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arXiv Computer Vision By Ahmed Tahseen Minhaz, Richard Lartey, Zhiyuan Zhang, Jeehun Kim, Kunio Nakamura, Mingrui Yang, Jiasen Zhang, Weihong Guo, Naveen Subhas, Carl S. Winalski, Xiaojuan Li

Multitask Conditional Generative Adversarial Network Enables Automatic Whole Knee Cartilage and Menisci Segmentation and Reliable $T_{1\rho}$ and $T_2$ Quantification Without High-Resolution Morphological Images

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The study introduces a multitask conditional generative adversarial network (MT‑cGAN) that simultaneously synthesizes high‑resolution DESS‑like images and segments knee cartilage and menisci directly from quantitative MRI echo images. Evaluated on 508 knee MRIs from 361 subjects, MT‑cGAN achieved a mean Dice score of 0.84 for segmentation and the lowest coefficient of variation for T1ρ (1.84%) and T2 (1.81%) quantification, outperforming existing conditional GAN approaches. By eliminating the need for separate high‑resolution morphological scans, the method shortens scan times and supports clinical adoption of quantitative MRI.

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