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Title:

LST-AI: A deep learning ensemble for accurate MS lesion segmentation.

Document type:
Journal Article
Author(s):
Wiltgen, Tun; McGinnis, Julian; Schlaeger, Sarah; Kofler, Florian; Voon, CuiCi; Berthele, Achim; Bischl, Daria; Grundl, Lioba; Will, Nikolaus; Metz, Marie; Schinz, David; Sepp, Dominik; Prucker, Philipp; Schmitz-Koep, Benita; Zimmer, Claus; Menze, Bjoern; Rueckert, Daniel; Hemmer, Bernhard; Kirschke, Jan; Mühlau, Mark; Wiestler, Benedikt
Abstract:
Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt. As an open-source tool, we present LST-AI, an advanced deep learning-based extension of LST that consists of an ensemble of three 3D U...     »
Journal title abbreviation:
Neuroimage Clin
Year:
2024
Journal volume:
42
Fulltext / DOI:
doi:10.1016/j.nicl.2024.103611
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/38703470
TUM Institution:
Institut für KI und Informatik in der Medizin (Prof. Rückert)
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