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Titel:

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

Dokumenttyp:
Journal Article
Autor(en):
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...     »
Zeitschriftentitel:
Neuroimage Clin
Jahr:
2024
Band / Volume:
42
Volltext / DOI:
doi:10.1016/j.nicl.2024.103611
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/38703470
TUM Einrichtung:
Institut für KI und Informatik in der Medizin (Prof. Rückert)
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