Near-tubular fiber bundle segmentation for diffusion weighted imaging: Segmentation through frame reorientation

Abstract

This paper proposes a methodology to segment near-tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. Segmentation is achieved through simple global statistical modeling of diffusion orientation. Utilizing a modification of a recent segmentation approach by Bresson et al. allows for a convex optimization formulation of the segmentation problem, combining orientation statistics and spatial regularization. The approach compares favorably with segmentation by full-brain streamline tractography.

A Tract-Specific Framework for White Matter Morphometry Combining Macroscopic and Microscopic Tract Features

“…As identified in [6], six major tracts fit into this category: corpus callosum (CC), corticospinal tracts (CST), inferior fronto-occipital tracts (IFO), inferior longitudinal tracts (ILF), superior longitudinal tracts (SLF), and uncinates (UNC). White matter tracts that are more appropriately represented by tubular models have been extensively studied in the literature [3,4,5] and are not considered here. After fiber tractography, binary 3D segmentations of individual tracts are generated by labeling voxels in the template through which at least one fiber passes.…”

“…However, the whole-brain approach of the TBSS fundamentally limits its anatomical specificity. Recognizing the importance of tract-specific analysis, many groups have recently developed innovative techniques for analyzing individual WM tracts with either tubular geometry [3,4,5] or sheet-like appearance [6].…”

A Cross-Sectional Piecewise Constant Model for Segmenting Highly Curved Fiber Tracts in Diffusion MR Images

“…The latter approach has been more popular over the past decade with examples including segmentation based on pre-computed edge information [10] and clustering voxels using local statistics pre-computed from Parzen windows [2]. More recently, tractography results have been used to provide global tract shape information as input to the segmentation process [3,13,15], allowing for the pre-processing of an image based on the orientation of the tract. This global shape information may well complement the local appearance information obtained using Parzen windowing or edge maps, yet, individually, these approaches are limited by either susceptibility to noise or lack of fidelity between the data and the image model [13].…”

“…The piecewise constancy assumption is then justifiably applied only at a local scale while, at the global scale, cross-sectional planes are defined based on an "anchor curve" obtained from tractography. Results on both synthetic and real data show improved segmentation quality compared to state-of-the-art methods [2,[9][10][11]15], particularly in areas of crossing fiber tracts. Our proposed segmentation workflow.…”

Detecting Structure in Diffusion Tensor MR Images

“…These features allow us to capture structural information within a DT image that has previously not been explored. We note here that our approach to detecting these structural features is different from other approaches [11,15,21] in that we work with more fundamental operators of low-level computer vision as opposed to higher level characterization of the data.…”