Detection of Basal Nuclei on Magnetic Resonance Images using Support Vector Machines

Villegas, Ricardo Astudillo; Bosnjak, Antonio; Chumbimuni, R.; Flores, Eliezer Soares; López, C.; Montilla, G.

doi:10.1007/978-3-540-89208-3_99

Cited by 2 publications

(1 citation statement)

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“…Such patient-to-template or template-to-patient registrations are termed “atlasbased segmentation” and are important because manual segmentations of DBS related structures is highly time consuming, requires expert anatomical knowledge (Forstmann et al, 2017; Zwirner et al, 2016; Visser et al, 2016b; Chakravarty et al, 2013) and may not be straightforward on clinical MRI data given insufficient signal-to-noise ratio or resolution. Thus, the automated segmentation of STN and GP has been a field of vital and ongoing work with innovative contributions by multiple research groups (Garzón et al, 2017; Visser et al, 2016b; 2016a; Chakravarty et al, 2013; Haegelen et al, 2012; Helms et al, 2009; Lim et al, 2013; Villegas et al, 2009). In addition, many more general methods facilitating imaging co-registration have been developed over the last 20 years, primarily within the field of brain imaging (Ashburner, 2007; Ashburner and Friston, 2011; Andersson et al, 2010, Avants et al, 2010; Schonecker et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Optimization and comparative evaluation of nonlinear deformation algorithms for atlas-based segmentation of DBS target nuclei

et al. 2019

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Nonlinear registration of individual brain MRI scans to standard brain templates is common practice in neuroimaging and multiple registration algorithms have been developed and refined over the last 20 years. However, little has been done to quantitatively compare the available algorithms and much of that work has exclusively focused on cortical structures given their importance in the fMRI literature. In contrast, for clinical applications such as functional neurosurgery and deep brain stimulation (DBS), proper alignment of subcortical structures between template and individual space is important. This allows for atlas-based segmentations of anatomical DBS targets such as the subthalamic nucleus (STN) and internal pallidum (GPi). Here, we systematically evaluated the performance of six modern and established algorithms on subcortical normalization and segmentation results by calculating over 11,000 nonlinear warps in over 100 subjects. For each algorithm, we evaluated its performance using T1- or T2-weighted acquisitions alone or a combination of T1-, T2- and PD-weighted acquisitions in parallel. Furthermore, we present optimized parameters for the best performing algorithms. We tested each algorithm on two datasets, a state-of-theart MRI cohort of young subjects and a cohort of subjects age- and MR-quality-matched to a typical DBS Parkinson’s Disease cohort. Our final pipeline is able to segment DBS targets with precision comparable to manual expert segmentations in both cohorts. Although the present study focuses on the two prominent DBS targets, STN and GPi, these methods may extend to other small subcortical structures like thalamic nuclei or the nucleus accumbens.

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