Szczegóły publikacji
Opis bibliograficzny
Invnet: a deep learning approach to invert complex deformation fields / Marek WODZIŃSKI, Henning Müller // W: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) [Dokument elektroniczny] : April 13-16, 2021, Nice, France. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, cop. 2021. — (Proceedings (International Symposium on Biomedical Imaging) ; ISSN 1945-7928). — Dod. ISBN: 978-1-6654-1245-2, 978-1-6654-2947-4. — e-ISBN: 978-1-6654-1246-9. — S. 1302-1305. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 1305, Abstr.
Autorzy (2)
- AGHWodziński Marek
- Müller Henning
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 134591 |
|---|---|
| Data dodania do BaDAP | 2021-06-16 |
| Tekst źródłowy | URL |
| DOI | 10.1109/ISBI48211.2021.9433904 |
| Rok publikacji | 2021 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Czasopismo/seria | Proceedings (International Symposium on Biomedical Imaging) |
Abstract
Inverting a deformation field is a crucial part for numerous image registration methods and has an important impact on the final registration results. There are methods that work well for small and relatively simple deformations. However, a problem arises when the deformation field consists of complex and large deformations, potentially including folding. For such cases, the state-of-the-art methods fail and the inversion results are unpredictable. In this article, we propose a deep network using the encoder-decoder architecture to improve the inverse calculation. The network is trained using deformations randomly generated using various transformation models and their compositions, with a symmetric inverse consistency error as the cost function. The results are validated using synthetic deformations resembling real ones, as well as deformation fields calculated during registration of real histology data. We show that the proposed method provides an approximate inverse with a lower error than the current state-of-the-art methods.
