Visual interactive exploration and clustering of brain fiber tracts

Xu, Chaoqing; Liu, Yi‐Peng; Jiang, Zhechen; Sun, Guodao; Jiang, Li; Liang, Ronghua

doi:10.1007/s12650-020-00642-1

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…A typical framework for geometry-based fiber clustering defines similarity/distance between pairwise fiber tracts. This framework involves many similarity matrices, including Euclidean distance [53], Hausdorff distance [25], Mahalanobis distance [31],etc. Román [39] proposed a whole-brain fiber clustering method based on distance metric, which is computed by Euclidean distance between corresponding points of two fibers.…”

Section: Brain Fiber Clusteringmentioning

confidence: 99%

Vector Field Streamline Clustering Framework for Brain Fiber Tract Segmentation

Sun

Liang

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

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Brain fiber tracts are widely used in studying brain diseases, which may lead to a better understanding of how disease affects the brain. The segmentation of brain fiber tracts assumed enormous importance in disease analysis. In this paper, we propose a novel vector field streamline clustering framework for brain fiber tract segmentations. Brain fiber tracts are firstly expressed in a vector field and compressed using the streamline simplification algorithm. After streamline normalization and regular-polyhedron projection, high-dimensional features of each fiber tract are computed and fed to the IDEC clustering algorithm. We also provide qualitative and quantitative evaluations of the IDEC clustering method and QB clustering method. Our clustering results of the brain fiber tracts help researchers gain perception of the brain structure. This work has the potential to automatically create a robust fiber bundle template that can effectively segment brain fiber tracts while enabling consistent anatomical tract identification.

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Section: Brain Fiber Clusteringmentioning

confidence: 99%

Vector Field Streamline Clustering Framework for Brain Fiber Tract Segmentation

Sun

Liang

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

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“…These in turn enable one to create a simplified visualization by e.g., rendering each cluster with a different color or reducing the cluster to a simpler representative, which is next visualized. Xu et al [30] use the DBSCAN clustering algorithm [31] to create such clusters and combine them with user specification of regions of interest to create a rich palette of focus-and-context fiber tract visualization. Poco et al [32] approach this by reducing every 3D fiber to a high-dimensional feature vector that represents position, geometry, and smoothness.…”

Section: Diffusion Tensor Imaging and Tractographymentioning

confidence: 99%

Structure-Aware Trail Bundling for Large DTI Datasets

2020

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Creating simplified visualizations of large 3D trail sets with limited occlusion and preservation of the main structures in the data is challenging. We address this challenge for the specific context of 3D fiber trails created by DTI tractography. For this, we propose to jointly simplify trails in both the geometric space (by extending and adapting an existing bundling method to handle 3D trails) and in the image space (by proposing several shading and rendering techniques). Our method can handle 3D datasets of hundreds of thousands of trails at interactive rate, has parameters for the most of which good preset values are given, and produces visualizations that have been found, in a small-scale user study involving five medical professionals, to be better in occlusion reduction, conveying the connectivity structure of the brain, and overall clarity than existing methods for the same data. We demonstrate our technique with several real-world public DTI datasets.

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