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,

SUBMITTER: Buddenkotte T 

PROVIDER: S-EPMC12095456 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature