ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs

Analyzing anatomic shapes of tissues and organs is pivotal for accurate disease diagnostics and clinical decision-making. One prominent disease that depends on anatomic shape analysis is osteoarthritis, which affects 30 million Americans. To advance osteoarthritis diagnostics and prognostics, we int...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2024-10, Vol.PP, p.1-1
Main Authors: Gatti, Anthony A., Blankemeier, Louis, Veen, Dave Van, Hargreaves, Brian, Delp, Scott L., Gold, Garry E., Kogan, Feliks, Chaudhari, Akshay S.
Format: Article
Language:English
Subjects:
Biological system modeling
Biomarkers
Bones
Deep Learning
Diseases
Image reconstruction
Magnetic resonance imaging
Medical diagnostic imaging
Neural Networks
Osteoarthritis
Shape
Shape Analysis
Surface reconstruction
Three-dimensional displays
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Summary:Analyzing anatomic shapes of tissues and organs is pivotal for accurate disease diagnostics and clinical decision-making. One prominent disease that depends on anatomic shape analysis is osteoarthritis, which affects 30 million Americans. To advance osteoarthritis diagnostics and prognostics, we introduce ShapeMed-Knee , a 3D shape dataset with 9,376 high-resolution, medical-imaging-based 3D shapes of both femur bone and cartilage. Besides data, ShapeMed-Knee includes two benchmarks for assessing reconstruction accuracy and five clinical prediction tasks that assess the utility of learned shape representations. Leveraging ShapeMed-Knee, we develop and evaluate a novel hybrid explicit-implicit neural shape model which achieves up to 40% better reconstruction accuracy than a statistical shape model and implicit neural shape model. Our hybrid models achieve state-of-the-art performance for preserving cartilage biomarkers; they're also the first models to successfully predict localized structural features of osteoarthritis, outperforming shape models and convolutional neural networks applied to raw magnetic resonance images and segmentations. The ShapeMed-Knee dataset provides medical evaluations to reconstruct multiple anatomic surfaces and embed meaningful disease-specific information. ShapeMed-Knee reduces barriers to applying 3D modeling in medicine, and our benchmarks highlight that advancements in 3D modeling can enhance the diagnosis and risk stratification for complex diseases. The dataset, code, and benchmarks will be made freely accessible.
ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2024.3485613