Automated deep learning segmentation of cardiac inflammatory FDG PET

Fluorodeoxyglucose positron emission tomography (FDG PET) with suppression of myocardial glucose utilization plays a pivotal role in diagnosing cardiac sarcoidosis. Reorientation of images to match perfusion datasets and myocardial segmentation enables consistent image scaling and quantification. However, such manual tasks are cumbersome. We developed a 3D U-Net deep-learning (DL) algorithm for automated myocardial segmentation in cardiac sarcoidosis FDG PET.

The DL model was trained on FDG PET scans from 316 patients with left ventricular contours derived from paired perfusion datasets. Qualitative analysis of clinical readability was performed to compare DL segmentation with the current automated method on a 50-patient test subset. Additionally, left ventricle displacement and angulation, as well as SUVmax sampling were compared with inter-user reproducibility results. A hybrid workflow was also investigated to accelerate study processing time.

DL segmentation enhanced readability scores in over 90% of cases compared with the standard segmentation currently used in the software. DL segmentation performed similar to a trained technologist, surpassing standard segmentation for left ventricle displacement and angulation, as well as correlation of SUVmax. Using the DL segmentation as initial placement for manual segmentation significantly decreased the processing time.

A novel DL-based automated segmentation tool markedly improves processing of cardiac sarcoidosis FDG PET. This tool yields optimized splash display of sarcoidosis FDG PET datasets with no user input and offers significant processing time improvement for manual segmentation of such datasets.