mlsanityData to 14 Oct 2026

CartiMorph: a framework for automated knee articular cartilage morphometrics

arXiv 2308.01981 ↗First posted 3 Aug 2023eess.IV cs.CV cs.LGcited by 5

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Abstract (arXiv)

We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation. Deep learning models were trained and validated for tissue segmentation, template construction, and template-to-image registration. We established methods for surface-normal-based cartilage thickness mapping, FCL estimation, and rule-based cartilage parcellation. Our cartilage thickness map showed less error in thin and peripheral regions. We evaluated the effectiveness of the adopted segmentation model by comparing the quantitative metrics obtained from model segmentation and those from manual segmentation. The root-mean-squared deviation of the FCL measurements was less than 8%, and strong correlations were observed for the mean thickness (Pearson's correlation coefficient ρ ∈ [0.82,0.97]), surface area (ρ ∈ [0.82,0.98]) and volume (ρ ∈ [0.89,0.98]) measurements. We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths. We observed superior performance of the proposed rule-based cartilage parcellation method compared with the atlas-based approach. CartiMorph has the potential to promote imaging biomarkers discovery for knee osteoarthritis.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 28 of its 123 references that are in the map. First posted on arXiv 3 Aug 2023.

Builds on

28 of 123 references.

References · 28

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16 more
  1. 2017End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Networkcited by 75
  2. 2019DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentationcited by 66
  3. 2019Learning Conditional Deformable Templates with Convolutional Networkscited by 65
  4. 2018Learning a Probabilistic Model for Diffeomorphic Registrationcited by 62
  5. 2018Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequencescited by 47
  6. 2022XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attentioncited by 46
  7. 2019Networks for Joint Affine and Non-parametric Image Registrationcited by 44
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.

  1. 2024Quantifying Knee Cartilage Shape and Lesion: From Image to Metricscited by 1
  2. 2026Retrieving Patient-Specific Radiomic Feature Sets for Transparent Knee MRI Assessmentcited by 1
  3. 2025MedVision: Benchmarking Quantitative Medical Image Analysiscited by 0
  4. 2025LM-CartSeg: Automated Segmentation of Lateral and Medial Cartilage and Subchondral Bone for Radiomics Analysiscited by 0
  5. 2026Learning Coarse-to-Fine Osteoarthritis Representations under Noisy Hierarchical Labelscited by 0