Szczegóły publikacji
Opis bibliograficzny
Deep learning-based framework for automatic cranial defect reconstruction and implant modeling / Marek WODZIŃSKI, Mateusz DANIOŁ, Mirosław SOCHA, Daria HEMMERLING, Maciej STANUCH, Andrzej SKALSKI // Computer Methods and Programs in Biomedicine ; ISSN 0169-2607 . — 2022 — vol. 226 art. no. 107173, s. 1–13. — Bibliogr. s. 12–13, Abstr. — Publikacja dostępna online od: 2022-10-11. — M. Wodziński - dod. afiliacje: MedApp S A., Krakow ; University of Applied Sciences Western Switzerland, Sierre, Switzerland; M. Danioł, M. Stanuch, A. Skalski - dod. afiliacja: MedApp S A., Krakow
Autorzy (6)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 143473 |
|---|---|
| Data dodania do BaDAP | 2022-11-15 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.cmpb.2022.107173 |
| Rok publikacji | 2022 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Computer Methods and Programs in Biomedicine |
Abstract
Background and Objective: This article presents a robust, fast, and fully automatic method for personalized cranial defect reconstruction and implant modeling. Methods: We propose a two-step deep learning-based method using a modified U-Net architecture to perform the defect reconstruction, and a dedicated iterative procedure to improve the implant geometry, followed by an automatic generation of models ready for 3-D printing. We propose a cross-case augmentation based on imperfect image registration combining cases from different datasets. Additional ablation studies compare different augmentation strategies and other state-of-the-art methods. Results: We evaluate the method on three datasets introduced during the AutoImplant 2021 challenge, organized jointly with the MICCAI conference. We perform the quantitative evaluation using the Dice and boundary Dice coefficients, and the Hausdorff distance. The Dice coefficient, boundary Dice coefficient, and the 95th percentile of Hausdorff distance averaged across all test sets, are 0.91, 0.94, and 1.53 mm respectively. We perform an additional qualitative evaluation by 3-D printing and visualization in mixed reality to confirm the implant's usefulness. Conclusion: The article proposes a complete pipeline that enables one to create the cranial implant model ready for 3-D printing. The described method is a greatly extended version of the method that scored 1st place in all AutoImplant 2021 challenge tasks. We freely release the source code, which together with the open datasets, makes the results fully reproducible. The automatic reconstruction of cranial defects may enable manufacturing personalized implants in a significantly shorter time, possibly allowing one to perform the 3-D printing process directly during a given intervention. Moreover, we show the usability of the defect reconstruction in a mixed reality that may further reduce the surgery time.
