Abstract
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.
Highlights
The human cerebellum plays an essential role in critical tasks, like motor coordination and cognition, and is related to other functions, e.g., language and emotions [1,2]
It may be observed that the scores obtained are competitive with those obtained in our dataset, as mean dice score (DSC) and overlap coefficient (OC) are 0.951 and 0.983, respectively
The same occurs with the scores for model M3 (Table A9), with 0.964 and 0.982 as mean DSC and OC, respectively
Summary
The human cerebellum plays an essential role in critical tasks, like motor coordination and cognition, and is related to other functions, e.g., language and emotions [1,2]. Diseases like spinocerebellar ataxias (SCAs), multiple sclerosis (MD), or Alzheimer’s disease (AD), are known to cause damage in the cerebellum, conducting patients to progressive loss in such functions and, in some cases, to premature death [3]. Knowing how to observe such fissures allows specialists to obtain some important characteristics from the patients, like volume loss related to the specific disease. Segmentation of magnetic resonance imaging (MRI) is often performed, and clinicians make research with several patients, with the goal of learning more about the disease, and how to treat it better. Computational tools are required for performing those processes automatically
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