Szczegóły publikacji
Opis bibliograficzny
Automatic aorta segmentation with heavily augmented, high-resolution 3-D ResUNet: contribution to the SEG.A challenge / Marek WODZIŃSKI, Henning Müller // W: Segmentation of the Aorta : towards the automatic segmentation, modeling, and meshing of the aortic vessel tree from multicenter acquisition : first challenge, SEG.A. 2023 : held in conjunction with MICCAI 2023 : Vancouver, BC, Canada, October 8, 2023 : proceedings / eds. Antonio Pepe, Gian Marco Melito, Jan Egger. — Cham : Springer Nature Switzerland, cop. 2024. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; vol. 14539). — ISBN: 978-3-031-53240-5; e-ISBN: 978-3-031-53241-2. — S. 42–54. — Bibliogr., Abstr. — Publikacja dostępna online od: 2024-02-10. — M. Wodziński - dod. afiliacja: University of Applied Sciences Western Switzerland (HES-SO Valais), Sierre, Switzerland
Autorzy (2)
- AGHWodziński Marek
- Müller Henning
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 152032 |
|---|---|
| Data dodania do BaDAP | 2024-04-05 |
| DOI | 10.1007/978-3-031-53241-2_4 |
| Rok publikacji | 2024 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | Medical Image Computing and Computer-Assisted Intervention 2023 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Automatic aorta segmentation from 3-D medical volumes is an important yet difficult task. Several factors make the problem challenging, e.g. the possibility of aortic dissection or the difficulty with segmenting and annotating the small branches. This work presents a contribution by the MedGIFT team to the SEG.A challenge organized during the MICCAI 2023 conference. We propose a fully automated algorithm based on deep encoder-decoder architecture. The main assumption behind our work is that data preprocessing and augmentation are much more important than the deep architecture, especially in low data regimes. Therefore, the solution is based on a variant of traditional convolutional U-Net. The proposed solution achieved a Dice score above 0.9 for all testing cases with the highest stability among all participants. The method scored 1st, 4th, and 3rd in terms of the clinical evaluation, quantitative results, and volumetric meshing quality, respectively. We freely release the source code, pretrained model, and provide access to the algorithm on the Grand-Challenge platform.
