التفاصيل البيبلوغرافية
العنوان: |
Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice. |
المؤلفون: |
Boaro, Alessandro, Kaczmarzyk, Jakub R., Kavouridis, Vasileios K., Harary, Maya, Mammi, Marco, Dawood, Hassan, Shea, Alice, Cho, Elise Y., Juvekar, Parikshit, Noh, Thomas, Rana, Aakanksha, Ghosh, Satrajit, Arnaout, Omar |
المصدر: |
Scientific Reports; 9/14/2022, Vol. 12 Issue 1, p1-11, 11p |
مستخلص: |
Accurate brain meningioma segmentation and volumetric assessment are critical for serial patient follow-up, surgical planning and monitoring response to treatment. Current gold standard of manual labeling is a time-consuming process, subject to inter-user variability. Fully-automated algorithms for meningioma segmentation have the potential to bring volumetric analysis into clinical and research workflows by increasing accuracy and efficiency, reducing inter-user variability and saving time. Previous research has focused solely on segmentation tasks without assessment of impact and usability of deep learning solutions in clinical practice. Herein, we demonstrate a three-dimensional convolutional neural network (3D-CNN) that performs expert-level, automated meningioma segmentation and volume estimation on MRI scans. A 3D-CNN was initially trained by segmenting entire brain volumes using a dataset of 10,099 healthy brain MRIs. Using transfer learning, the network was then specifically trained on meningioma segmentation using 806 expert-labeled MRIs. The final model achieved a median performance of 88.2% reaching the spectrum of current inter-expert variability (82.6–91.6%). We demonstrate in a simulated clinical scenario that a deep learning approach to meningioma segmentation is feasible, highly accurate and has the potential to improve current clinical practice. [ABSTRACT FROM AUTHOR] |
|
Copyright of Scientific Reports is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
قاعدة البيانات: |
Complementary Index |