ConvNets for automatic detection of polyglutamine SCAs from brain MRIs: state of the art applications.

Polyglutamine spinocerebellar ataxias (polyQ SCAs) are a group of neurodegenerative diseases, clinically and genetically heterogeneous, characterized by loss of balance and motor coordination due to dysfunction of the cerebellum and its connections. The diagnosis of each type of polyQ SCA, alongside with genetic tests, includes medical images analysis, and its automation may help specialists to distinguish between each type. Convolutional neural networks (ConvNets or CNNs) have been recently used for medical image processing, with outstanding results. In this work, we present the main clinical and imaging features of polyglutamine SCAs, and the basics of CNNs. Finally, we review studies that have used this approach to automatically process brain medical images and may be applied to SCAs detection. We conclude by discussing the possible limitations and opportunities of using ConvNets for SCAs diagnose in the future.

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

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.

Tomografía computarizada y sólidos virtuales para obtener modelos biomecánicos computacionales / Computed tomography and virtual solids to obtain computational biomechanical models

Uno de los padecimientos más comunes de los huesos es la fractura, definida como la pérdida de la continuidad del material óseo. Implantes y prótesis son utilizados para tratar algunas de ellas. Actualmente, antes de usar uno de estos dispositivos, se prueban modelos virtuales de los mismos utilizando un programa de diseño asistido por computadora. Para dichas pruebas, se requieren también modelos virtuales de los huesos. Los modelos óseos son obtenidos aplicando técnicas de segmentación de imágenes a las tomografías computarizadas (TC). Este trabajo presenta un procedimiento para la obtención de modelos biomecánicos hueso-implante a partir de las TCs y sólidos virtuales, teniendo en cuenta la estructura real de los huesos, compuesta de tejido cortical y trabecular. Para realizar los análisis de verificación del procedimiento se utilizó un modelo de un implante DHS y de una prótesis de cadera(AU)

One of the most common bone conditions is fracture, defined as the loss of the continuity of the bone material. Implants and prostheses are used to treat some of them. Currently, before using one of these devices, virtual models are tested using a computer-aided design program. For these tests, virtual models of the bones are also required. Bone models are obtained by applying image segmentation techniques to computed tomography (CT). This paper presents a procedure for obtaining biomechanical bone-implant models from the CTs and virtual solids, taking into account the real structure of the bones, composed of cortical and trabecular tissue. A DHS implant model and a hip prosthesis were used to perform the procedure verification tests(AU)