Automatic detection and classification of regions of FDG uptake in whole-body PET-CT lymphoma studies. (September 2017)

• Record Type: Journal Article
• Authors: Bi, Lei; Kim, Jinman; Kumar, Ashnil; Wen, Lingfeng; Feng, Dagan; Fulham, Michael
• Is Part Of: Computerized medical imaging and graphics. Volume 60(2017)
• Page Start: 3
• Page End: 10
• Publication Date: 2017-09
• Subjects: Classification -- Thresholding -- PET-CT -- CNN; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2016.11.008
• Languages: English
• ISSNs: 0895-6111

Abstract

Graphical abstract: Highlights: We present a method for classifying sites of FDG uptake in PET–CT images. Multi-scale superpixels are used to encode uptake regions of various sizes. Features from domain transferred deep learning are applied at individual classes. Our method operates on whole-body PET–CT images rather than specific body regions. We achieve higher classification accuracy compared to the state-of-the-art methods. Abstract: [ 18 F]-Fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET–CT) scans of lymphoma patients usually show disease involvement as foci of increased radiotracer uptake. Existing methods for detecting abnormalities, model the characteristics of these foci; this is challenging due to the inconsistent shape and localization information about the lesions. Thresholding the degree of FDG uptake is the standard method to separate different sites of involvement. But may fragment sites into smaller regions, and may also incorrectly identify sites of normal physiological FDG uptake and normal FDG excretion (sFEPU) such as the kidneys, bladder, brain and heart. These sFEPU can obscure sites of abnormal uptake, which can make image interpretation problematic. Identifying sFEPU is therefore important for improving the sensitivity of lesion detection and image interpretation. Existing methods to identify sFEPU are inaccurate because they fail to account for the low inter-class differences between sFEPU fragments and their inconsistent localization information. In this study, we address this issue by using a multi-scale superpixel-based encoding (MSE) to group the individual sFEPU fragments into larger regions, thereby, enabling the extraction of highly discriminative image features via domain transferred convolutional neural networks. We then classify there regions into one of the sFEPU classes using a class-driven feature selection and classification model (CFSC) method that avoids overfitting to the most frequently occurring classes. Our experiments on 40 whole-body lymphoma PET-CT studies show that our method achieved better accuracy (an average F-score of 91.73%) compared to existing methods in the classification of sFEPU.