Automated 3D Fetal Brain Segmentation Using an Optimized Deep Learning Approach
| Autor: | L. Zhao, J.D. Asis-Cruz, X. Feng, Y. Wu, K. Kapse, A. Largent, J. Quistorff, C. Lopez, D. Wu, K. Qing, C. Meyer, C. Limperopoulos |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | AJNR Am J Neuroradiol |
| ISSN: | 1936-959X 0195-6108 |
| DOI: | 10.3174/ajnr.a7419 |
| Popis: | BACKGROUND AND PURPOSE: MR imaging provides critical information about fetal brain growth and development. Currently, morphologic analysis primarily relies on manual segmentation, which is time-intensive and has limited repeatability. This work aimed to develop a deep learning–based automatic fetal brain segmentation method that provides improved accuracy and robustness compared with atlas-based methods. MATERIALS AND METHODS: A total of 106 fetal MR imaging studies were acquired prospectively from fetuses between 23 and 39 weeks of gestation. We trained a deep learning model on the MR imaging scans of 65 healthy fetuses and compared its performance with a 4D atlas-based segmentation method using the Wilcoxon signed-rank test. The trained model was also evaluated on data from 41 fetuses diagnosed with congenital heart disease. RESULTS: The proposed method showed high consistency with the manual segmentation, with an average Dice score of 0.897. It also demonstrated significantly improved performance (P |
| Databáze: | OpenAIRE |
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