A myocardial extraction method using deep learning for 99mTc myocardial perfusion SPECT images: A basic study to reduce the effects of extra-myocardial activity
A myocardial extraction method using deep learning for 99mTc myocardial perfusion SPECT images: A basic study to reduce the effects of extra-myocardial activity
复制标题
利用深度学习获取 99mTc 心肌灌注 SPECT 图像的心肌提取方法：减少心肌外活动影响的基础研究
DOI：
10.1016/j.compbiomed.2021.105164
复制
发表时间：
2021
影响因子：
7.7
通讯作者：
Hiroto Yoneyama
中科院分区：
文献类型：
--
作者：
Akihiro Kikuchi ;Naofumi Wada;Takashi Kawakami;Kenichi Nakajima;Hiroto Yoneyama
目的本研究的目的是利用深度学习从经轴单光子发射计算机断层扫描（SPECT）图像中自动提取心肌区域，以减少心外活动的影响，这在心脏核成像中一直是个问题。方法：采用U-Net和U-Net++两种深度神经网络架构进行心肌区域提取，并以694张手动标记心肌区域的心肌SPECT图像作为训练数据。此外，在学习过程中引入了多切片输入方法，同时考虑了与相邻切片的关系。使用切片和像素级别的 Dice 系数评估准确性，并确定输入切片的最有效数量。结果：像素级别的 Dice 系数为 0.918，使用具有 9 个输入切片的 U-Net++ 在切片级别没有出现误报。结论：所提出的基于U-Net++的多切片输入系统提供了高精度的心肌区域提取，并减少了心肌SPECT图像中心外活动的影响。
AimThe 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.