Convolutional Neural Networks for Segmenting Cerebellar Fissures from Magnetic Resonance Imaging.

Autor: Cabeza-Ruiz R; CAD/CAM Study Centre, University of Holguín, Holguín 80100, Cuba., Velázquez-Pérez L; Cuban Academy of Sciences, Havana 10200, Cuba.; Centre for the Research and Rehabilitation of Hereditary Ataxias, Holguín 80100, Cuba., Linares-Barranco A; Robotics and Tech. of Computers Lab, University of Seville, 41012 Seville, Spain.; Escuela Politécnica Superior (EPS), University of Seville, 41011 Seville, Spain.; Smart Computer Systems Research and Engineering Lab (SCORE), Research Institute of Computer Engineering (I3US), University of Seville, 41012 Seville, Spain., Pérez-Rodríguez R; CAD/CAM Study Centre, University of Holguín, Holguín 80100, Cuba.; Cuban Academy of Sciences, Havana 10200, Cuba.
Jazyk: angličtina
Zdroj: Sensors (Basel, Switzerland) [Sensors (Basel)] 2022 Feb 10; Vol. 22 (4). Date of Electronic Publication: 2022 Feb 10.
DOI: 10.3390/s22041345
Abstrakt: 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.
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje