Fully automatic categorical analysis of striatal subregions in dopamine transporter SPECT using a convolutional neural network.

Abstract

Objective: To provide fully automatic scanner-independent 5-level categorization of the [¹²³I]FP-CIT uptake in striatal subregions in dopamine transporter SPECT.

Methods: A total of 3500 [¹²³I]FP-CIT SPECT scans from two in house (n = 1740, n = 640) and two external (n = 645, n = 475) datasets were used for this study. A convolutional neural network (CNN) was trained for the categorization of the [¹²³I]FP-CIT uptake in unilateral caudate and putamen in both hemispheres according to 5 levels: normal, borderline, moderate reduction, strong reduction, almost missing. Reference standard labels for the network training were created automatically by fitting a Gaussian mixture model to histograms of the specific [¹²³I]FP-CIT binding ratio, separately for caudate and putamen and separately for each dataset. The CNN was trained on a mixed-scanner subsample (n = 1957) and tested on one independent identically distributed (IID, n = 1068) and one out-of-distribution (OOD, n = 475) test dataset.

Results: The accuracy of the CNN for the 5-level prediction of the [¹²³I]FP-CIT uptake in caudate/putamen was 80.1/78.0% in the IID test dataset and 78.1/76.5% in the OOD test dataset. All 4 regional 5-level predictions were correct in 54.3/52.6% of the cases in the IID/OOD test dataset. A global binary score automatically derived from the regional 5-scores achieved 97.4/96.2% accuracy for automatic classification of the scans as normal or reduced relative to visual expert read as reference standard.

Conclusions: Automatic scanner-independent 5-level categorization of the [¹²³I]FP-CIT uptake in striatal subregions by a CNN model is feasible with clinically useful accuracy.