  The importance of imaging exams, such as Magnetic Resonance Imaging (MRI),
for the diagnostic and follow-up of pediatric pathologies and the assessment of
anatomical structures' development has been increasingly highlighted in recent
times. Manual analysis of MRIs is time-consuming, subjective, and requires
significant expertise. To mitigate this, automatic techniques are necessary.
Most techniques focus on adult subjects, while pediatric MRI has specific
challenges such as the ongoing anatomical and histological changes related to
normal development of the organs, reduced signal-to-noise ratio due to the
smaller bodies, motion artifacts and cooperation issues, especially in long
exams, which can in many cases preclude common analysis methods developed for
use in adults. Therefore, the development of a robust technique to aid in
pediatric MRI analysis is necessary. This paper presents the current
development of a new method based on the learning and matching of structural
relational graphs (SRGs). The experiments were performed on liver MRI sequences
of one patient from ICr-HC-FMUSP, and preliminary results showcased the
viability of the project. Future experiments are expected to culminate with an
application for pediatric liver substructure and brain tumor segmentation.

Computer Vision and Pattern Recognition