학술논문

Brain Segmentation from Super-Resolved Magnetic Resonance Images
Document Type
Conference
Source
2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME) Advances in Biomedical Engineering (ICABME), 2019 Fifth International Conference on. :1-4 Oct, 2019
Subject
Bioengineering
Image segmentation
Two dimensional displays
Magnetic resonance imaging
Spatial resolution
Aging
Three-dimensional displays
Single image super-resolution
structural MRI
segmentation
cerebral aging
marmoset
ICV
Language
ISSN
2377-5696
Abstract
The objective of this work is to investigate the ability of a 2D super resolution (SR) technique in 3D restoration and enhancement of brain magnetic resonance images to facilitate the study of cerebral aging bio-markers. The SR method exploits the joint properties of the system point spread function and sub-sampling operators to derive a fast algorithm. Brain images of the common marmoset, Callithrix jacchus, acquired at different ages are used in this study. The evaluation of the final outcome of our method is done by computing the intracranial volume from the segmentation of the brain compartments: gray matter, white matter and cerebrospinal fluid. Results show that the deblurring of the images improves the segmentation process with respect to the ground truth. However, super resolution leads to the best quantification of the intracranial volume when compared to the deblurred and the original images. Therefore, despite its sub-optimality, the 2D SR method provides reliable results for improving the quality of the images used in the study of aging in terms of precision of reconstruction and computational time.