Dokument: Reproducibility and across-site transferability of an improved deep learning approach for aneurysm detection and segmentation in time-of-flight MR-angiograms
| Titel: | Reproducibility and across-site transferability of an improved deep learning approach for aneurysm detection and segmentation in time-of-flight MR-angiograms | |||||||
| URL für Lesezeichen: | https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=68295 | |||||||
| URN (NBN): | urn:nbn:de:hbz:061-20250127-112456-6 | |||||||
| Kollektion: | Publikationen | |||||||
| Sprache: | Englisch | |||||||
| Dokumententyp: | Wissenschaftliche Texte » Artikel, Aufsatz | |||||||
| Medientyp: | Text | |||||||
| Autoren: | Vach, Marius [Autor] Wolf, Luisa [Autor] Weiss, Daniel [Autor] Ivan, Vivien Lorena [Autor] Hofmann, Björn B. [Autor] Himmelspach, Ludmila [Autor] Caspers, Julian [Autor] Rubbert, Christian [Autor] | |||||||
| Dateien: |
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| Stichwörter: | Intracranial aneurysm, Reproducibility, Magnetic resonance angiography, Convolutional neural network, Deep learning | |||||||
| Beschreibung: | This study aimed to (1) replicate a deep-learning-based model for cerebral aneurysm segmentation in TOF-MRAs, (2) improve the approach by testing various fully automatic pre-processing pipelines, and (3) rigorously validate the model’s transferability on independent, external test-datasets. A convolutional neural network was trained on 235 TOF-MRAs acquired on local scanners from a single vendor to segment intracranial aneurysms. Different pre-processing pipelines including bias field correction, resampling, cropping and intensity-normalization were compared regarding their effect on model performance. The models were tested on independent, external same-vendor and other-vendor test-datasets, each comprised of 70 TOF-MRAs, including patients with and without aneurysms. The best-performing model achieved excellent results on the external same-vendor test-dataset, surpassing the results of the previous publication with an improved sensitivity (0.97 vs. ~ 0.86), a higher Dice score coefficient (DSC, 0.60 ± 0.25 vs. 0.53 ± 0.31), and an improved false-positive rate (0.87 ± 1.35 vs. ~ 2.7 FPs/case). The model further showed excellent performance in the external other-vendor test-datasets (DSC 0.65 ± 0.26; sensitivity 0.92, 0.96 ± 2.38 FPs/case). Specificity was 0.38 and 0.53, respectively. Raising the voxel-size from 0.5 × 0.5×0.5 mm to 1 × 1×1 mm reduced the false-positive rate seven-fold. This study successfully replicated core principles of a previous approach for detecting and segmenting cerebral aneurysms in TOF-MRAs with a robust, fully automatable pre-processing pipeline. The model demonstrated robust transferability on two independent external datasets using TOF-MRAs from the same scanner vendor as the training dataset and from other vendors. These findings are very encouraging regarding the clinical application of such an approach. | |||||||
| Rechtliche Vermerke: | Originalveröffentlichung:
Vach, M., Wolf, L., Weiß, D. A., Ivan, V. L., Hofmann, B. B., Himmelspach, L., Caspers, J., & Rubbert, C. (2024). Reproducibility and across-site transferability of an improved deep learning approach for aneurysm detection and segmentation in time-of-flight MR-angiograms. Scientific Reports, 14, Article 18749. https://doi.org/10.1038/s41598-024-68805-w | |||||||
| Lizenz: |  Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz | |||||||
| Fachbereich / Einrichtung: | Medizinische Fakultät | |||||||
| Dokument erstellt am: | 27.01.2025 | |||||||
| Dateien geändert am: | 27.01.2025 |
