Abstract #2316

Automatic in vivo detection of transplanted cells in MRI using transfer learning paradigm

Muhammad Jamal Afridi¹, Arun Ross², Steven Hoffman², and Erik M Shapiro³

¹Department of Radiology and Department of Computer Science, Michigan State University, East Lansing, MI, United States, ²Department of Computer Science, Michigan State University, East Lansing, MI, United States, ³Department of Radiology, Michigan State University, East Lansing, MI, United States

Despite advances in machine learning and computer-vision, many MRI studies rely on tedious manual procedures for quantifying imaging features, i.e. cell numbers, contrast area etc. Development of intelligent, automatic tools for quantifying imaging data requires large scale data for their training and tuning, which in the clinical arena is challenging to obtain. Here, we present an approach that obviates the need for large scale data collection to develop an intelligent and automatic tool for single cell detection in MRI. Our strategy achieves 91.3% accuracy for in vivo cell detection in MRI despite using only 40% of the data for training.

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