A myocardial extraction method using deep learning for 99mTc myocardial perfusion SPECT images: A basic study to reduce the effects of extra-myocardial activity.

Aim: The purpose of this study was to automatically extract myocardial regions from transaxial single-photon emission computed tomography (SPECT) images using deep learning to reduce the effects of extracardiac activity, which has been problematic in cardiac nuclear imaging.
Method: Myocardial region extraction was performed using two deep neural network architectures, U-Net and U-Net ++, and 694 myocardial SPECT images manually labeled with myocardial regions were used as the training data. In addition, a multi-slice input method was introduced during the learning session while taking the relationships to adjacent slices into account. Accuracy was assessed using Dice coefficients at both the slice and pixel levels, and the most effective number of input slices was determined.
Results: The Dice coefficient was 0.918 at the pixel level, and there were no false positives at the slice level using U-Net++ with 9 input slices.
Conclusion: The proposed system based on U-Net++ with multi-slice input provided highly accurate myocardial region extraction and reduced the effects of extracardiac activity in myocardial SPECT images.
(Copyright © 2021. Published by Elsevier Ltd.)