A Multidimensional Framework Incorporating 2D U-Net and 3D Attention U-Net for the Segmentation of Organs from 3D Fluorodeoxyglucose-Positron Emission Tomography Images

Abstract

Accurate analysis of Fluorodeoxyglucose (FDG)-Positron Emission Tomography (PET) images is crucial for the diagnosis, treatment assessment, and monitoring of patients suffering from various cancer types. FDG-PET images provide valuable insights by revealing regions where FDG, a glucose analog, accumulates within the body. While regions of high FDG uptake include suspicious tumor lesions, FDG also accumulates in non-tumor-specific regions and organs. Identifying these regions is crucial for excluding them from certain measurements, or calculating useful parameters, for example, the mean standardized uptake value (SUV) to assess the metabolic activity of the liver. Manual organ delineation from FDG-PET by clinicians demands significant effort and time, which is often not feasible in real clinical workflows with high patient loads. For this reason, this study focuses on automatically identifying key organs with high FDG uptake, namely the brain, left cardiac ventricle, kidneys, liver, and bladder. To this end, an ensemble approach is adopted, where a three-dimensional Attention U-Net (3D AU-Net) is employed for robust three-dimensional analysis, while a two-dimensional U-Net (2D U-Net) is utilized for analysis in the coronal plane. The 3D AU-Net demonstrates highly detailed organ segmentations, but also includes many false positive regions. In contrast, 2D U-Net achieves higher reliability with minimal false positive regions, but lacks the 3D details. Experiments conducted on a subset of the public AutoPET dataset with 60 PET scans demonstrate that the proposed ensemble model achieves high accuracy in segmenting the required organs, surpassing current state-of-the-art techniques, and supporting the potential utilization of the proposed methodology in accelerating and enhancing the clinical workflow of cancer patients.