Convolutional neural network-based automatic heart segmentation and quantitation in (123)I-metaiodobenzylguanidine SPECT imaging.
Convolutional neural network-based automatic heart segmentation and quantitation in (123)I-metaiodobenzylguanidine SPECT imaging.
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DOI：
10.1186/s13550-021-00847-x
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发表时间：
2021-10-12
期刊：
影响因子：
3.2
通讯作者：
Kinuya S
中科院分区：
文献类型：
--
作者：
Saito S;Nakajima K;Edenbrandt L;Enqvist O;Ulén J;Kinuya S
由于 123I-间碘苄基胍（MIBG）研究中心脏区域的三维分割尚未建立，本研究旨在利用卷积神经网络（CNN）结合 123I-MIBG 单光子发射计算机断层扫描（SPECT）成像实现器官分割，自动计算心脏计数和洗脱率（WR），并与基于平面成像的传统定量进行比较。我们评估了 48 名患有心脏和神经系统疾病的患者（年龄 68.4±11.7 岁），包括慢性心力衰竭、路易体痴呆和帕金森病。所有患者均通过早期和晚期 123I-MIBG 平面和 SPECT 成像进行评估。 CNN 最初被训练为在早期和晚期 SPECT 图像上单独分割肺和肝脏。对齐分割掩模，然后训练 CNN 直接分割心脏，并使用四重交叉验证评估所有模型。计算基于 CNN 的平均心脏计数和 WR，并与使用平面参数确定的结果进行比较。基于 CNN 的 SPECT 和传统平面心脏计数通过物理时间衰减、 123I-MIBG 注射剂量和体重进行校正。我们还根据平面 WR 和基于 CNN 的 WR 之间的关系确定的线性回归线将 WR 分为正常组和异常组，然后分析它们之间的一致性。 CNN 对摄取正常和减少的患者的心脏区域进行了分割。基于 CNN 的 SPECT 心脏计数与带或不带背景校正的传统平面心脏计数以及平面心脏与纵隔比率显着相关（分别为 R2 = 0.862、0.827 和 0.729，p< 0.0001）。基于 CNN 和平面的 WR 还与背景校正和无背景校正以及基于心脏与纵隔比率的 WR 相关，分别为 R2 = 0.584、0.568 和 0.507 (p < 0.0001)。高 WR 和低 WR 的列联表结果（平面和 SPECT 研究的截止值分别为 34% 和 30%）显示基于 CNN 的方法和平面方法之间的一致性为 87.2%。 CNN 可以对 SPECT 图像进行分割，并可靠地进行三维计算平均心脏计数和 WR，这可能是量化神经支配 SPECT 图像的一种新方法。在线版本包含可在 10.1186/s13550-021-00847-x 获取的补充材料。
Since three-dimensional segmentation of cardiac region in 123I-metaiodobenzylguanidine (MIBG) study has not been established, this study aimed to achieve organ segmentation using a convolutional neural network (CNN) with 123I-MIBG single photon emission computed tomography (SPECT) imaging, to calculate heart counts and washout rates (WR) automatically and to compare with conventional quantitation based on planar imaging. We assessed 48 patients (aged 68.4 ± 11.7 years) with heart and neurological diseases, including chronic heart failure, dementia with Lewy bodies, and Parkinson's disease. All patients were assessed by early and late 123I-MIBG planar and SPECT imaging. The CNN was initially trained to individually segment the lungs and liver on early and late SPECT images. The segmentation masks were aligned, and then, the CNN was trained to directly segment the heart, and all models were evaluated using fourfold cross-validation. The CNN-based average heart counts and WR were calculated and compared with those determined using planar parameters. The CNN-based SPECT and conventional planar heart counts were corrected by physical time decay, injected dose of 123I-MIBG, and body weight. We also divided WR into normal and abnormal groups from linear regression lines determined by the relationship between planar WR and CNN-based WR and then analyzed agreement between them. The CNN segmented the cardiac region in patients with normal and reduced uptake. The CNN-based SPECT heart counts significantly correlated with conventional planar heart counts with and without background correction and a planar heart-to-mediastinum ratio (R2 = 0.862, 0.827, and 0.729, p < 0.0001, respectively). The CNN-based and planar WRs also correlated with and without background correction and WR based on heart-to-mediastinum ratios of R2 = 0.584, 0.568 and 0.507, respectively (p < 0.0001). Contingency table findings of high and low WR (cutoffs: 34% and 30% for planar and SPECT studies, respectively) showed 87.2% agreement between CNN-based and planar methods. The CNN could create segmentation from SPECT images, and average heart counts and WR were reliably calculated three-dimensionally, which might be a novel approach to quantifying SPECT images of innervation. The online version contains supplementary material available at 10.1186/s13550-021-00847-x.
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影响因子：
2.4
作者：
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DOI：
10.1007/s00259-014-2759-x
发表时间：
2014-09-01
影响因子：
9.1
作者：
Nakajima, Kenichi;Nakata, Tomoaki;Jacobson, Arnold F.
通讯作者：
Jacobson, Arnold F.