Mert Sisman

Alexandra G. Roberts, Maneesh John, Jinwei Zhang et al.

We propose a novel spectral vision transformer architecture for efficient tokenization in limited data, with an emphasis on medical imaging. We outline convenient theoretical properties arising from the choice of basis including spatial invariance and optimal signal-to-noise ratio. We show reduced complexity arising from the spectral projection compared to spatial vision transformers. We show equitable or superior performance with a reduced number of parameters as compared to a variety of models including compact and standard vision transformers, convolutional neural networks with attention, shifted window transformers, multi-layer perceptrons, and logistic regression. We include simulated, public, and clinical data in our analysis and release our code at: \verb+github.com/agr78/spectralViT+.

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.