DOMAIN GENERALIZATION IN FETAL BRAIN MRI SEGMENTATION WITH MULTI-RECONSTRUCTION AUGMENTATION

被引：0
|
作者：
de Dumast, Priscille [1 ,2 ,3 ]
Cuadra, Meritxell Bach [1 ,2 ,3 ]
机构：
[1] Lausanne Univ Hosp CHUV, Dept Radiol, Lausanne, Switzerland
[2] Univ Lausanne UNIL, Lausanne, Switzerland
[3] CIBM Ctr Biomed Imaging, Lausanne, Switzerland
来源：
2023 IEEE 20TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, ISBI | 2023年
基金：
瑞士国家科学基金会;
关键词：
Magnetic resonance imaging (MRI); Super-resolution (SR) reconstruction; Automated fetal brain tissue segmentation; Data augmentation; Domain adaptation;
D O I：
10.1109/ISBI53787.2023.10230402
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Quantitative analysis of in utero human brain development is crucial for abnormal characterization. Magnetic resonance image (MRI) segmentation is therefore an asset for quantitative analysis. However, the development of automated segmentation methods is hampered by the scarce availability of fetal brain MRI annotated datasets and the limited variability within these cohorts. In this context, we propose to leverage the power of fetal brain MRI super-resolution (SR) reconstruction methods to generate multiple reconstructions of a single subject with different parameters, thus as an efficient tuning-free data augmentation strategy. Overall, the latter significantly improves the generalization of segmentation methods over SR pipelines.
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页数：5
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