CartiMorph: a framework for automated knee articular cartilage morphometrics

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 $\rho \in [0.82,0.97]$), surface area ($\rho \in [0.82,0.98]$) and volume ($\rho \in [0.89,0.98]$) 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.Comment: To be published in Medical Image Analysi

Unsupervised Domain Adaptation for Automated Knee Osteoarthritis Phenotype Classification

Purpose: The aim of this study was to demonstrate the utility of unsupervised domain adaptation (UDA) in automated knee osteoarthritis (OA) phenotype classification using a small dataset (n=50). Materials and Methods: For this retrospective study, we collected 3,166 three-dimensional (3D) double-echo steady-state magnetic resonance (MR) images from the Osteoarthritis Initiative dataset and 50 3D turbo/fast spin-echo MR images from our institute (in 2020 and 2021) as the source and target datasets, respectively. For each patient, the degree of knee OA was initially graded according to the MRI Osteoarthritis Knee Score (MOAKS) before being converted to binary OA phenotype labels. The proposed UDA pipeline included (a) pre-processing, which involved automatic segmentation and region-of-interest cropping; (b) source classifier training, which involved pre-training phenotype classifiers on the source dataset; (c) target encoder adaptation, which involved unsupervised adaption of the source encoder to the target encoder and (d) target classifier validation, which involved statistical analysis of the target classification performance evaluated by the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity and accuracy. Additionally, a classifier was trained without UDA for comparison. Results: The target classifier trained with UDA achieved improved AUROC, sensitivity, specificity and accuracy for both knee OA phenotypes compared with the classifier trained without UDA. Conclusion: The proposed UDA approach improves the performance of automated knee OA phenotype classification for small target datasets by utilising a large, high-quality source dataset for training. The results successfully demonstrated the advantages of the UDA approach in classification on small datasets.Comment: Junru Zhong and Yongcheng Yao share the same contribution. 17 pages, 4 figures, 4 table

Primer sequences of the microsatellite markers used in linkage mapping.

<p>Primer sequences of the microsatellite markers used in linkage mapping.</p

Two-point linkage analysis using genetic markers flanking the <i>MYBPC1</i> in these two DA2 families.

<p>Two-point linkage analysis using genetic markers flanking the <i>MYBPC1</i> in these two DA2 families.</p

Summary of the features of the chin of FSS or SHS cases from literatures.

<p>Summary of the features of the chin of FSS or SHS cases from literatures.</p

Haplotype analysis in pedigree X.

<p>Haplotype analysis indicating microsatellite markers on chromosome 12q flanking <i>MYBPC1</i> common to all affected individuals with distal arthrogryposis type 2. Common haplotype is highlighted in yellow and derived from the mildly symptomatic founder (case X1). The question mark indicates the unknown status of the individuals of family X. Case X1 had minor facial anomalies, suggesting she may have a somatic mosaicism of the MYBPC1 mutation (p.E359K). The small filled circle inside the open circle indicates an asymptomatic carrier. Marker’s name is shown at the left. Dotted line in pedigree X indicates individuals 16, 19 and 20 were born from different fathers.</p

Primer sequences of the microsatellite markers used in linkage mapping.

<p>Primer sequences of the microsatellite markers used in linkage mapping.</p

Typical limb contractures and overextension contractures in the two DA2 families.

<p><b>A</b>: Note that typical limb contractures include ulnar deviation of fingers, adducted stiff/clasped thumbs, severe camptodactyly, overlapping fingers (H1), vertical talus(X6) and clubfeet after surgical corrections accompanied by overriding toes (H1) in severely affected individuals. <b>B</b>: Note that overextension contractures were observed at the metatarsophalangeal joints(X5, X12 and H40) or the proximal interphalangeal joints(X13 and X19) of the toes.</p

Clinical data of affected individuals in these two Chinese families with DA2.

<p>Clinical data of affected individuals in these two Chinese families with DA2.</p

Facial contractures in affected individuals from both Chinese DA2 families.

<p><b>A</b>: Note that prominent nasolabial folds and small nose with small nostrils were present in the affected individuals. Pouting lips (H1, H23, X2, X5 and X6) or pinched lips (H19, H36 and X1) were noticed. A crease extending laterally and downward from corners of the mouth (prominent in H1 and X1, mild in H19, H23 and H36) or “non-H-shaped” cutaneous dimples on the sides of the chin (H38, X2 and X5) were also observed. The prominent nasolabial folds extended downward below the corners of the mouth so that which exhibiting like “parentheses” around the mouth (X2 and X5). A very small whistling mouth (X6) and a deep vertical groove besides the left corner of the mouth (H38) were remarkable. <b>B</b>: Note that dental crowding were found in four affected individuals of pedigree H. <b>C</b>: Micrognathia was also present in pedigree H.</p