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Abstract #1540

Utilization of deep learning-based registration method in Parkinson’s disease diagnostic tool

MyeongOh Lee1, Hwan Heo1, Jeongwon Jo1, Intae Shin1, Min Seung Kim2, Seok Jong Chung3, Suk Yun Kang2, and Soohwa Song1
1Heuron Co.Ltd., Seoul, Korea, Republic of, 2Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Korea, Republic of, 3Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, Korea, Republic of

Synopsis

Keywords: Diagnosis/Prediction, Parkinson's Disease

Motivation: Image registration is a fundamental technique widely used in medical image analysis. However, the ANTs-based registration method has a limitation in terms of long processing time, which may make it difficult to apply in practice.

Goal(s): To address this limitation, we changed the registration method used in Heuron IPD, a commercialized Parkinson's disease diagnostic tool, from ANTs-based method to a deep learning-based method.

Approach: Specifically, we used TransMorph, an unsupervised medical image registration model and evaluated its performance on prospectively collected data.

Results: The execution time of registration process was reduced while maintaining diagnostic performance of Heuron IPD.

Impact: By changing the ANTs-based SyN registration method in Heuron IPD with TransMorph, a deep learning-based model, we reduced the registration processing time by approximately 32 times while maintaining the diagnostic performance of Heuron IPD.

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Keywords