https://github.com/osbm/monai-prostate158-inference
https://github.com/osbm/monai-prostate158-inference
Last synced: over 1 year ago
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- Host: GitHub
- URL: https://github.com/osbm/monai-prostate158-inference
- Owner: osbm
- Created: 2023-08-04T23:17:50.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-08-23T16:36:38.000Z (almost 3 years ago)
- Last Synced: 2025-02-01T23:38:28.509Z (over 1 year ago)
- Language: Python
- Size: 8.79 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Steps to reproduce scores
## Install requirements
```
pip install -r requirements.txt
```
## Download the dataset
Download and unzip [this file](https://huggingface.co/datasets/osbm/prostate158/blob/main/data.zip).
```python
from huggingface_hub import hf_hub_download
path = hf_hub_download(repo_id="osbm/prostate158", filename="data.zip", repo_type="dataset")
!unzip {path} -d .
```
## Download the model
Download the `anatomy.pt` file from this [zenodo repository](https://zenodo.org/record/6397057)
You can use my zenodo file downloader for this step:
```python
from datasets import load_dataset
load_dataset("osbm/zenodo", "7040585_anatomy.pt")
```
## Run the inference script
In this step, the produced masks will be saved to the `test` folder as `predicted.nii.gz` files for every case.
- This step takes about 5 minutes in cpu.
```
python inference.py
```
## Run score calculator script
```
python score.py
```
# Results
DICE scores:
| Case | Background | Inner Prostate | Outer Prostate |
| ---| ---| ---| --- |
|1 |0.99 | 0.792 | 0.81 |
| 2|0.993 | 0.897 | 0.703 |
|3 |0.992 | 0.917 | 0.757 |
| 4|0.994 | 0.838 | 0.725 |
|5 |0.994 | 0.899 | 0.771 |
| 6|0.995 | 0.871 | 0.77 |
|7 |0.995 | 0.892 | 0.761 |
| 8|0.996 | 0.87 | 0.78 |
|9 |0.996 | 0.847 | 0.722 |
| 10|0.988 | 0.841 | 0.693 |
|11 |0.996 | 0.839 | 0.825 |
| 12 |0.994 | 0.831 | 0.817 |
| 13 |0.993 | 0.823 | 0.744 |
| 14 |0.996 | 0.859 | 0.842 |
| 15 |0.995 | 0.889 | 0.767 |
| 16 |0.996 | 0.83 | 0.684 |
| 17 |0.995 | 0.864 | 0.779 |
| 18 |0.989 | 0.857 | 0.636 |
| 19 |0.995 | 0.915 | 0.636 |
| Mean | 0.99378947 | 0.86163158 | 0.74852632 |