التفاصيل البيبلوغرافية
| العنوان: |
Deep learning-based affine medical image registration for multimodal minimal-invasive image-guided interventions – A comparative study on generalizability |
| المؤلفون: |
Anika Strittmatter, Lothar R. Schad, Frank G. Zöllner |
| المصدر: |
Zeitschrift für Medizinische Physik, Vol 34, Iss 2, Pp 291-317 (2024) |
| بيانات النشر: |
Elsevier, 2024. |
| سنة النشر: |
2024 |
| المجموعة: |
LCC:Medical physics. Medical radiology. Nuclear medicine |
| مصطلحات موضوعية: |
Registration, Affine, Deep learning, Neural networks, Medical images, Multimodal data, Medical physics. Medical radiology. Nuclear medicine, R895-920 |
| الوصف: |
Multimodal image registration is applied in medical image analysis as it allows the integration of complementary data from multiple imaging modalities. In recent years, various neural network-based approaches for medical image registration have been presented in papers, but due to the use of different datasets, a fair comparison is not possible. In this research 20 different neural networks for an affine registration of medical images were implemented. The networks’ performance and the networks’ generalizability to new datasets were evaluated using two multimodal datasets - a synthetic and a real patient dataset - of three-dimensional CT and MR images of the liver. The networks were first trained semi-supervised using the synthetic dataset and then evaluated on the synthetic dataset and the unseen patient dataset. Afterwards, the networks were finetuned on the patient dataset and subsequently evaluated on the patient dataset. The networks were compared using our own developed CNN as benchmark and a conventional affine registration with SimpleElastix as baseline. Six networks improved the pre-registration Dice coefficient of the synthetic dataset significantly (p-value |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
0939-3889 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S0939388923000715; https://doaj.org/toc/0939-3889 |
| DOI: |
10.1016/j.zemedi.2023.05.003 |
| URL الوصول: |
https://doaj.org/article/42b9f402e21b48a4b6dbd5f09281ab58 |
| رقم الانضمام: |
edsdoj.42b9f402e21b48a4b6dbd5f09281ab58 |
| قاعدة البيانات: |
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