README.md

---
tags:
- medical-segmentation
- mri
- knee
- oai
- random-forest
library_name: kneeseg
license: mit
---

# Knee Bone & Cartilage Segmentation Models

This repository contains **Random Forest** models for segmentation of knee bone and cartilage from 3D MRI, trained on the **downsampled OAI dataset**.

These models were trained using the `kneeseg` library: [https://github.com/wq2012/kneeseg](https://github.com/wq2012/kneeseg).

## Model Details

*   **Architecture**: Dense Auto-Context Random Forest (Bone), Semantic Context Forest (Cartilage).
*   **Resolution**: Trained on downsampled images (140x140x112).
*   **Labels**:
    *   `1`: Femur
    *   `2`: Femoral Cartilage
    *   `3`: Tibia
    *   `4`: Tibial Cartilage
    *   `5`: Patella
    *   `6`: Patellar Cartilage

## Dataset

### Original dataset

The original dataset is from Osteoarthritis Initiative (OAI). It contains
176 3D MRI images, each with 160x384x384 voxels, and 0.7x0.364x0.364 resolution.

### Downsampling

All images have been downsampled to 112x140x140 voxels, with 1x1x1 resolution.

### Filtering

We removed images that does not have ground truth labels for any of the 3 bones
and 3 cartilages. This results in 159 images remaining.

The removed images are:

```
[
    "image-9172459_V01.mhd",
    "image-9674570_V01.mhd",
    "image-9867284_V00.mhd",
    "image-9905863_V01.mhd",
    "image-9884303_V00.mhd",
    "image-9352883_V00.mhd",
    "image-9968924_V01.mhd",
    "image-9965231_V01.mhd",
    "image-9905863_V00.mhd",
    "image-9992358_V00.mhd",
    "image-9382271_V00.mhd",
    "image-9607698_V00.mhd",
    "image-9599539_V01.mhd",
    "image-9352437_V00.mhd",
    "image-9352437_V01.mhd",
    "image-9382271_V01.mhd",
    "image-9674570_V00.mhd"
]
```

### Train-Eval Split

After downsampling and filtering, we performed a 80%-20% split,
using 128 images for training, and 31 images for evaluation. See
`oai_split.json` for the split.

## Performance (DSC)

Evaluated on 31 held-out test cases:

| Structure | Dice Score (Mean ± Std) |
| :--- | :--- |
| **Femur** | 0.7130 ± 0.0673 |
| **Tibia** | 0.7545 ± 0.0598 |
| **Patella** | 0.5209 ± 0.0831 |
| **FemCart** | 0.5171 ± 0.0716 |
| **TibCart** | 0.4134 ± 0.0888 |
| **PatCart** | 0.3633 ± 0.1406 |

## Usage

Load these models using the `kneeseg` library:

```python
from kneeseg.bone_rf import BoneClassifier
from kneeseg.rf_seg import CartilageClassifier

# Pass 1 Models
bone_p1 = BoneClassifier()
bone_p1.load("bone_rf_p1.joblib")

cart_p1 = CartilageClassifier()
cart_p1.load("cartilage_rf_p1.joblib")

# Pass 2 Models
bone_p2 = BoneClassifier()
bone_p2.load("bone_rf_p2.joblib")

cart_p2 = CartilageClassifier()
cart_p2.load("cartilage_rf_p2.joblib")
```


## Citation

**Plain Text:**

> Quan Wang, Dijia Wu, Le Lu, Meizhu Liu, Kim L. Boyer, and Shaohua Kevin Zhou.
> "Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images."
> MICCAI 2013: Workshop on Medical Computer Vision.

> Quan Wang.
> Exploiting Geometric and Spatial Constraints for Vision and Lighting Applications.
> Ph.D. dissertation, Rensselaer Polytechnic Institute, 2014.

**BibTeX:**

```bibtex
@inproceedings{wang2013semantic,
  title={Semantic context forests for learning-based knee cartilage segmentation in 3D MR images},
  author={Wang, Quan and Wu, Dijia and Lu, Le and Liu, Meizhu and Boyer, Kim L and Zhou, Shaohua Kevin},
  booktitle={International MICCAI Workshop on Medical Computer Vision},
  pages={105--115},
  year={2013},
  organization={Springer}
}

@phdthesis{wang2014exploiting,
  title={Exploiting Geometric and Spatial Constraints for Vision and Lighting Applications},
  author={Quan Wang},
  year={2014},
  school={Rensselaer Polytechnic Institute},
}
```