Convolutional Neural Networks for Segmenting Cerebellar Fissures from Magnetic Resonance Imaging

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 4; p. 1345
• Main Authors: Cabeza-Ruiz, Robin, Velázquez-Pérez, Luis, Linares-Barranco, Alejandro, Pérez-Rodríguez, Roberto
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.02.2022; MDPI
• Subjects: Alzheimer's disease; Atrophy; Automation; Brain; Brain research; Care and treatment; cerebellar fissures; Cerebellum - diagnostic imaging; cerebellum segmentation; convolutional neural network; Datasets; Humans; Image Processing, Computer-Assisted - methods; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging equipment; Nervous system diseases; Neural networks; Neural Networks, Computer; neurodegenerative disease; Patients; Registration; Mexico; cerebellar fissures; magnetic resonance imaging; neurodegenerative disease; convolutional neural network; cerebellum segmentation
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22041345

The human cerebellum plays an important role in coordination tasks. Diseases such as spinocerebellar ataxias tend to cause severe damage to the cerebellum, leading patients to a progressive loss of motor coordination. The detection of such damages can help specialists to approximate the state of the disease, as well as to perform statistical analysis, in order to propose treatment therapies for the patients. Manual segmentation of such patterns from magnetic resonance imaging is a very difficult and time-consuming task, and is not a viable solution if the number of images to process is relatively large. In recent years, deep learning techniques such as convolutional neural networks (CNNs or convnets) have experienced an increased development, and many researchers have used them to automatically segment medical images. In this research, we propose the use of convolutional neural networks for automatically segmenting the cerebellar fissures from brain magnetic resonance imaging. Three models are presented, based on the same CNN architecture, for obtaining three different binary masks: fissures, cerebellum with fissures, and cerebellum without fissures. The models perform well in terms of precision and efficiency. Evaluation results show that convnets can be trained for such purposes, and could be considered as additional tools in the diagnosis and characterization of neurodegenerative diseases.