Szczegóły publikacji
Opis bibliograficzny
Unsupervised method for intra-patient registration of brain magnetic resonance images based on objective function weighting by inverse consistency: contribution to the BraTS-Reg challenge / Marek WODZIŃSKI, Artur Jurgas, Niccolò Marini, Manfredo Atzori, Henning Müller // W: Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries : 8th international workshop, BrainLes 2022 : held in conjunction with MICCAI 2022 : Singapore, September 18, 2022 : revised selected papers / eds. Spyridon Bakas [et al.]. — Cham : Springer Nature Switzerland, cop. 2023. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 13769). — ISBN: 978-3-031-33841-0; e-ISBN: 978-3-031-33842-7. — S. 241–251. — Bibliogr., Abstr. — Publikacja dostępna online od: 2023-07-18. — M. Wodziński, A. Jurgas - dod. afiliacja: University of Applied Sciences Western Switzerland Information Systems Institute, Sierre, Switzerland
Autorzy (5)
- AGHWodziński Marek
- AGHJurgas Artur
- Marini Niccolò
- Atzori Manfredo
- Müller Henning
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 147832 |
|---|---|
| Data dodania do BaDAP | 2023-10-16 |
| DOI | 10.1007/978-3-031-33842-7_21 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | Medical Image Computing and Computer-Assisted Intervention 2022 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Registration of brain scans with pathologies is difficult, yet important research area. The importance of this task motivated researchers to organize the BraTS-Reg challenge, jointly with IEEE ISBI 2022 and MICCAI 2022 conferences. The organizers introduced the task of aligning pre-operative to follow-up magnetic resonance images of glioma. The main difficulties are connected with the missing data leading to large, nonrigid, and noninvertible deformations. In this work, we describe our contributions to both the editions of the BraTS-Reg challenge. The proposed method is based on combined deep learning and instance optimization approaches. First, the instance optimization enriches the state-of-the-art LapIRN method to improve the generalizability and fine-details preservation. Second, an additional objective function weighting is introduced, based on the inverse consistency. The proposed method is fully unsupervised and exhibits high registration quality and robustness. The quantitative results on the external validation set are: (i) IEEE ISBI 2022 edition: 1.85, and 0.86, (ii) MICCAI 2022 edition: 1.71, and 0.86, in terms of the mean of median absolute error and robustness respectively. The method scored the 1st place during the IEEE ISBI 2022 version of the challenge and the 3rd place during the MICCAI 2022. Future work could transfer the inverse consistency-based weighting directly into the deep network training.
