Automated tract extraction via atlas based Adaptive Clustering

Abstract

Advancements in imaging protocols such as the high angular resolution diffusion-weighted imaging (HARDI) and in tractography techniques are expected to cause an increase in the tract-based analyses. Statistical analyses over white matter tracts can contribute greatly towards understanding structural mechanisms of the brain since tracts are representative of the connectivity pathways. The main challenge with tract-based studies is the extraction of the tracts of interest in a consistent and comparable manner over a large group of individuals without drawing the inclusion and exclusion regions of interest. In this work, we design a framework for automated extraction of white matter tracts. The framework introduces three main components, namely a connectivity based fiber representation, a fiber clustering atlas, and a clustering approach called Adaptive Clustering. The fiber representation relies on the connectivity signatures of fibers to establish an easy correspondence between different subjects. A group-wise clustering of these fibers that are represented by the connectivity signatures is then used to generate a fiber bundle atlas. Finally, Adaptive Clustering incorporates the previously generated clustering atlas as a prior, to cluster the fibers of a new subject automatically. Experiments on the HARDI scans of healthy individuals acquired repeatedly, demonstrate the applicability, the reliability and the repeatability of our approach in extracting white matter tracts. By alleviating the seed region selection or the inclusion/exclusion ROI drawing requirements that are usually handled by trained radiologists, the proposed framework expands the range of possible clinical applications and establishes the ability to perform tract-based analyses with large samples.

Robust and efficient linear registration of white-matter fascicles in the space of streamlines

“…Another limitation is that for bundle specific use of the SLR an initial segmentation of bundles is required and it would be easier to provide those segmentations automatically. However, as we showed in this paper the Tract-Querier can be used (Wassermann et al, 2013), new techniques are continuously being published on the topic, see for example (Tunç et al, 2014), and also, several labs have growing databases of manually dissected bundles (Mori et al, 2005;Fortin et al, 2012).…”

Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma

“…The applicability, reliability and repeatability of the automated tract extraction tool integrated in brain-CaPTk, was validated in a dataset of healthy individuals acquired repeatedly [35]. Compared to the clustering of fibers for each scan independently, our framework provided better reproducibility (test-retest) results, with decreased (25%) mean intra-individual distance (i.e., disagreement of clusters between different time-points of the same individual), while preserving inter-individual differences.…”

“…This underlines the needs for methods that can track through edema and reconstruct even partial and displaced tracts. To this end, brain-CaPTk provides tools for tractography robust to edema [32, 33] and automated tract detection based on connectivity signatures [34, 35], to extract fiber tracts, even distorted or broken, in the presence of mass effect and edema (Fig. 4).…”

“…The automated tract detection framework defines a fiber bundle atlas that will emulate the expert, using white matter fibers of healthy individuals. The white matter tracts in any patient are then extracted based on the definitions encoded in the atlas, using connectivity signatures of the fibers [34, 35]. …”

AnatomiCuts: Hierarchical clustering of tractography streamlines based on anatomical similarity

“…Previous attempts to incorporate anatomical information in unsupervised streamline clustering used the structures that the streamlines terminate in, either in the similarity measure itself (Tunc et al ., 2014), in a post-hoc manner (Wassermann et al ., 2010), or for initialization (Guevara et al ., 2011). Alternatively, supervised clustering approaches introduced prior information on WM anatomy from an atlas of predefined bundles, labeled by an expert (O’Donnell & Westin, 2007; Maddah et al ., 2008; Ziyan et al ., 2009; Guevara et al ., 2012; Wang et al ., 2013; Ros et al ., 2013; Jin et al ., 2014).…”