Atlas-based methods are the standard approaches for automatic targeting of the Anterior Nucleus of the Thalamus (ANT) for Deep Brain Stimulation. These are known to lack robustness when anatomic differences between subjects are large. We propose a novel two-stage deep learning (DL) framework.
Atlas-based methods are the standard approaches for automatic targeting of
the Anterior Nucleus of the Thalamus (ANT) for Deep Brain Stimulation (DBS),
but these are known to lack robustness when anatomic differences between
atlases and subjects are large. To improve the localization robustness, we
propose a novel two-stage deep learning (DL) framework, where the first stage
identifies and crops the thalamus regions from the whole brain MRI and the
second stage performs per-voxel regression on the cropped volume to localize
the targets at the finest resolution scale. To address the issue of data
scarcity, we train the models with the pseudo labels which are created based on
the available labeled data using multi-atlas registration. To assess the
performance of the proposed framework, we validate two sampling-based
uncertainty estimation techniques namely Monte Carlo Dropout (MCDO) and
Test-Time Augmentation (TTA) on the second-stage localization network.
Moreover, we propose a novel uncertainty estimation metric called maximum
activation dispersion (MAD) to estimate the image-wise uncertainty for
localization tasks. Our results show that the proposed method achieved more
robust localization performance than the traditional multi-atlas method and TTA
could further improve the robustness. Moreover, the epistemic and hybrid
uncertainty estimated by MAD could be used to detect the unreliable
localizations and the magnitude of the uncertainty estimated by MAD could
reflect the degree of unreliability for the rejected predictions.