Multitask Conditional Generative Adversarial Network Enables Automatic Whole Knee Cartilage and Menisci Segmentation and Reliable T1r{ho} and T2 Quantification Without High-Resolution Morphological Images

arXiv:2610.06602v1 Announce Type: new Abstract: Early osteoarthritis detection through quantitative MRI (qMRI) requires accurate cartilage and meniscus segmentation, traditionally necessitating time-consuming, costly 3D high-resolution Double Echo Steady-State (DESS) MRI scans. This study developed a multi-task conditional generative adversarial network (MT-cGAN) to simultaneously synthesize DESS-like images and segment tissues directly from qMRI echo images. This retrospective study evaluated 508 knee MRI volumes from 361 subjects (mean age: 40.4 pm 12.2 years; 179 female) across three cohorts. Ground truth segmentation masks were generated from DESS images using a pretrained model with manual correction, and T_{1rho} and T_2 maps were computed from magnetization-prepared angle-modulated partitioned k-space spoiled gradient echo snapshots (MAPSS) echo images. MT-cGAN was trained to jointly synthesize DESS-like images and segment cartilage and meniscus directly from echo images. Model performance was evaluated using Dice score for segmentation accuracy and coefficient of variation (CV) for T_{1rho} and T_2 quantification. MT-cGAN achieved the highest segmentation performance, mean Dice score 0.84 (range: 0.80--0.86) across all cartilage and meniscus compartments and significantly outperformed the state-of-the-art conditional GAN model with transfer learning (mean Dice, 0.82; p < 0.001, Wilcoxon signed-rank test). For relaxometry quantification, MT-cGAN demonstrated the highest consistency with the reference DESS protocol, yielding the lowest CV (T_{1rho}: 1.84%, T_2: 1.81%). The proposed MT-cGAN accurately segmented cartilage and menisci while providing reliable T_{1rho} and T_2 quantification directly from echo images. By eliminating the need for separate morphological DESS scans, this workflow reduces required scan times to facilitate the clinical translation of qMRI.

Related

• MRecover: A Conditional Generative Model for Recovering Motion-Corrupted MR images Using AI Generated Contrast
• Beyond Accuracy: Uncertainty-Guided Boundary Refinement for Reliable Biomedical Image Segmentation
• SMILE-UHURA Challenge -- Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms
• M^{2}SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation

Source: arXiv cs.CV | 2026-10-06