kneeseg 0.1.0

A Python reimplementation of Semantic Context Forests for Knee Cartilage Segmentation

kneeseg: Knee Bone & Cartilage Segmentation in 3D MRI

kneeseg is a Python reimplementation of the paper "Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images". [paper] [slides]

	Original MICCAI Workshop Paper	This Implementation
Bone Segmentation	Active Shape Model (Siemens proprietary)	Dense Random Forest (Auto-Context)
Cartilage Segmentation	Semantic Context Forest (C++)	Semantic Context Forest (Python)
Dataset	Osteoarthritis Initiative (OAI)	SKI10

Table of Contents

1. Installation
2. Usage
3. Experiments Folder
4. Experiment Results (SKI10)
5. Algorithm Details
6. Citation

Installation

You can install the package via pip:

pip install kneeseg

Usage

As a Library

from kneeseg.io import load_volume
from kneeseg.bone_rf import BoneClassifier
# ...

Running the Pipeline

The package includes a unified pipeline script kneeseg-pipeline (or via python module):

# Run using the installed command
kneeseg-pipeline --data-dir /path/to/SKI10/data

For advanced usage and reproduction scripts (e.g., training models from scratch), please refer to the Experiments Documentation.

Experiments Folder

The experiments/ directory contains reproduceable scripts and will store the output models (models/) and predictions (predictions/) if you run the scripts provided there. See experiments/README.md for details.

Experiment Results (SKI10)

Since the SKI10 dataset doesn not provide the ground truth labels for its default testing set, we evaluated the pipeline on a 20% hold-out set (20 cases) from the SKI10 training data (Total 80 cases: 60 Train, 20 Eval).

Metrics

Structure	Dice Similarity Coefficient (DSC)
Femur	0.9046 ± 0.0361
Tibia	0.9292 ± 0.0260
Femoral Cartilage	0.5944 ± 0.0654
Tibial Cartilage	0.5805 ± 0.0533

Evaluation Set

The following 20 cases were held out for evaluation: image-004, image-005, image-012, image-014, image-015, image-018, image-028, image-029, image-030, image-032, image-036, image-055, image-065, image-070, image-076, image-082, image-087, image-089, image-095, image-098.

Algorithm Details

Bone Segmentation (Dense Auto-Context RF)

1. Pass 1: Dense Random Forest voxel classification.

   • Features: Normalized Intensity, Gaussian Smoothed Intensity ($\sigma=2.0, 4.0$), Spatial Coordinates, RSID (20 offsets).
   • Target: 3-class classification (Background, Femur, Tibia).

2. Pass 2 (Refinement): Auto-Context Random Forest.

   • Features: All Pass 1 features + Probabilities from Pass 1.
   • Performance: Achieves >0.90 DSC on Bones.

Cartilage Segmentation (Semantic Context Forest)

1. Feature Extraction:
   • Semantic Context: Signed Distance Transforms (SDT) computed from Pass 2 bone masks.
   • Texture: RSID (30 offsets).
   • Local: Intensity, Gaussian ($\sigma=1.0$), Gradient.
   • Arithmetic: DT Sum/Diff.
2. Classification: Dense Random Forest (100 trees).
   • Performance: Achieves ~0.60 DSC.

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:

@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},
}