RT master thesis
T1 Deep Regression Model for Total Brain Volume Estimation in Preterm Neonates
T2 Modelo de regresión profunda para la estimación del volumen cerebral en neonatos prematuros
A1 Antonio, Borrero Foncubierta
A2 Ingeniería Informática
K1 Deep Learning
K1 Deep Convolutional Networks
K1 Total Brain Volume
K1 3D Ultrasound Imaging
K1 Bayesian Network Approximation
AB This study investigates the use of Deep Learning (DL) models for estimating total brain volume (TBV) in preterm infants utilizing 3D ultrasound (US) imaging data. A custom model with 3D convolution kernels and residual connections is developed, trained on 370 3D raster images from a cohort of 51 infants of postnatal age below 98 days (Mean age: 23.31 days, Standard Deviation: 21.03 days). The impact of incorporating postnatal age, either as an input parameter or a predicted variable, is briefly analyzed. Model uncertainty representations based on dropout are explored, leading to the development of a novel method centered on input transformations. The practical application of this research culminates in the creation of a graphical user interface that can load 3D US images, estimate TBV within seconds, and autonomously reject images deviating significantly from the model’s training distribution. This work contributes to the understanding of DL-based TBV estimation in preterm infants, highlighting the potential benefits of incorporating uncertainty measures and providing a user-friendly tool for medical practitioners.
YR 2023
FD 2023-09-06
LK http://hdl.handle.net/10498/31305
UL http://hdl.handle.net/10498/31305
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 06-nov-2026