Convolutional neural network-based automatic heart segmentation and quantitation in ¹²³I-metaiodobenzylguanidine SPECT imaging.

ABSTRACT:

Background

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.

Methods

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 t

SUBMITTER: Saito S 

PROVIDER: S-EPMC8511236 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature