Brain Segmentation from Super-Resolved Magnetic Resonance Images

Farah Bazzi (1) , Juan D Dios Rodriguez-Callejas (2) , Caroline Fonta (2) , Ahmad Diab (3, 4) , Hassan Amoud (5, 4) , Omar Falou (6, 4, 7) , Muriel Mescam (2) , Adrian Basarab (8, 1) , Denis Kouamé (8)

1 UT3 - Université Toulouse III - Paul Sabatier
2 CERCO - Centre de recherche cerveau et cognition
3 LU / ULB - الجامعة اللبنانية [بيروت] = Lebanese University [Beirut] = Université libanaise [Beyrouth]
4 Azm Center for Biotechnology Research
5 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
6 Doctoral School of Sciences and Technology [Lebanese University]
7 AUCE - American University of Culture & Education
8 IRIT-MINDS - CoMputational imagINg anD viSion

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.

Mots clés: Single image super-resolution; cerebral aging; structural MRI; segmentation; marmoset; ICV

Domaines: Traitement du signal et de l'image [eess.SP]; Imagerie médicale; Imagerie

Farah Bazzi, Juan D Dios Rodriguez-Callejas, Caroline Fonta, Ahmad Diab, Hassan Amoud, et al.. Brain Segmentation from Super-Resolved Magnetic Resonance Images. 5th International Conference on Advances in Biomedical Engineering (ICABME 2019), Oct 2019, Tripoli, Lebanon. ⟨10.1109/ICABME47164.2019.8940281⟩. ⟨hal-02875503⟩