Elsevier

Computers & Graphics

Volume 99, October 2021, Pages 259-271
Computers & Graphics

Special Section on VCBM 2020
inCCsight: A software for exploration and visualization of DT-MRI data of the Corpus Callosum

https://doi.org/10.1016/j.cag.2021.07.012Get rights and content

Highlights

  • Free, open-source, portable, web-based framework for processing of DTI data of the Corpus Callosum.

  • Implements measurable segmentation quality control.

  • Presents interactive and intuitive dashboard for data visualization.

  • Allows exporting high quality tabular and graphical data.

Abstract

The Corpus Callosum (CC) is the largest white matter structure in the human brain. Due to its highly organized fibers, analysis with diffusion tensor imaging (DTI) has been extensively used and has provided new relevant information about the CC. Most CC studies on DTI are concerned with diffusion measures alongside the structure, which requires its segmentation - determination of its limits - and parcellation - division of the structure in different parts, according to the cortical regions with which they are interconnected and their respective functions.

Recent access to larger datasets has required the use of fully automated methods for segmentation and parcellation for CC studies on DTI, often available as algorithms, but not as implemented tool. This leads to studies that lack reproducibility and are incomparable due to the differences in the early stages of segmentation and parcellation. To allow researchers to perform CC analysis on DTI with confidence, especially when working with large datasets, we implemented inCCsight (available at https://github.com/MICLab-Unicamp/inCCsight), a portable platform for DTI automated segmentation and parcellation for small or large datasets, with an automated step of quality assessment of resulting segmentations and several interactive plots and measurements to allow data visualization and exploration, inciting discoveries even based on previously observed data.

Section snippets

Introduction and motivation

The corpus callosum (CC) is the major interhemispheric commissure that connects most of the cortical areas and is the largest white matter structure in the human brain [1]. Several studies have related CC characteristics with sex [2], intelligence [3] and laterality [4]. Furthermore, it has also been related to several conditions, including but not limited to: autism [5], epilepsy [6], different psychiatric conditions such as depression [7] and schizophrenia [8] and different neurodegenerative

Background

This section addresses the theoretical concepts necessary to understand the developed tool.

Available DTI tools

Given the complexity of DTI processing and analysis, several tools were developed and are widely used by the scientific community. The existing ones are mainly focused on: preprocessing, visualization, tractography, and connectivity studies. Just a few allow region-based analysis but often require manual segmentation. None of them incorporated automated DTI-based methods for segmentation and parcellation of the CC. Information about such tools summarized in this section was obtained in the

Requirement analysis

Following the nested model for visualization design by Munzner [61], we characterized the problem domain. We started by doing extensive but unstructured research on recent, well-cited papers that considered analysis of the corpus callosum with the pipeline considered in Section 2.3, in order to understand the type of visualization and statistical analysis their results relied on.

To meet the requirements and the demands of the target audience, we also consulted with researchers that work with

inCCsight

In this section, we present the features and characteristics of inCCsight, also describing how it works, its inputs, and outputs. We wanted to develop this software to be simple, intuitive, open-source, and portable, so we chose to develop it as a web-based application to run on the browser of the user’s choice. It can be installed in Linux from source and run in Windows or Mac using a Docker image (https://github.com/MICLab-Unicamp/inCCsight).

Case study

We conducted a case study in collaboration with our domain experts, who also co-authored this paper. The study exemplifies cases that would appear in a neuroscientific environment in this or a similar form and describes how using different automatic segmentation methods might impact study results. We present a study comparing subjects with Systemic Lupus Erythematosus (SLE) and a control group. Further information about subjects was not used, as the goal of the proposed analysis is to compare

Discussion

This work presents a new web-based open-source tool, inCCsight, for segmentation, quality control, parcellation, data visualization, and exploration of the Corpus Callosum from DTI data. The motivation given for the new tool is the need for fully automatic methods for the segmentation and parcellation of the corpus callosum, considering the availability of large amounts of data, while still promoting transparency and reproducibility.

We implement in a single tool: (1) automated CC segmentation

Conclusion

The proposed software, inCCsight, has proven to be a practical and valuable tool for DTI-based analysis of the corpus callosum. It is the first publicly available tool that provides an automated analysis pipeline comprising: CC segmentation by two distinct methods; automated quality assessment of segmentation results; CC parcellation by five different methods; and visualizations on a rich, comprehensive dashboard, including several tables and plots. The interactive aspect was designed to allow

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The authors would like to thank the National Council for Scientific and Technological Development (CNPq - process 313598/2020-7) and the São Paulo Research Foundation (FAPESP - process 2013/07559-3 for financial support.

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