12 March 2018 A structural connectivity approach to validate a model-based technique for the segmentation of the pulvinar complex
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Abstract
The pulvinar of the thalamus is a higher-order thalamic nucleus that is responsible for gating information flow to the cortical regions of the brain. It is involved in several cortico-thalamocortical relay circuits and is known to be affected in a number of neurological disorders. Segmenting the pulvinar in clinically acquired images is important to support studies exploring its role in brain function. In recent years, we have proposed an active shape model method to segment multiple thalamic nuclei, including the pulvinar. The model was created by manual delineation of high resolution 7T images and the process was guided by the Morel stereotactic atlas. However, this model is based on a small library of healthy subjects, and it is important to validate the reliability of the segmentation method on a larger population of clinically acquired images. The pulvinar is known to have particularly strong white matter connections to the hippocampus, which allows us to identify the pulvinar from thalamic regions of high hippocampal structural connectivity. In this study, we obtained T1-weighted and diffusion MR data from 43 healthy volunteers using a clinical 3T MRI scanner. We applied the segmentation method to the T1-weighted images to obtain the intrathalamic nuclei, and we calculated the connectivity maps between the hippocampus and thalamus using the diffusion images. Our results show that the shape model segmentation consistently localizes the pulvinar in the region with the highest hippocampal connectivity. The proposed method can be extended to other nuclei to further validate our segmentation method.
Conference Presentation
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Srijata Chakravorti, Victoria L. Morgan, Paula Trujillo-Diaz, Raul Wirz, Benoit M. Dawant, "A structural connectivity approach to validate a model-based technique for the segmentation of the pulvinar complex ", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105780T (12 March 2018); doi: 10.1117/12.2293685; https://doi.org/10.1117/12.2293685
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