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