Francesca Bagnato

Jiacheng Wang, Hao Li, Han Liu et al.

Automated segmentation of multiple sclerosis (MS) lesions from MRI scans is important to quantify disease progression. In recent years, convolutional neural networks (CNNs) have shown top performance for this task when a large amount of labeled data is available. However, the accuracy of CNNs suffers when dealing with few and/or sparsely labeled datasets. A potential solution is to leverage the information available in large public datasets in conjunction with a target dataset which only has limited labeled data. In this paper, we propose a training framework, SSL2 (self-supervised-semi-supervised), for multi-modality MS lesion segmentation with limited supervision. We adopt self-supervised learning to leverage the knowledge from large public 3T datasets to tackle the limitations of a small 7T target dataset. To leverage the information from unlabeled 7T data, we also evaluate state-of-the-art semi-supervised methods for other limited annotation settings, such as small labeled training size and sparse annotations. We use the shifted-window (Swin) transformer1 as our backbone network. The effectiveness of self-supervised and semi-supervised training strategies is evaluated in our in-house 7T MRI dataset. The results indicate that each strategy improves lesion segmentation for both limited training data size and for sparse labeling scenarios. The combined overall framework further improves the performance substantially compared to either of its components alone. Our proposed framework thus provides a promising solution for future data/label-hungry 7T MS studies.

Jiacheng Wang, Hao Li, Xing Yao et al.

Quantitative magnetization transfer (qMT) imaging provides myelin-sensitive biomarkers, such as the pool size ratio (PSR), which is valuable for multiple sclerosis (MS) assessment. However, qMT requires specialized 20-30 minute scans. We propose DEMIST to synthesize PSR maps from standard T1w and FLAIR images using a 3D latent diffusion model with three complementary conditioning mechanisms. Our approach has two stages: first, we train separate autoencoders for PSR and anatomical images to learn aligned latent representations. Second, we train a conditional diffusion model in this latent space on top of a frozen diffusion foundation backbone. Conditioning is decoupled into: (i) \textbf{semantic} tokens via cross-attention, (ii) \textbf{spatial} per-scale residual hints via a 3D ControlNet branch, and (iii) \textbf{adaptive} LoRA-modulated attention. We include edge-aware loss terms to preserve lesion boundaries and alignment losses to maintain quantitative consistency, while keeping the number of trainable parameters low and retaining the inductive bias of the pretrained model. We evaluate on 163 scans from 99 subjects using 5-fold cross-validation. Our method outperforms VAE, GAN and diffusion baselines on multiple metrics, producing sharper boundaries and better quantitative agreement with ground truth. Our code is publicly available at https://github.com/MedICL-VU/MS-Synthesis-3DcLDM.

Ha Luu, Mert Sisman, Ilhami Kovanlikaya et al.

Paramagnetic rim lesions (PRLs) are an emerging biomarker in multiple sclerosis (MS). Manual identification and rim segmentation of PRLs on quantitative susceptibility mapping (QSM) images are time-consuming. Deep learning-based QSM-RimNet can provide automated PRL detection, but this method does not provide rim segmentation for microglial density quantification and requires precise QSM lesion masks. The purpose of this study is to develop a U-Net-based QSM-RimDS method for joint PRL detection and rim segmentation using readily available T2-weighted (T2W) fluid-attenuated inversion recovery (FLAIR) lesion masks. Two expert readers performed PRL classification and rim segmentation as the reference. Dice similarity coefficient (DSC) was used to assess the agreement between rim segmentation obtained by QSM-RimDS and the manual expert segmentation. The PRL detection performances of QSM-RimDS and QSM-RimNet were evaluated using receiver operating characteristic (ROC) and precision-recall (PR) plots in a five-fold cross validation. A total of 260 PRLs (3.3\%) and 7720 non-PRLs (96.7\%) were identified by the readers. Compared to the expert rim segmentation, QSM-RimDS provided a mean DSC of 0.57 \pm 0.02 with moderate to high agreement (DSC \leq 0.5) in 73.8pm 5.7\% of PRLs over five folds. QSM-RimDS produced better and more consistent detection performance with a mean area under curve (AUC) of 0.754 \pm 0.037 vs. 0.514 \pm 0.121 by QSM-RimNet (46.7\% improvement) on PR plots, and 0.956 \pm 0.034 vs. 0.908 \pm 0.073 (5.3\% improvement) on ROC plots. In conclusion, QSM-RimDS improves PRL detection accuracy compared to QSM-RimNet and unlike QSM-RimNet can provide reasonably accurate rim segmentation.