24 March 2016 Machine-learning model observer for detection and localization tasks in clinical SPECT-MPI
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Abstract
In this work we propose a machine-learning MO based on Naive-Bayes classification (NB-MO) for the diagnostic tasks of detection, localization and assessment of perfusion defects in clinical SPECT Myocardial Perfusion Imaging (MPI), with the goal of evaluating several image reconstruction methods used in clinical practice. NB-MO uses image features extracted from polar-maps in order to predict lesion detection, localization and severity scores given by human readers in a series of 3D SPECT-MPI. The population used to tune (i.e. train) the NB-MO consisted of simulated SPECT-MPI cases – divided into normals or with lesions in variable sizes and locations – reconstructed using filtered backprojection (FBP) method. An ensemble of five human specialists (physicians) read a subset of simulated reconstructed images, and assigned a perfusion score for each region of the left-ventricle (LV). Polar-maps generated from the simulated volumes along with their corresponding human scores were used to train five NB-MOs (one per human reader), which are subsequently applied (i.e. tested) on three sets of clinical SPECT-MPI polar maps, in order to predict human detection and localization scores. The clinical “testing” population comprises healthy individuals and patients suffering from coronary artery disease (CAD) in three possible regions, namely: LAD, LcX and RCA. Each clinical case was reconstructed using three reconstruction strategies, namely: FBP with no SC (i.e. scatter compensation), OSEM with Triple Energy Window (TEW) SC method, and OSEM with Effective Source Scatter Estimation (ESSE) SC. Alternative Free-Response (AFROC) analysis of perfusion scores shows that NB-MO predicts a higher human performance for scatter-compensated reconstructions, in agreement with what has been reported in published literature. These results suggest that NB-MO has good potential to generalize well to reconstruction methods not used during training, even for reasonably dissimilar datasets (i.e. simulated vs. clinical).
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Felipe M. Parages, J. Michael O'Connor, P. Hendrik Pretorius, Jovan G. Brankov, "Machine-learning model observer for detection and localization tasks in clinical SPECT-MPI", Proc. SPIE 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, 97870W (24 March 2016); doi: 10.1117/12.2217124; https://doi.org/10.1117/12.2217124
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