VIBE, and ADC) as reported in the study Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network.
Requirements
Last checked and validated with fastMONAI version 0.3.9. 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. Test our model live with the Gradio app for VIBE on Hugging Face Spaces.
Results for VIBE
The box plot of the predictions on the validation set:
The results from the validation set are also presented in the table below:
| subject_id | tumor_vol | inter_rater | r1_ml | r2_ml | n_components | |
|---|---|---|---|---|---|---|
| 0 | 29 | 4.16 | 0.201835 | 0.806382 | 0.00623053 | 3 |
| 1 | 32 | 8 | 0.684142 | 0.293306 | 0.209449 | 4 |
| 2 | 36 | 19.06 | 0.92875 | 0.793055 | 0.784799 | 2 |
| 3 | 47 | 11.01 | 0.944209 | 0.900945 | 0.898409 | 2 |
| 4 | 50 | 6.26 | 0.722867 | 0.614357 | 0.624832 | 1 |
| 5 | 65 | 13.09 | 0.930613 | 0.879279 | 0.850546 | 2 |
| 6 | 67 | 3.71 | 0.943498 | 0.887189 | 0.878163 | 2 |
| 7 | 75 | 7.16 | 0.263539 | 0.774237 | 0.266619 | 2 |
| 8 | 86 | 7.04 | 0.842577 | 0.821208 | 0.798148 | 1 |
| 9 | 135 | 8.1 | 0.839964 | 0.758176 | 0.680348 | 2 |
| 10 | 140 | 19.78 | 0.895506 | 0.936177 | 0.874019 | 4 |
| 11 | 164 | 16.98 | 0.905008 | 0.923559 | 0.887268 | 1 |
| 12 | 246 | 6.59 | 0.899448 | 0.895311 | 0.860322 | 3 |
| 13 | 255 | 36.22 | 0.955784 | 0.927517 | 0.921816 | 6 |
| 14 | 343 | 0.69 | 0.528261 | 0.840237 | 0.600751 | 4 |
| 15 | 349 | 2.96 | 0.912664 | 0.828181 | 0.778983 | 1 |
| 16 | 367 | 1.02 | 0.0734848 | 0.391737 | 0.118035 | 1 |
| 17 | 370 | 10.82 | 0.953443 | 0.917094 | 0.908893 | 1 |
| 18 | 371 | 3.83 | 0.859781 | 0.684751 | 0.618114 | 1 |
| 19 | 375 | 11.67 | 0.911141 | 0.921079 | 0.91056 | 4 |
| 20 | 377 | 4.37 | 0.782994 | 0.712791 | 0.680165 | 1 |
| 21 | 381 | 7.63 | 0.89199 | 0.246428 | 0.238641 | 1 |
| 22 | 385 | 2.67 | 0.803215 | 0.641916 | 0.60169 | 1 |
| 23 | 395 | 0.68 | 0.770738 | 0.198273 | 0.236343 | 5 |
| 24 | 397 | 5.94 | 0.904544 | 0.882265 | 0.874036 | 3 |
| 25 | 409 | 11.86 | 0.944934 | 0.900727 | 0.900767 | 1 |
| 26 | 411 | 5.98 | 0.949977 | 0.933271 | 0.929499 | 1 |
| 27 | 425 | 0.91 | 0.802867 | 0.589069 | 0.545761 | 1 |
| 28 | 434 | 94.42 | 0.894601 | 0.590408 | 0.580585 | 1 |
| 29 | 531 | 22.08 | 0.89225 | 0.555066 | 0.505109 | 1 |
| 30 | 540 | 8.35 | 0.923702 | 0.855009 | 0.840958 | 1 |
Median DSC: 0.8946, 0.8212, 0.779
Results for multi-sequence (T2, VIBE, and ADC)
The box plot of the predictions on the validation set:
The results from the validation set are also presented in the table below:
| subject_id | tumor_vol | inter_rater | r1_ml | r2_ml | n_components | |
|---|---|---|---|---|---|---|
| 0 | 29 | 4.16 | 0.201835 | 0.859937 | 0.148586 | 4 |
| 1 | 32 | 8 | 0.684142 | 0.662779 | 0.515479 | 10 |
| 2 | 36 | 19.06 | 0.92875 | 0.902343 | 0.888306 | 1 |
| 3 | 47 | 11.01 | 0.944209 | 0.907344 | 0.907 | 3 |
| 4 | 50 | 6.26 | 0.722867 | 0.581594 | 0.540991 | 5 |
| 5 | 65 | 13.09 | 0.930613 | 0.889782 | 0.862255 | 4 |
| 6 | 67 | 3.71 | 0.943498 | 0.851658 | 0.842331 | 2 |
| 7 | 75 | 7.16 | 0.263539 | 0.750551 | 0.205457 | 2 |
| 8 | 86 | 7.04 | 0.842577 | 0.87216 | 0.81374 | 1 |
| 9 | 135 | 8.1 | 0.839964 | 0.80436 | 0.747164 | 1 |
| 10 | 140 | 19.78 | 0.895506 | 0.907457 | 0.852548 | 1 |
| 11 | 164 | 16.98 | 0.905008 | 0.92533 | 0.893135 | 2 |
| 12 | 246 | 6.59 | 0.899448 | 0.906569 | 0.852195 | 5 |
| 13 | 255 | 36.22 | 0.955784 | 0.924517 | 0.927624 | 2 |
| 14 | 343 | 0.69 | 0.528261 | 0.868251 | 0.457711 | 3 |
| 15 | 349 | 2.96 | 0.912664 | 0.85214 | 0.819898 | 1 |
| 16 | 367 | 1.02 | 0.0734848 | 0.383455 | 0.0891463 | 3 |
| 17 | 370 | 10.82 | 0.953443 | 0.916154 | 0.911768 | 2 |
| 18 | 371 | 3.83 | 0.859781 | 0.593136 | 0.565848 | 8 |
| 19 | 375 | 11.67 | 0.911141 | 0.898501 | 0.910147 | 3 |
| 20 | 377 | 4.37 | 0.782994 | 0.713798 | 0.646684 | 3 |
| 21 | 381 | 7.63 | 0.89199 | 0.4375 | 0.430847 | 1 |
| 22 | 385 | 2.67 | 0.803215 | 0.688608 | 0.624595 | 1 |
| 23 | 395 | 0.68 | 0.770738 | 0.385992 | 0.43154 | 2 |
| 24 | 397 | 5.94 | 0.904544 | 0.868022 | 0.850653 | 6 |
| 25 | 409 | 11.86 | 0.944934 | 0.83407 | 0.833206 | 5 |
| 26 | 411 | 5.98 | 0.949977 | 0.867137 | 0.866112 | 1 |
| 27 | 425 | 0.91 | 0.802867 | 0.557732 | 0.475499 | 3 |
| 28 | 434 | 94.42 | 0.894601 | 0.618916 | 0.605596 | 6 |
| 29 | 531 | 22.08 | 0.89225 | 0.349648 | 0.319533 | 1 |
| 30 | 540 | 8.35 | 0.923702 | 0.890343 | 0.88052 | 1 |
Median DSC: 0.8946, 0.8521, 0.8137
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.