  In the last years there has been a growing interest in the applications of
diffusion tractography for target identification in neurofunctional disorders
for an increasingly tailored approach. The growing diffusion of
well-established neurosurgical procedures as deep brain stimulation (DBS) and
trans-cranial Magnetic Resonance-guided Focused Ultrasound (tcMRgFUS) favored
this trend. Tractography can indeed provide more accurate, patient-specific,
information about the targeted region if compared to stereotactic atlases. On
the other hand, this tractography-based approachs is not very
physician-friendly, and its heavily time consuming since needs several hours
for Magnetic Resonance Imaging (MRI) data processing. In this study we propose
a novel open-source deep learning framework called DeLTA-BIT (acronym of
Deep-learning Local TrActography for BraIn Targeting) for reconstructing
thalamic probabilistic tractography maps enabling a personalized
(patient-specific) target identification for functional neurological disorders.
The proposed framework exploits two convolutional neural networks (CNNs) to
segment the thalamus and reconstruct the connectivity map between each voxel
inside the thalamus and any user-specified target cortical or subcortical
masks. These CNNs were trained, validated, and tested on the datasets from the
Human Connectome Project (HCP) database. Our results are comparable with the
state-of-the-art data-driven segmentation and probabilistic tractography and
demonstrate the high performance of proposed networks for personalized thalamic
target identification with a Dice coefficient of 0.93 +/- 0.01 for thalamus
segmentation and a Dice Similarity Coefficient for the projection to the
precentral gyrus from the thalamus of 0.80 +/- 0.06. The code of these models
is freely available on GitHub.
