  Thalamic nuclei have been implicated in several neurological diseases.
WMn-MPRAGE images have been shown to provide better intra-thalamic nuclear
contrast compared to conventional MPRAGE images but the additional acquisition
results in increased examination times. In this work, we investigated 3D
Convolutional Neural Network (CNN) based techniques for thalamic nuclei
parcellation from conventional MPRAGE images. Two 3D CNNs were developed and
compared for thalamic nuclei parcellation using MPRAGE images: a) a native
contrast segmentation (NCS) and b) a synthesized contrast segmentation (SCS)
using WMn-MPRAGE images synthesized from MPRAGE images. We trained the two
segmentation frameworks using MPRAGE images (n=35) and thalamic nuclei labels
generated on WMn-MPRAGE images using a multi-atlas based parcellation
technique. The segmentation accuracy and clinical utility were evaluated on a
cohort comprising of healthy subjects and patients with alcohol use disorder
(AUD) (n=45). The SCS network yielded higher Dice scores in the Medial
geniculate nucleus (P=.003) and Centromedian nucleus (P=.01) with lower volume
differences for Ventral anterior (P=.001) and Ventral posterior lateral (P=.01)
nuclei when compared to the NCS network. A Bland-Altman analysis revealed
tighter limits of agreement with lower coefficient of variation between true
volumes and those predicted by the SCS network. The SCS network demonstrated a
significant atrophy in Ventral lateral posterior nucleus in AUD patients
compared to healthy age-matched controls (P=0.01), agreeing with previous
studies on thalamic atrophy in alcoholism, whereas the NCS network showed
spurious atrophy of the Ventral posterior lateral nucleus. CNN-based contrast
synthesis prior to segmentation can provide fast and accurate thalamic nuclei
segmentation from conventional MPRAGE images.
