Automatic brain segmentation in preterm infants with post‐hemorrhagic hydrocephalus using 3D Bayesian U‐Net

Autor: Axel Largent, Josepheen De Asis‐Cruz, Kushal Kapse, Scott D. Barnett, Jonathan Murnick, Sudeepta Basu, Nicole Andersen, Stephanie Norman, Nickie Andescavage, Catherine Limperopoulos
Rok vydání: 2022
Předmět:
Zdroj: Human Brain Mapping. 43:1895-1916
ISSN: 1097-0193
1065-9471
Popis: Post-hemorrhagic hydrocephalus (PHH) is a severe complication of intraventricular hemorrhage (IVH) in very preterm infants. PHH monitoring and treatment decisions rely heavily on manual and subjective two-dimensional measurements of the ventricles. Automatic and reliable three-dimensional (3D) measurements of the ventricles may provide a more accurate assessment of PHH, and lead to improved monitoring and treatment decisions. To accurately and efficiently obtain these 3D measurements, automatic segmentation of the ventricles can be explored. However, this segmentation is challenging due to the large ventricular anatomical shape variability in preterm infants diagnosed with PHH. This study aims to (a) propose a Bayesian U-Net method using 3D spatial concrete dropout for automatic brain segmentation (with uncertainty assessment) of preterm infants with PHH; and (b) compare the Bayesian method to three reference methods: DenseNet, U-Net, and ensemble learning using DenseNets and U-Nets. A total of 41 T
Databáze: OpenAIRE
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