[
    {
        "id": "osp-17977",
        "type": "article-journal",
        "title": "Multitask Conditional Generative Adversarial Network Enables Automatic Whole Knee Cartilage and Menisci Segmentation and Reliable $T_{1ρ}$ and $T_2$ Quantification Without High-Resolution Morphological Images",
        "author": [
            {
                "family": "Minhaz",
                "given": "Ahmed Tahseen"
            },
            {
                "family": "Lartey",
                "given": "Richard"
            },
            {
                "family": "Zhang",
                "given": "Zhiyuan"
            },
            {
                "family": "Kim",
                "given": "Jeehun"
            },
            {
                "family": "Nakamura",
                "given": "Kunio"
            },
            {
                "family": "Yang",
                "given": "Mingrui"
            },
            {
                "family": "Zhang",
                "given": "Jiasen"
            },
            {
                "family": "Guo",
                "given": "Weihong"
            },
            {
                "family": "Subhas",
                "given": "Naveen"
            },
            {
                "family": "Winalski",
                "given": "Carl S."
            },
            {
                "family": "Li",
                "given": "Xiaojuan"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/multitask-conditional-generative-adversarial-network-enables-automatic-whole-knee-cartilage-and-menisci-segmentation-and-reliable-t-1-and-t-2-quantifi",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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 $T_{1ρ}$ 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 $T_{1ρ}$ 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 ($T_{1ρ}$: 1.84%, $T_2$: 1.81%). The proposed MT-cGAN accurately segmented cartilage and menisci while providing reliable $T_{1ρ}$ 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."
    }
]