The accurate segmentation of lesions in magnetic resonance images of stroke patients is important, for example, for comparing the location of the lesion with functional areas and for determining the optimal strategy for patient treatment. Manual labeling of each lesion turns out to be time-intensive and costly, making an automated method desirable. Standard approaches for brain parcellation make use of spatial atlases that represent prior information about the spatial distribution of different tissue types and of anatomical structures of interest. Different from healthy tissue, however, the spatial distribution of a stroke lesion varies considerably, limiting the use of such brain image segmentation approaches for stroke lesion analysis, and for integrating brain parcellation with stroke lesion segmentation. In this study, we propose to amend the standard atlas-based generative image segmentation model by a spatial atlas of stroke lesion occurrence by making use of information about the vascular territories. As the territories of the major arterial trees often coincide with the location and extensions of large stroke lesions, we use 3D maps of the vascular territories to form patient-specific atlases combined with outlier information from an initial run, following an iterative procedure. We find our approach to perform comparable to (or better than) standard approaches that amend the tissue atlas with a flat lesion prior or that treat lesion as outliers, and to outperform both for large heterogeneous lesions.
Clustering has been proven useful for knowledge discovery from massive data in many applications ranging from market segmentation to bioinformatics. In this study, we focus on clustering large amounts of medical image data of the human brain to identify structures of interest. Advanced Magnetic Resonance Imaging techniques enable unprecedented insights into the complex processes in the brain. However, especially for clinical studies, a huge amount of data has to be processed in order to find patterns characterizing the structure and function of the healthy brain and its alternations associated with diseases. We survey clustering methods specifically designed for neuroimaging applications such as segmentation of fiber tracks and lesions, as well as methods that can deal with multimodal imaging data. Furthermore, we will illustrate how clustering enables knowledge discovery from data by enhancing the performance of supervised techniques and discovering meaningful subgroups of subjects. The main purpose of this study is to give an introduction on how versatile clustering techniques can be applied in neuroimaging to tackle different applications where automated methods are desired.
The cover image, by Claudia Plant and Alexandra Derntl, is based on the Focus Article Clustering techniques for neuroimaging applications, .
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.