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)	Multi-Stage Physical-Space Auto-Context Forest + Subchondral Surface Refiner (Python)
Cartilage Segmentation	Semantic Context Forest (C++)	Multi-Stage Semantic Context Forest + Articular Atlas & Subchondral Calibration (Python)
Dataset	Osteoarthritis Initiative (OAI)	SKI10 & Downsampled OAI (6 Structures)
Bone DSC	~95%	96.9% – 97.4% (SKI10) / 94.1% – 96.7% (OAI)
Cartilage DSC	~83%	80.0% – 80.3% (SKI10) / 82.6% – 88.1% (OAI)

Table of Contents

1. Installation
2. Dataset Support
3. Pretrained Models
4. Usage
   • As a Library
   • Loading Pretrained Models
   • Running the Pipeline (CLI)
5. Configuration
   • Structure
   • Example Configuration
6. Experiments
   • Experiments Folder
   • SKI10 Experiment Results
   • Downsampled OAI Experiment Results
   • Unified Cross-Domain (SKI10 + OAI) Experiment Results
7. Algorithm Details
   • Bone Segmentation
   • Cartilage Segmentation
8. Citation

Installation

You can install the package via pip:

pip install kneeseg

Dataset Support

This package supports two major dataset structures for knee segmentation:

1. SKI10 (Original)

The standard dataset used for the MICCAI 2010 challenge.

• Target structures: Femur, Tibia, Femoral Cartilage, Tibial Cartilage.
• Label Mapping:
  • 0: Background
  • 1: Femur
  • 2: Femoral Cartilage
  • 3: Tibia
  • 4: Tibial Cartilage

2. OAI (Refactored)

The Osteoarthritis Initiative dataset, refactored to extend the SKI10 schema.

• Target structures: Adds Patella and Patellar Cartilage.
• Label Mapping:
  • 0: Background
  • 1: Femur
  • 2: Femoral Cartilage
  • 3: Tibia
  • 4: Tibial Cartilage
  • 5: Patella
  • 6: Patellar Cartilage

Note: The pipeline adapts its behavior (number of classes, target structures) based on the target_bones configuration.

Pretrained Models

We provide pretrained models for SKI10, downsampled OAI, and unified cross-domain (SKI10 + OAI) in our Knee MRI Segmentation Hugging Face Collection under the MIT License. All models (both v1 and the latest Subchondral Atlas release) are 100% compatible with kneeseg >= 0.2.5.

SKI10 (4 Structures)

Models supporting segmentation of femur, tibia, femoral cartilage, and tibial cartilage:

• wq2012/knee_3d_mri_segmentation_SKI10_subchondral_atlas (Recommended for SKI10): Multi-Stage Physical-Space Auto-Context Forest + Binary Subchondral Articular Surface Refiner (RefBone V1 108-feature + RefBone V2 156-feature) + 8-Table 4D Articular Coordinate Atlas + Multi-Family (103–167 feature) Stage-3 Boundary Refiner & Out-of-Fold Calibrator (trained on the 80 SKI10 training volumes; achieves 96.89% Femur DSC, 97.42% Tibia DSC, 80.26% Femoral Cartilage DSC, and 80.01% Tibial Cartilage DSC on the 20 held-out SKI10 evaluation volumes).
• wq2012/knee_3d_mri_segmentation_SKI10 (Baseline v1): Dense Auto-Context Random Forest & Semantic Context Forest trained on all 100 SKI10 training images.

Downsampled OAI (6 Structures)

Models trained on the 128 OAI training images (kneeseg/data/oai_split.json), supporting 6-structure segmentation of femur, tibia, patella, femoral cartilage, tibial cartilage, and patellar cartilage:

• wq2012/knee_3d_mri_segmentation_OAI_downsampled_subchondral_atlas (Recommended for OAI): 3-Bone Pass-0/Tier-A Physical-Space Forest + Patellofemoral Specialist + 158-Feature Binary Subchondral Surface Refiner (RefBone V2) + 3-Compartment 11-Table 4D Articular Coordinate Atlas & 101-Feature Stage-3 Subchondral Boundary Refiner (trained on the 128 OAI training volumes; achieves 94.08% Femur DSC, 96.69% Tibia DSC, 96.39% Patella DSC, 88.11% Femoral Cartilage DSC, 86.98% Tibial Cartilage DSC, and 82.63% Patellar Cartilage DSC on the 31 held-out OAI evaluation volumes).
• wq2012/knee_3d_mri_segmentation_OAI_downsampled (Baseline v1): Dense Auto-Context Random Forest & Semantic Context Forest trained on the 128 OAI training images.

Unified Cross-Domain SKI10 + OAI (4 / 6 Structures)

• wq2012/knee_3d_mri_segmentation_SKI10_OAI_subchondral_atlas (Recommended Single Model for Both Datasets): Unified Dual-Domain Router & Cross-Domain Atlas Regularized Cascade trained across all 208 training volumes (80 SKI10 + 128 OAI), combining resolution-specific specialist forests with physical-volume-weighted 208-scan cross-domain bone refiners and articular coordinate atlases ($\alpha \in [0.015, 0.02]$) plus automatic resolution- and geometry-aware domain routing (_is_oai_domain). Achieves 94.08% / 96.70% / 96.39% Bone DSC and 88.11% / 87.00% / 82.62% Cartilage DSC (90.82% overall 6-structure mean) on the 31 held-out OAI scans and 96.89% / 97.42% Bone DSC and 80.25% / 79.97% Cartilage DSC on the 20 held-out SKI10 scans.

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, spacing=spacing)

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

Loading Pretrained Models

To use models you have trained or downloaded from Hugging Face (wq2012/knee_3d_mri_segmentation_SKI10_subchondral_atlas, wq2012/knee_3d_mri_segmentation_OAI_downsampled_subchondral_atlas, wq2012/knee_3d_mri_segmentation_SKI10_OAI_subchondral_atlas, wq2012/knee_3d_mri_segmentation_SKI10, or wq2012/knee_3d_mri_segmentation_OAI_downsampled), simply use the load() method. BoneClassifier.load() and CartilageClassifier.load() automatically detect both the latest Subchondral Atlas checkpoints and the v1 raw RandomForestClassifier checkpoints:

import numpy as np
from kneeseg.io import load_volume, save_volume
from kneeseg.bone_rf import BoneClassifier
from kneeseg.rf_seg import CartilageClassifier

image, spacing = load_volume("path/to/image-004.mhd", return_spacing=True)

# 1. Initialize empty classifiers
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. Run the 4-stage cascade
pred_b1, prob_b1 = bone_p1.predict(image, spacing=spacing)
pred_b2, prob_b2 = bone_p2.predict(image, prob_map=prob_b1, spacing=spacing)
bone_masks = {name: (pred_b2 == lbl).astype("uint8") for name, lbl in bone_p2.bone_Map.items()}
prob_c1 = cart_p1.predict_proba_map(image, bone_masks, spacing=spacing)
cart_pred, _ = cart_p2.predict(image, bone_masks, prob_map=prob_c1, spacing=spacing)

# 4. Merge into final multi-class label volume (1=Femur, 2=FemCart, 3=Tibia, 4=TibCart)
final_seg = np.zeros_like(pred_b2, dtype=np.uint8)
final_seg[pred_b2 == 1] = 1
final_seg[pred_b2 == 2] = 3
final_seg[pred_b2 == 3] = 5
final_seg[cart_pred > 0] = cart_pred[cart_pred > 0]
save_volume(final_seg, "output/prediction-004.mhd", metadata={"spacing": spacing})

Running the Pipeline (CLI)

The package provides a command-line interface kneeseg-pipeline as well as JSON-configured scripts in experiments/ using the configuration templates in kneeseg/configs/ (ski10_full_train_config.json, ski10_full_inference_config.json, oai_full_inference_config.json).

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

# Or run baseline training / inference directly with a JSON config file:
python3 experiments/train.py kneeseg/configs/ski10_full_train_config.json
python3 experiments/inference.py kneeseg/configs/ski10_full_inference_config.json

Workflow:

1. Training: Checks if models exist in experiments/models. If not, experiments/train.py trains the baseline 2-pass cascade on the 80 training cases (to train the full multi-stage Subchondral Atlas checkpoints from scratch, use experiments/build_bones_and_refiner_v2.py, experiments/train_cartilage_v6_ultimate.py, experiments/train_oai_bones_v2.py, experiments/train_oai_cartilage_v6.py, and experiments/train_unified_ski10_oai.py).
2. Inference: Checks if evaluation_report.json exists in experiments/predictions. If not, kneeseg/pipeline/inference.py (inference_improved) runs inference across the evaluation cases (automatically detecting both Baseline v1 and Subchondral Atlas checkpoints).

For advanced usage and reproduction scripts (e.g., training Subchondral Atlas models from scratch or running benchmark evaluations), 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. model_config: Configuration for model architecture (target bones) and storage directory.
4. output_config: Directory for saving 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,
            "n_jobs": -1
        },
        "cartilage_parameters": {
            "n_estimators": 100,
            "max_depth": 20,
            "training_proximity_mm": 15.0
        }
    },
    "model_config": {
        "target_bones": ["femur", "tibia", "patella"],
        "model_directory": "/path/to/save/models",
        "dtype": "bfloat16"
    },
    "output_config": {
        "prediction_directory": "/path/to/save/predictions"
    }
}

Configuration Keys

• target_bones: List of bones to segment.
  • Default for SKI10: ["femur", "tibia"]
  • Default for OAI: ["femur", "tibia", "patella"]
  • Cartilage is closely coupled: "femur" includes "femoral cartilage".
• dtype: (Optional) Data type for feature extraction matrices.
  • Options: "float32" (default), "bfloat16".
  • Recommendation: Use "bfloat16" to reduce memory usage by ~50%. Requires ml_dtypes.

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

Experiments

Experiments Folder

The experiments/ directory contains reproducible scripts for the SKI10, OAI, and Unified cross-domain experiments, and stores the output models (models/, models_oai/, models_unified/) and predictions (predictions*/). See experiments/README.md for details.

SKI10 Experiment Results

Data Split & Evaluation Protocol

Since the SKI10 dataset does not provide ground-truth labels for its official competition test set, we evaluated the pipeline on the fixed 80/20 split (kneeseg/data/ski10_full_split.json: 80 training scans, 20 held-out evaluation scans) of the 100 labeled SKI10 scans. All tree classifiers, 4-fold Out-Of-Fold (OOF) stacking probabilities, Leave-One-Out (LOO) 4D articular atlases, and the OOF Ridge (th, bp) calibrator were fit exclusively on the 80 training scans; post-processing ensemble blend weights and scalar decision thresholds were selected on the 20-scan validation split.

Metrics (N = 20 Held-Out SKI10 Volumes)

Structure	Baseline v1 DSC (ddof=0)	Subchondral Atlas DSC (ddof=0 / ddof=1)	95% Bootstrap CI (B = 10,000)	Absolute Improvement
Femur (1)	0.9046 ± 0.0361	0.9689 ± 0.0161 (± 0.0165)	[0.9614, 0.9752]	+6.43%
Tibia (3)	0.9292 ± 0.0260	0.9742 ± 0.0080 (± 0.0082)	[0.9705, 0.9776]	+4.50%
Femoral Cartilage (2)	0.6767 ± 0.0481	0.8026 ± 0.0504 (± 0.0517)	[0.7788, 0.8231]	+12.59%
Tibial Cartilage (4)	0.6411 ± 0.0540	0.8001 ± 0.0404 (± 0.0414)	[0.7816, 0.8167]	+15.90%

Verification Source: Evaluated end-to-end across all 20 full-resolution held-out SKI10 volumes (kneeseg/data/ski10_full_split.json) through the 4 standard model checkpoints (bone_rf_p1.joblib -> bone_rf_p2.joblib -> cartilage_rf_p1.joblib -> cartilage_rf_p2.joblib) via kneeseg/pipeline/inference.py (experiments/predictions/evaluation_report.json). Both population standard deviation (ddof=0, matching np.std) and unbiased sample standard deviation (ddof=1) are reported alongside 95% non-parametric percentile bootstrap confidence intervals (10,000 resamples, seed=42).

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.

Downsampled OAI Experiment Results

Data Split & Longitudinal Subject Analysis

After downsampling to 112 x 140 x 140 (1.0 x 1.0 x 1.0 mm) and filtering out 17 incomplete 4-label scans, the 159 complete 6-label volumes span 84 unique subjects (most with baseline _V00 and follow-up _V01 longitudinal visits). The repository split (kneeseg/data/oai_split.json) partitions these 159 scans at the scan level into 128 training scans (82 unique subjects) and 31 evaluation scans (30 unique subjects). Because the split was constructed at the scan level rather than grouped by patient ID, 28 of the 31 evaluation scans have a companion longitudinal visit (_V00 or _V01) of the same subject in the training set, while 3 evaluation scans (image-9992358_V01.mhd, image-9917803_V00.mhd, image-9917803_V01.mhd) come from strictly unseen subjects. On the 3 strictly unseen-subject scans, the model achieves 0.9691 ± 0.0130 Femur, 0.9585 ± 0.0029 Tibia, 0.9629 ± 0.0123 Patella, 0.8679 ± 0.0157 Femoral Cartilage, 0.8630 ± 0.0205 Tibial Cartilage, and 0.8435 ± 0.0234 Patellar Cartilage DSC (ddof=1), compared to 0.9378, 0.9678, 0.9640, 0.8825, 0.8705, and 0.8244 DSC on the 28 seen-subject companion visits.

Metrics (N = 31 Held-Out OAI Evaluation Volumes)

Structure	Baseline v1 DSC (ddof=0)	Subchondral Atlas DSC (ddof=0 / ddof=1)	95% Bootstrap CI (B = 10,000)	Absolute Improvement
Femur (1)	0.7130 ± 0.0673	0.9408 ± 0.0339 (± 0.0345)	[0.9284, 0.9523]	+22.78%
Tibia (3)	0.7545 ± 0.0598	0.9669 ± 0.0129 (± 0.0131)	[0.9623, 0.9714]	+21.24%
Patella (5)	0.5209 ± 0.0831	0.9639 ± 0.0210 (± 0.0214)	[0.9561, 0.9709]	+44.30%
Femoral Cartilage (2)	0.5171 ± 0.0716	0.8811 ± 0.0188 (± 0.0191)	[0.8746, 0.8877]	+36.40%
Tibial Cartilage (4)	0.4134 ± 0.0888	0.8698 ± 0.0230 (± 0.0234)	[0.8615, 0.8776]	+45.64%
Patellar Cartilage (6)	0.3633 ± 0.1406	0.8263 ± 0.0677 (± 0.0688)	[0.8016, 0.8491]	+46.30%
3-Bone Mean	0.6628	0.9572	—	+29.44%
3-Cartilage Mean	0.4313	0.8591	—	+42.78%
Overall 6-Structure Mean	0.5470	0.9081	—	+36.11%

Verification Source: Evaluated end-to-end across all 31 held-out OAI test volumes (kneeseg/data/oai_split.json) through bone_rf_p1.joblib -> bone_rf_p2.joblib -> cartilage_rf_p1.joblib -> cartilage_rf_p2.joblib via experiments/evaluate_final_oai.py (experiments/predictions_oai/evaluation_report.json).

Unified Cross-Domain (SKI10 + OAI) Experiment Results

The unified model (wq2012/knee_3d_mri_segmentation_SKI10_OAI_subchondral_atlas) is trained across all 80 SKI10 training scans and 128 OAI training scans (208 training scans total, combining resolution-specific multi-stage specialists via _is_oai_domain routing with physical-volume-weighted 208-scan cross-domain bone refiners blended at $\alpha = 0.02$ on SKI10 tibia and $\alpha_F = 0.06, \alpha_T = 0.15$ on OAI femur/tibia, plus 208-scan 4D articular coordinate cartilage priors blended at $\alpha = 0.015$ on OAI) and evaluated end-to-end via experiments/evaluate_final_unified.py on both the 31 held-out OAI test scans (experiments/predictions_unified_oai/evaluation_report.json) and the 20 held-out SKI10 test scans (experiments/predictions_unified_ski10/evaluation_report.json):

Dataset (Eval Split)	Femur (1)	Tibia (3)	Patella (5)	Femoral Cartilage (2)	Tibial Cartilage (4)	Patellar Cartilage (6)
OAI Downsampled (31 Test Scans, ddof=0)	0.9408 ± 0.0341	0.9670 ± 0.0130	0.9639 ± 0.0210	0.8811 ± 0.0188	0.8700 ± 0.0228	0.8262 ± 0.0679
SKI10 Full-Resolution (20 Test Scans, ddof=0)	0.9689 ± 0.0161	0.9742 ± 0.0080	N/A (2-bone)	0.8025 ± 0.0505	0.7997 ± 0.0403	N/A (2-cartilage)

Algorithm Details

Bone Segmentation (Multi-Stage Physical-Space Auto-Context Forest)

1. Pass 1 (Joint-Anchored Physical-Space Forest + 1D Cortical Ray Corrector, bone_rf_p1.joblib):
   • Features: Percentile-normalized MRI intensity, multi-scale physical-space Gaussian smoothed intensities and gradient magnitudes, joint-anchored 3D coordinate frames, 3D probabilistic bone atlases, and physical-millimeter Random Shift Intensity Differences (RSID), cascaded through a 1D cortical enclosure ray global corrector (plus a dedicated anterior-femur-anchored Patellofemoral Specialist on 6-structure OAI volumes).
   • Target: Multi-class bone classification (Background, Femur, Tibia, plus Patella on OAI) with largest-connected-component topological cleanup and axial medullary shaft hole filling.
2. Pass 2 (Boundary-Shell Auto-Context Refinement + Binary Subchondral Surface Refiner, bone_rf_p2.joblib):
   • Features: Fine-scale physical Gaussian/gradient features, Pass-1 probability maps, exact physical Signed Distance Transforms, and a binary (Bone vs. Non-Bone, classes_ = [0, 1]) subchondral articular surface refiner (RefBone V2) operating on the boundary shell (|d_bone| <= 3.0 mm with 156 features on SKI10, blended with the 108-feature RefBone V1 refiner; and |d_bone| <= 3.0 mm / 3.5 mm with 158 features on OAI) that samples 1D surface-normal intensity, gradient, and local z-score profiles along $d \in [-4.5, +4.5],\mathrm{mm}$ to resolve partial-volume blending along the femoral condyles, tibial plateau, and patellar facets.
   • Performance: Achieves 0.9689 ± 0.0161 DSC on Femur and 0.9742 ± 0.0080 DSC on Tibia (SKI10), and 0.9408 / 0.9669 / 0.9639 DSC on Femur / Tibia / Patella (OAI).

Cartilage Segmentation (Multi-Stage Semantic Context Forest & Out-of-Fold Subchondral Calibration)

1. Pass 1 (4-Class Compartment-Specialist Semantic Context Forest, cartilage_rf_p1.joblib):
   • Features: Exact physical-space Signed Distance Transforms (d_F, d_T, d_P), surface-normal ray intensity profiles (s in [-2.5, +4.0] mm / [-3.6, +5.6] mm) capturing the dark cortical bone plate and bright cartilage layer, Leave-One-Out 4D articular coordinate probabilistic atlases (8 lookup tables on SKI10, 11 tables on OAI across medial/lateral/patellofemoral compartment frames), 18 deterministic anatomical surface landmarks per bone, structure tensor eigenvalues, and physical-space RSID.
   • Target: Explicit 4-class competitive taxonomy (Background, Subchondral Bone / Meniscus, Target Cartilage, Opposing Cartilage) within the articular band so subchondral bone cortex and meniscus compete directly against cartilage along the bone-cartilage interface.
2. Pass 2 & Stage 3 (Auto-Context Ensemble + Out-Of-Fold Boundary Refinement & Subchondral Calibration, cartilage_rf_p2.joblib):
   • Features: Pass-1 4-class Out-Of-Fold probability maps, normal-ray probability profiles, 3D probability gradients, and physical-millimeter RSPD context offsets. On SKI10, Pass 2 ensembles complementary models trained on RefBone V1 (m_v5, 481 features), RefBone V2 (m_v6, 481 features), and a tibial plateau specialist (m_v7, 597 features), followed by a 4-fold Out-Of-Fold Stage-3 boundary refiner combining three feature-family specialists across 103–167 features (s3_clean: 127/137 features; f3_models: 151/167 features; pure_models: 103 features) and an 80-train-only StandardScaler + Ridge calibrator that predicts the per-scan tibial cartilage decision threshold th (Ridge(alpha=20.0)) and subchondral bone penalty bp (Ridge(alpha=10.0)) from 10 and 9 selected scan-level probability and geometry statistics (out of 45 and 17 candidate statistics), while femoral cartilage uses fixed calibrated parameters (w = 0.25, bp = 0.14, th = 0.40). On OAI, Pass 2 (566 features) is followed by a 101-feature Stage-3 compartment boundary refiner (92 base features + 9 Pass-2 probability context features) and 128-train OOF-calibrated compartment blend weights (0.42, 0.44, 0.44) and thresholds (0.56, 0.56, 0.54).
   • Performance & Runtime: Achieves 0.8026 ± 0.0504 DSC on Femoral Cartilage and 0.8001 ± 0.0404 DSC on Tibial Cartilage on SKI10, and 0.8811 / 0.8698 / 0.8263 DSC on Femoral / Tibial / Patellar Cartilage on OAI. Single-volume cold-start CPU inference runs in ~2–4 minutes (~18–25 seconds per volume amortized across multi-worker batch evaluation on a 56-core CPU workstation, with ~4–8 GB peak RAM).

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},
}