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- W2928951869 abstract "Preterm birth is connected to impairments and altered brain growth. Compared to their term born peers, preterm infants have a higher risk of behavioral and cognitive problems since most part of their brain development is in extra-uterine conditions. This paper presents different deep learning approaches with the objective of quantifying the volumes of 8 brain tissues and 5 other image-based descriptors that quantify the state of brain development. Two datasets were used: one with 86 MR brain images of patients around 30 weeks PMA and the other with 153 patients around 40 weeks PMA. Two approaches were evaluated: (1) using the full image as 3D input and (2) using multiple image slices as 3D input, both achieving promising results. A second study, using a dataset of MR brain images of rats, was also performed to assess the performance of this method with other brains. A 2D approach was used to estimate the volumes of 3 rat brain tissues." @default.
- W2928951869 created "2019-04-11" @default.
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- W2928951869 date "2019-01-01" @default.
- W2928951869 modified "2023-09-24" @default.
- W2928951869 title "Convolutional Neural Network-Based Regression for Quantification of Brain Characteristics Using MRI" @default.
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- W2928951869 doi "https://doi.org/10.1007/978-3-030-16184-2_55" @default.
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