Abstract #3055

Deep Learning with Anatomical Attention Mechanism for Distinguishing Parkinson’s Disease from Normal Controls in MR imaging

Yida Wang¹, Naying He², Chenglong Wang¹, Yan Li², Zhijia Jin², Xiance Zhao³, Ewart Mark Haacke^2,4, Fuhua Yan², and Guang Yang¹

¹Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China, ²Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, ³Philips Healthcare, Shanghai, China, ⁴Department of Biomedical Engineering, Wayne State University, Detroit, MI, United States

Synopsis

We proposed an automatic cascaded framework based on deep learning to segment deep brain nuclei and distinguish Parkinson’s disease from normal controls using quantitative susceptibility mapping (QSM) images. A 3D CA-Net model integrating channel attention, spatial attention and scale attention module was utilized to segment 5 brain nuclei from QSM and T1W data. Then, the QSM images and the segmented brain nuclei ROIs were fed into the SE-ResNeXt50 with anatomical attention mechanism to get the predicted PD probability. The proposed method provided good interpretability and achieved AUC values of 0.97 and 0.90 on training and testing cohort, respectively.

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