Automatic in vivo cell detection in MRI

Abstract

Due to recent advances in cell-based therapies, non-invasive monitoring of in vivo cells in MRI is gaining enormous interest. However, to date, the monitoring and analysis process is conducted manually and is extremely tedious, especially in the clinical arena. Therefore, this paper proposes a novel computer vision-based learning approach that creates superpixel-based 3D models for candidate spots in MRI, extracts a novel set of superfern features, and utilizes a partition-based Bayesian classifier ensemble to distinguish spots from non-spots. Unlike traditional ferns that utilize pixel-based differences, superferns exploit superpixel averages in computing difference-based features despite the absence of any order in superpixel arrangement. To evaluate the proposed approach, we develop the first labeled database with a total of more than 16 thousand labels on five in vivo and four in vitro MRI scans. Experimental results show the superiority of our approach in comparison to the two most relevant baselines. To the best of our knowledge, this is the first study to utilize a learning-based methodology for in vivo cell detection in MRI.