LM-CartSeg: Automated Segmentation of Lateral and Medial Cartilage and Subchondral Bone for Radiomics Analysis

• URL: http://arxiv.org/abs/2512.03449v2
• Date: Fri, 05 Dec 2025 16:30:37 GMT
• Title: LM-CartSeg: Automated Segmentation of Lateral and Medial Cartilage and Subchondral Bone for Radiomics Analysis
• Abstract summary: LM-CartSeg is a fully automatic pipeline for cartilage/bone segmentation, geometric lateral/medial compartmentalisation and radiomics analysis.<n>Results: Post-processing improved macro ASSD on OAIZIB-CM from 2.63 to 0.36 mm and HD95 from 25.2 to 3.35 mm, with DSC 0.91; zero-shot datasets on SKI-10 was 0.80.
• Score: 0.2538209532048867
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background and Objective: Radiomics of knee MRI requires robust, anatomically meaningful regions of interest (ROIs) that jointly capture cartilage and subchondral bone. Most existing work relies on manual ROIs and rarely reports quality control (QC). We present LM-CartSeg, a fully automatic pipeline for cartilage/bone segmentation, geometric lateral/medial (L/M) compartmentalisation and radiomics analysis. Methods: Two 3D nnU-Net models were trained on SKM-TEA (138 knees) and OAIZIB-CM (404 knees). At test time, zero-shot predictions were fused and refined by simple geometric rules: connected-component cleaning, construction of 10 mm subchondral bone bands in physical space, and a data-driven tibial L/M split based on PCA and k-means. Segmentation was evaluated on an OAIZIB-CM test set (103 knees) and on SKI-10 (100 knees). QC used volume and thickness signatures. From 10 ROIs we extracted 4 650 non-shape radiomic features to study inter-compartment similarity, dependence on ROI size, and OA vs. non-OA classification on OAIZIB-CM Results: Post-processing improved macro ASSD on OAIZIB-CM from 2.63 to 0.36 mm and HD95 from 25.2 to 3.35 mm, with DSC 0.91; zero-shot DSC on SKI-10 was 0.80. The geometric L/M rule produced stable compartments across datasets, whereas a direct L/M nnU-Net showed domain-dependent side swaps. Only 6 to 12 percent of features per ROI were strongly correlated with volume or thickness. Radiomics-based models models restricted to size-linked features. Conclusions: LM-CartSeg yields automatic, QCd ROIs and radiomic features that carry discriminative information beyond simple morphometry, providing a practical foundation for multi-centre knee OA radiomics studies.

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