FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3156196068> ?p ?o ?g. }

• W3156196068 endingPage "2692" @default.
• W3156196068 startingPage "2680" @default.
• W3156196068 abstract "Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system, characterized by the appearance of focal lesions in the white and gray matter that topographically correlate with an individual patient’s neurological symptoms and signs. Magnetic resonance imaging (MRI) provides detailed in-vivo structural information, permitting the quantification and categorization of MS lesions that critically inform disease management. Traditionally, MS lesions have been manually annotated on 2D MRI slices, a process that is inefficient and prone to inter-/intra-observer errors. Recently, automated statistical imaging analysis techniques have been proposed to detect and segment MS lesions based on MRI voxel intensity. However, their effectiveness is limited by the heterogeneity of both MRI data acquisition techniques and the appearance of MS lesions. By learning complex lesion representations directly from images, deep learning techniques have achieved remarkable breakthroughs in the MS lesion segmentation task. Here, we provide a comprehensive review of state-of-the-art automatic statistical and deep-learning MS segmentation methods and discuss current and future clinical applications. Further, we review technical strategies, such as domain adaptation, to enhance MS lesion segmentation in real-world clinical settings." @default.
• W3156196068 created "2021-04-26" @default.
• W3156196068 creator A5012523052 @default.
• W3156196068 creator A5027686620 @default.
• W3156196068 creator A5041567418 @default.
• W3156196068 creator A5042866937 @default.
• W3156196068 creator A5049566648 @default.
• W3156196068 creator A5053879401 @default.
• W3156196068 creator A5069710974 @default.
• W3156196068 creator A5080719369 @default.
• W3156196068 creator A5083946982 @default.
• W3156196068 date "2022-06-01" @default.
• W3156196068 modified "2023-10-10" @default.
• W3156196068 title "Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications" @default.
• W3156196068 cites W1666336517 @default.
• W3156196068 cites W1817267053 @default.
• W3156196068 cites W1955665456 @default.
• W3156196068 cites W1973457617 @default.
• W3156196068 cites W1989102171 @default.
• W3156196068 cites W2006096283 @default.
• W3156196068 cites W2021204548 @default.
• W3156196068 cites W2023363651 @default.
• W3156196068 cites W2028094999 @default.
• W3156196068 cites W2028502203 @default.
• W3156196068 cites W2036564586 @default.
• W3156196068 cites W2063948766 @default.
• W3156196068 cites W2095417127 @default.
• W3156196068 cites W2102848905 @default.
• W3156196068 cites W2118287630 @default.
• W3156196068 cites W2120109874 @default.
• W3156196068 cites W2122545833 @default.
• W3156196068 cites W2143100862 @default.
• W3156196068 cites W2145661921 @default.
• W3156196068 cites W2194775991 @default.
• W3156196068 cites W2221898772 @default.
• W3156196068 cites W2342591535 @default.
• W3156196068 cites W2411162855 @default.
• W3156196068 cites W2502805798 @default.
• W3156196068 cites W2521464619 @default.
• W3156196068 cites W2561981131 @default.
• W3156196068 cites W2562469482 @default.
• W3156196068 cites W2567324963 @default.
• W3156196068 cites W2570286783 @default.
• W3156196068 cites W2575552683 @default.
• W3156196068 cites W2589409328 @default.
• W3156196068 cites W2606665325 @default.
• W3156196068 cites W2621365959 @default.
• W3156196068 cites W266124145 @default.
• W3156196068 cites W2787687030 @default.
• W3156196068 cites W2789057179 @default.
• W3156196068 cites W2789235061 @default.
• W3156196068 cites W2849179291 @default.
• W3156196068 cites W2895614609 @default.
• W3156196068 cites W2900299818 @default.
• W3156196068 cites W2903617078 @default.
• W3156196068 cites W2909173434 @default.
• W3156196068 cites W2943358380 @default.
• W3156196068 cites W2961602927 @default.
• W3156196068 cites W2962793481 @default.
• W3156196068 cites W2963052998 @default.
• W3156196068 cites W2971561076 @default.
• W3156196068 cites W2977883299 @default.
• W3156196068 cites W2988064907 @default.
• W3156196068 cites W2995209110 @default.
• W3156196068 cites W2997337740 @default.
• W3156196068 cites W3000974005 @default.
• W3156196068 cites W3005146403 @default.
• W3156196068 cites W3021605584 @default.
• W3156196068 cites W3030790048 @default.
• W3156196068 cites W3033511014 @default.
• W3156196068 cites W3038972077 @default.
• W3156196068 cites W3048834235 @default.
• W3156196068 cites W3097353281 @default.
• W3156196068 cites W3100715778 @default.
• W3156196068 cites W3104085283 @default.
• W3156196068 cites W3112701542 @default.
• W3156196068 cites W3119458123 @default.
• W3156196068 cites W3134475970 @default.
• W3156196068 cites W3145064452 @default.
• W3156196068 cites W3164573547 @default.
• W3156196068 cites W3164617602 @default.
• W3156196068 cites W3171747481 @default.
• W3156196068 cites W3199771792 @default.
• W3156196068 cites W341325887 @default.
• W3156196068 cites W4205923499 @default.
• W3156196068 cites W4206077053 @default.
• W3156196068 cites W4213328656 @default.
• W3156196068 cites W4250145052 @default.
• W3156196068 cites W989828740 @default.
• W3156196068 doi "https://doi.org/10.1109/jbhi.2022.3151741" @default.
• W3156196068 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35171783" @default.
• W3156196068 hasPublicationYear "2022" @default.
• W3156196068 type Work @default.
• W3156196068 sameAs 3156196068 @default.
• W3156196068 citedByCount "13" @default.
• W3156196068 countsByYear W31561960682021 @default.
• W3156196068 countsByYear W31561960682022 @default.
• W3156196068 countsByYear W31561960682023 @default.

• next