Corpus callosum – MICLab

A framework for quality control of corpus callosum segmentation in large-scale studies

MIC-Suporte — Sun, 10 Apr 2022 12:33:29 +0000

A framework for quality control of corpus callosum segmentation in large-scale studies

Journal of Neuroscience Methods, Volume 334, 15 March 2020, 108593

The corpus callosum (CC) is the largest white matter structure in the brain, responsible for the interconnection of the brain hemispheres. Its segmentation is a required preliminary step for any posterior analysis, such as parcellation, registration, and feature extraction. In this context, the quality control (QC) of CC segmentation allows studies on large datasets with no human interaction, and the proper usage of available automated and semi-automated algorithms. We propose a framework for QC of CC segmentation based on the shape signature, computed at 49 distinct resolutions. At each resolution, a support vector machine (SVM) classifier was trained, generating 49 individual classifiers. Then, a disagreement metric was used to cluster these individual classifiers. The final ensemble was constructed by selecting one representation from each cluster. The proposed framework achieved an area under the curve (AUC) metric of 98.25% on the test set (207 subjects) employing an ensemble composed of 12 components. This ensemble outperformed all individual classifiers. The shape descriptor is robust and versatile, describing the segmentation at different resolutions. The selection of classifiers and the disagreement measure lead to an ensemble composed of high-quality and heterogeneous classifiers, ensuring an optimal trade-off between the ensemble size and high AUC.

Full paper here: https://doi.org/10.1016/j.jneumeth.2020.108593

Reduction of cerebral and corpus callosum volumes in childhood-onset systemic lupus erythematosus

MIC-Suporte — Tue, 05 Apr 2022 12:28:06 +0000

Reduction of Cerebral and Corpus Callosum Volumes in Childhood-Onset Systemic Lupus Erythematosus

Systemic Lupus Erythematosus, Volume 68, Issue 9, September 2016. Pages 2193-2199

There have been few studies in which the prevalence of cerebral atrophy in childhood-onset systemic lupus erythematosus (SLE) was evaluated using magnetic resonance imaging (MRI) volumetric measurements. This study was undertaken to determine the prevalence of cerebral and corpus callosum atrophy in childhood-onset SLE and to determine the possible relationships between atrophy and clinical, laboratory, and treatment features of the disease.

Full paper here: https://doi.org/10.1002/art.39680

Divergence map from diffusion tensor imaging: Concepts and application to corpus callosum

MIC-Suporte — Mon, 04 Apr 2022 12:11:43 +0000

Divergence map from diffusion tensor imaging: Concepts and application to corpus callosum

Engineering in Medicine and Biology Society (EMBC), 2016

This work proposes a novel approach to the analysis of Diffusion Tensor Imaging (DTI) by applying the mathematical concept of divergence, used in vector analysis. This is achieved by choosing an arbitrary direction of analysis and using this direction to transform the diffusion tensor field into an oriented vector field. The method was inspired by the idea of imposing a liquid flow inside the biological tissues, oriented in the direction of analysis, and watching the direction it would be expected to take as it flows through the paths created by the fibers. The experiments were conducted for the particular case of the analysis of the corpus callosum, using real DTI from several subjects. Results showed that the divergence map allows extraction of useful information about the spatial organization of the corpus callosum, providing a way to determine a reference plane that could be used, for example, in studies involving intersubject comparison.

Full paper here: https://doi.org/10.1109/EMBC.2016.7590900

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

MIC-Suporte — Fri, 01 Apr 2022 12:14:26 +0000

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

Brazilian Journal of Biomedical Engineering, 2014

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). 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. 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. 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.

Full paper here: https://doi.org/10.1590/rbeb.2014.012