Automatic DTI-based parcellation of the corpus callosum through the watershed transform

Abstract

Introduction: Parcellation of the corpus callosum (CC) in the midsagittal cross-section of the brain is of utmost importance for the study of diffusion properties within this structure. The complexity of this operation comes from the absence of macroscopic anatomical landmarks to help in dividing the CC into different callosal areas. In this paper we propose a completely automatic method for CC parcellation using diffusion tensor imaging (DTI). Methods: A dataset of 15 diffusion MRI volumes from normal subjects was used. For each subject, the midsagital slice was automatically detected based on the Fractional Anisotropy (FA) map. Then, segmentation of the CC in the midsgital slice was performed using the hierarchical watershed transform over a weighted FA-map. Finally, parcellation of the CC was obtained through the application of the watershed transform from chosen markers. Results: Parcellation results obtained were consistent for fourteen of the fifteen subjects tested. Results were similar to the ones obtained from tractography-based methods. Tractography confirmed that the cortical regions associated with each obtained CC region were consistent with the literature. Conclusions: A completely automatic DTI-based parcellation method for the CC was designed and presented. It is not based on tractography, which makes it fast and computationally inexpensive. While most of the existing methods for parcellation of the CC determine an average behavior for the subjects based on population studies, the proposed method reflects the diffusion properties specific for each subject. Parcellation boundaries are found based on the diffusion properties within each individual CC, which makes it more reliable and less affected by differences in size and shape among subjects.

Microstructure of Human Corpus Callosum across the Lifespan: Regional Variations in Axon Caliber, Density, and Myelin Content

“…The inconsistency may stem from the use of the mcDESPOT, an approach that has been criticized for confounding magnetization transfer effects (Zhang et al 2015) and the influence of water exchange between the different compartments (Lankford and Does 2013;Does 2018;West et al 2019). In addition, as expected, conventional DTI-derived indicators also evinced different patterns across the CC in healthy individuals (Hasan et al 2005;Hofer and Frahm 2006;Rittner et al 2014).…”

Unsupervised Method Based on Superpixel Segmentation for Corpus Callosum Parcellation in MRI Scans

“…We notice that geometric methods allow only to divide the CC into the same regions among all subjects without considering the human and individual brain features between different subjects. On the second hand, differently to geometric parcellation methods, Rittner proposed a data-driven method based on the Watershed technique [15]. This method is composed of four steps.…”

“…Despite its simplicity, SLIC has been demonstrated to be effective in various computer vision applications [14]. -The subdivision process of the proposed method is fully automatic and it is the second study that proposed a non-geometric analysis for the CC parcels, to the best of our knowledge [15]. Although it is based only on the MRI data of each analyzed subject, with no parameter adjusting, the proposed method proved quantitatively its superiority over state-of-the-art methods.…”

Populational brain models of diffusion tensor imaging for statistical analysis: a complementary information in common space

“…Following these brain templates and the raising necessity to others MR imaging modalities, the Diffusion Tensor Imaging (DTI) was also intensively studied to add more possibilities for the white matter brain studies (Alves et al, 2012;Itagiba et al, 2010;Miraldi et al, 2013;Rittner et al, 2014). In the past, the lack of white matter information is understandable due to a homogeneous appearance in conventional MRI, as well as in histology preparations Mori et al, 2008).…”