ArticleMedical image analysis2022
DADP: Dynamic abnormality detection and progression for longitudinal knee magnetic resonance images from the Osteoarthritis Initiative.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Ultrasound therapy inhibits knee osteoarthritis progression in rabbits by activating the PPARs pathway: a pilot study.Annals of medicine · 2025Article
- Learning co-plane attention across MRI sequences for diagnosing twelve types of knee abnormalities.Nature communications · 2024Article
- Investigating the relationship between radiographic joint space width loss and deep learning-derived magnetic resonance imaging-based cartilage thickness loss in the medial weight-bearing region of the tibiofemoral joint.Osteoarthritis and cartilage open · 2024Article
- MRIS: A Multi-modal Retrieval Approach for Image Synthesis on Diverse Modalities.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Modeling Longitudinal Optical Coherence Tomography Images for Monitoring and Analysis of Glaucoma Progression.Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.) · 2023Article
- Artificial intelligence in knee osteoarthritis: A comprehensive review for 2022.Osteoarthritis imaging · 2023Article
- Patterns of variation among baseline femoral and tibial cartilage thickness and clinical features: Data from the osteoarthritis initiative.Osteoarthritis and cartilage open · 2023Article
- Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches.Journal of healthcare engineering · 2022Review
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Authors and funding
10 authors.
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Abstract
Osteoarthritis (OA) is the most common disabling joint disease. Magnetic resonance (MR) imaging has been commonly used to assess knee joint degeneration due to its distinct advantage in detecting morphologic cartilage changes. Although several statistical methods over conventional radiography have been developed to perform quantitative cartilage analyses, little work has been done capturing the development and progression of cartilage lesions (or abnormal regions) and how they naturally progress. There are two major challenges, including (i) the lack of building spatial-temporal correspondences and correlations in cartilage thickness and (ii) the spatio-temporal heterogeneity in abnormal regions. The goal of this work is to propose a dynamic abnormality detection and progression (DADP) framework for quantitative cartilage analysis, while addressing the two challenges. First, spatial correspondences are established on flattened 2D cartilage thickness maps extracted from 3D knee MR images both across time within each subject and across all subjects. Second, a dynamic functional mixed effects model (DFMEM) is proposed to quantify abnormality progression across time points and subjects, while accounting for the spatio-temporal heterogeneity. We systematically evaluate our DADP using simulations and real data from the Osteoarthritis Initiative (OAI). Our results show that DADP not only effectively detects subject-specific dynamic abnormal regions, but also provides population-level statistical disease mapping and subgroup analysis.
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