Analysis on SPECT myocardial perfusion imaging with a tool derived from dynamic programming to deep learning
College of Computing
Our study tries to propose a method that can rapidly process and calibrate data sets through our previous dynamic programming (DP) tool, manually adjust error-heavy labels, and then train neural network models to accurately segment gated SPECT MPI images. Lastly, LV contours can be accurately determined on the gated SPECT MPI images and corresponding LV volumes can also be evaluated. First, we used our previous DP-based tool to coarsely determine the myocardial profiles on SPECT MPI images. In order to obtain a more accurate LV volumes and LVEF, it was also necessary to manually adjust the profiles that do not meet the clinical requirements. Then the results of manual adjustment were used as the ground truths to train the end-to-end full convolution neural networks by deep learning (DL). The method proposed in this paper was evaluated with the Dice similarity coefficient and Hausdorff distance, and the results showed that the proposed method had a high similarity with the ground truth. The LV volumes and LVEFs estimated by different methods were calculated separately and compared each other, and the experimental results showed that the performance of the proposed DL-based method was superior to that of the DP-based method and close to that of QGS program. As a quantitative analysis tool, the proposed DL-based method is fully automatic and more accurate in the evaluation of LV volumes and LVEFs, so it is expected that the DL-based method may have a wide application prospect in clinical practices.
Analysis on SPECT myocardial perfusion imaging with a tool derived from dynamic programming to deep learning.
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