Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases

Juan Eugenio Iglesias*, Koen Van Leemput, Polina Golland, Anastasia Yendiki

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Abstract

Segmentation of structural and diffusion MRI (sMRI/dMRI) is usually performed independently in neuroimaging pipelines. However, some brain structures (e.g., globus pallidus, thalamus and its nuclei) can be extracted more accurately by fusing the two modalities. Following the framework of Bayesian segmentation with probabilistic atlases and unsupervised appearance modeling, we present here a novel algorithm to jointly segment multi-modal sMRI/dMRI data. We propose a hierarchical likelihood term for the dMRI defined on the unit ball, which combines the Beta and Dimroth-Scheidegger-Watson distributions to model the data at each voxel. This term is integrated with a mixture of Gaussians for the sMRI data, such that the resulting joint unsupervised likelihood enables the analysis of multi-modal scans acquired with any type of MRI contrast, b-values, or number of directions, which enables wide applicability. We also propose an inference algorithm to estimate the maximum-a-posteriori model parameters from input images, and to compute the most likely segmentation. Using a recently published atlas derived from histology, we apply our method to thalamic nuclei segmentation on two datasets: HCP (state of the art) and ADNI (legacy) – producing lower sample sizes than Bayesian segmentation with sMRI alone.
Original languageEnglish
Title of host publicationInformation Processing in Medical Imaging : 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings
Volume11492
PublisherSpringer
Publication date2019
Pages767-779
DOIs
Publication statusPublished - 2019
Event26th International Conference on Information Processing in Medical Imaging - The Hong Kong University of Science and Technology, Hong Kong, Hong Kong
Duration: 2 Jun 20197 Jun 2019
Conference number: 26

Conference

Conference26th International Conference on Information Processing in Medical Imaging
Number26
LocationThe Hong Kong University of Science and Technology
CountryHong Kong
CityHong Kong
Period02/06/201907/06/2019
SeriesLecture Notes in Computer Science
Volume11492
ISSN0302-9743

Cite this

Iglesias, J. E., Van Leemput, K., Golland, P., & Yendiki, A. (2019). Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases. In Information Processing in Medical Imaging: 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings (Vol. 11492, pp. 767-779). Springer. Lecture Notes in Computer Science, Vol.. 11492 https://doi.org/10.1007/978-3-030-20351-1_60