QSM-RimDS: A highly sensitive paramagnetic rim lesion detection and segmentation tool for multiple sclerosis lesions

Autor:	Luu, Ha, Sisman, Mert, Kovanlikaya, Ilhami, Vu, Tam, Spincemaille, Pascal, Wang, Yi, Bagnato, Francesca, Gauthier, Susan, Nguyen, Thanh
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	Paramagnetic rim lesions (PRLs) are imaging biomarker of the innate immune response in MS lesions. QSM-RimNet, a state-of-the-art tool for PRLs detection on QSM, can identify PRLs but requires precise QSM lesion mask and does not provide rim segmentation. Therefore, the aims of this study are to develop QSM-RimDS algorithm to detect PRLs using the readily available FLAIR lesion mask and to provide rim segmentation for microglial quantification. QSM-RimDS, a deep-learning based tool for joint PRL rim segmentation and PRL detection has been developed. QSM-RimDS has obtained state-of-the art performance in PRL detection and therefore has the potential to be used in clinical practice as a tool to assist human readers for the time-consuming PRL detection and segmentation task. QSM-RimDS is made publicly available [https://github.com/kennyha85/QSM_RimDS] Comment: 11 pages, 6 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.10492 Zobrazit plný text záznamu View this record from Arxiv