Heart is one of the vital organs of human body. A minor dysfunction of heart even for a short time interval can be fatal, therefore, efficient monitoring of its physiological state is essential for the patients with cardiovascular diseases. In the recent past, various computer assisted medical imaging systems have been proposed for the segmentation of the organ of interest. However, for the segmentation of heart using MRI, only few methods have been proposed each with its own merits and demerits. For further advancement in this area of research, we analyze automated heart segmentation methods for magnetic resonance images. The analysis are based on deep learning methods that processes a full MR scan in a slice by slice fashion to predict desired mask for heart region. We design two encoder decoder type fully convolutional neural network models
@article{arxiv.1911.09332,
title = {Heart Segmentation From MRI Scans Using Convolutional Neural Network},
author = {Shakeel Muhammad Ibrahim and Muhammad Sohail Ibrahim and Muhammad Usman and Imran Naseem and Muhammad Moinuddin},
journal= {arXiv preprint arXiv:1911.09332},
year = {2019}
}
Comments
Accepted for oral presentation at 13th International Conference - Mathematics, Actuarial, Computer Science & Statistics (MACS 13) at IoBM, Karachi, Pakistan