Szczegóły publikacji
Opis bibliograficzny
Semi-supervised multilevel symmetric image registration method for magnetic resonance whole brain images / Marek WODZIŃSKI // W: Biomedical image registration, domain generalisation and out-of-distribution analysis : MICCAI 2021 challenges: MIDOG 2021, MOOD 2021, and Learn2Reg 2021 held in conjunction with MICCAI 2021 : Strasbourg, France, September 27 – October 1, 2021 : proceedings / eds. Marc Aubreville, David Zimmerer, Mattias Heinrich. — Cham : Springer Nature, cop. 2022. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 13166. Image Processing, Computer Vision, Pattern Recognition, and Graphics). — ISBN: 978-3-030-97280-6; e-ISBN: 978-3-030-97281-3. — S. 186–191. — Bibliogr., Abstr. — Publikacja dostępna online od: 2022-03-02. — Dod. afiliacja autora: University of Applied Sciences Western Switzerland (HES-SO Valais), Information Systems Institute, Sierre, Switzerland
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 139338 |
|---|---|
| Data dodania do BaDAP | 2022-03-03 |
| DOI | 10.1007/978-3-030-97281-3_27 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | Medical Image Computing and Computer-Assisted Intervention 2021 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
This paper describes a contribution to the second edition of the Learn2Reg challenge organized jointly with the MICCAI 2021 conference, more specifically, to the OASIS MRI task that is related to the registration of whole brain magnetic resonance images. The proposed algorithm is a multi-level, learning-based, and semi-supervised procedure. The algorithm consists of a multi-level input/output U-Net-like architecture trained with additional symmetry constraints. The method was ranked as the third-best for the brain registration task in terms of the combined challenge evaluation criteria.
