Automatic Segmentation and ROI detection in cardiac MRI of Cardiomyopathy using q-Sigmoid as preprocessing step

The growth of data volume is a reality in all such as segments of our society. Despite of personalized experiences, accurate and fast information, new challenges had arisen. For healthcare industry, for example, it was noted an increase of radiologists workload which may cause visual fatigue and, consequently, errors during diagnosis. intelligence was pointed as an option to support Artificial physicians analysis and reduce the visual fatigue. Thus, this paper focus on the proposal of a novel strategy to enhance cardiac magnetic resonance images (MRI) and automatically detect their region-of-interest (ROI) using a convolutional neural network (CNN). Our object of study is the disease of Cardiomyopathy and the desirable ROI is the left ventricle from axial slices. We evaluated q-Sigmoid performance using it as a preprocessing step and validate the results through modified CNNs: U-Net and ResNet.