Dr. Dianfu Li Profile

Dr. Dianfu Li

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Proceedings Article | 15 March 2019 Paper

A learning-based automatic segmentation method on left ventricle in SPECT imaging

Tonghe Wang, Yang Lei, Haipeng Tang, Joseph Harms, Cheng Wang, Tian Liu, Walter Curran, Weihua Zhou, Dianfu Li, Xiaofeng Yang

Proceedings Volume 10953, 109531M (2019) https://doi.org/10.1117/12.2512554

KEYWORDS: Image segmentation, Single photon emission computed tomography, Magnetic resonance imaging, Gated imaging, Machine learning, Computed tomography, Binary data, Heart, Cancer, 3D image processing, Myocardium, Education and training, Network architectures, Medical imaging

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Gated myocardial perfusion SPECT (MPS) is widely used to assess the left ventricular (LV) function. Its performance relies on the accuracy of segmentation on LV cavity. We propose a novel machine-learningbased method to automatically segment LV cavity and measure its volume in gated MPS imaging. To perform end-to-end segmentation, a multi-label V-Net is used to build the network architecture. The network segments a probability map for each heart contour (epicardium, endocardium and myocardium). To evaluate the accuracy of segmentation, we retrospectively investigated gated MPS images from 32 patients. The LV cavity was automatically segmented by the proposed method, and compared to manually outlined contours, which were taken as the ground truth. The derived LV cavity volumes were extracted from both ground truth and results of proposed method for comparison and evaluation. The mean DSC, sensitivity and specificity of the contours delineated by our method are all above 0.9 among all 32 patients and 8 phases. The correlation coefficient of the LV cavity volume between ground truth and results produced by the proposed method is 0.910±0.061, and the mean relative error of LV cavity volume among all patients and all phases is - 1.09±3.66 %. These results indicate that the proposed method accurately quantifies the changes in LV cavity volume during the cardiac cycle. It also demonstrates the potential of our learning-based segmentation methods in gated MPS imaging for clinical use.

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