kneeseg 0.1.2

A Python reimplementation of Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images

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] [poster]

	Original MICCAI Workshop Paper	This Implementation
Bone Segmentation	Active Shape Model (Siemens proprietary)	Dense Auto-Context Random Forest (Python)
Cartilage Segmentation	Semantic Context Forest (C++)	Semantic Context Forest (Python)
Dataset	Osteoarthritis Initiative (OAI)	SKI10
Bone DSC	~95%	~92%
Cartilage DSC	~83%	~66%

Table of Contents

1. Installation
2. Usage
   • As a Library
   • Loading Pretrained Models
   • Running the Pipeline (CLI)
3. Configuration
   • Structure
   • Example Configuration
4. Experiments Folder
5. Experiment Results (SKI10)
   • Metrics
   • Evaluation Set
6. Algorithm Details
   • Bone Segmentation (Dense Auto-Context RF)
   • Cartilage Segmentation (Semantic Context Forest)
7. Citation

Installation

You can install the package via pip:

pip install kneeseg

Usage

As a Library

You can use kneeseg modules directly in your Python scripts to load data, train models, or run inference.

import os
from kneeseg.io import load_volume, save_volume
from kneeseg.bone_rf import BoneClassifier

# 1. Load Data
# Data should be .mhd/.raw or .hdr/.img
image_path = 'data/image-001.mhd'
image, spacing = load_volume(image_path, return_spacing=True)

# 2. Initialize Model
# Example: Initialize the first-pass Bone Classifier
bone_rf = BoneClassifier(n_estimators=100, max_depth=25)

# 3. Predict (assuming pre-trained model loaded or trained)
# bone_rf.load('models/bone_rf_p1.joblib')
pred_mask, prob_map = bone_rf.predict(image)

# 4. Save Result
save_volume(pred_mask, 'output/prediction.mhd', metadata={'spacing': spacing})

Loading Pretrained Models

Pretrained models (trained on all 100 SKI10 training cases) are available on Hugging Face:

• Hugging Face Hub: https://huggingface.co/wq2012/knee_3d_mri_segmentation_SKI10

To use models you have trained or downloaded (e.g., from the Hugging Face release), simply use the load() method:

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

# 1. Initialize empty classifiers
# (Parameters must match training, or just use defaults if standard)
bone_p1 = BoneClassifier()
bone_p2 = BoneClassifier()
cart_p1 = CartilageClassifier()
cart_p2 = CartilageClassifier()

# 2. Load the weights
bone_p1.load("path/to/bone_rf_p1.joblib")
bone_p2.load("path/to/bone_rf_p2.joblib")
cart_p1.load("path/to/cartilage_rf_p1.joblib")
cart_p2.load("path/to/cartilage_rf_p2.joblib")

# 3. Predict (Example: Bone Pass 1)
pred_p1, prob_p1 = bone_p1.predict(image)

Running the Pipeline (CLI)

The package provides a command-line interface kneeseg-pipeline to orchestrate the full training and inference workflow using the SKI10 dataset split.

Prerequisites:

• Data Structure: The pipeline expects two directories:
  1. .../images: Contains .mhd image files.
  2. .../images_labels: Contains .mhd label files (folder name must be image_dir + _labels).
• File Naming: Files must match the SKI10 naming convention (e.g., image-001.mhd, labels-001.mhd) as defined in kneeseg/data/ski10_full_split.json.

Command:

# Point to your image directory. Expects sibling directory with "_labels" suffix.
kneeseg-pipeline --data-dir /path/to/SKI10/data/images

Workflow:

1. Training: Checks if models exist in experiments/models. If not, trains using the 60 training cases.
2. Inference: Checks if evaluation_report.json exists in experiments/predictions. If not, runs inference on the 20 evaluation cases.

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

Configuration

The pipeline relies on a JSON configuration file to define data paths and model parameters. You can create your own config file for custom experiments.

Structure

A valid configuration file has three main sections:

1. data_config: Paths to your data and split files.
2. training_config: Parameters for Random Forest training (e.g., number of trees).
3. output_config: Directories for saving models and predictions.

Example Configuration

{
    "data_config": {
        "image_directory": "/path/to/images",
        "label_directory": "/path/to/labels",
        "split_file": "/path/to/split.json"
    },
    "training_config": {
        "augmentation": true,
        "bone_parameters": {
            "n_estimators": 100,
            "max_depth": 25,
            "pca_components": 20
        },
        "cartilage_parameters": {
            "n_estimators": 100,
            "max_depth": 20,
            "training_proximity_mm": 15.0
        }
    },
    "output_config": {
        "model_directory": "/path/to/save/models",
        "prediction_directory": "/path/to/save/predictions"
    }
}

Note: The split_file should be a JSON containing {"train": ["file1.mhd", ...], "eval": ["file2.mhd", ...]}.

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 100 cases: 80 Train, 20 Eval).

Metrics

Structure	Dice Similarity Coefficient (DSC)
Femur	0.9046 ± 0.0361
Tibia	0.9292 ± 0.0260
Femoral Cartilage	0.6767 ± 0.0481
Tibial Cartilage	0.6411 ± 0.0540

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. Pass 1: Initial Semantic Context Forest.
   • Features: Signed Distance Transforms (SDT) from Bones, RSID, Texture, Gaussian.
   • Performance: ~0.60 DSC.
2. Pass 2 (Refinement): Auto-Context Random Forest.
   • Features: All Pass 1 features + Probabilities from Cartilage Pass 1.
   • Performance: Achieves ~0.70 DSC (Femoral) and ~0.68 DSC (Tibial).

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},
}