Data-Driven Corpus Callosum Parcellation Method Through Diffusion Tensor Imaging

Cover, Giovana S.; Pereira, Mariana Barbosa; Bento, Mariana; Appenzeller, Simone; Rittner, Letícia

doi:10.1109/access.2017.2761701

Cited by 12 publications

(7 citation statements)

References 40 publications

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“…Four of the implemented schemes are geometric: Witelson, 11 Hofer & Frahm, 12 Chao 13 and one based on Freesurfer. The last one, proposed by Cover et al 14 is the only data-driven method.…”

Section: Parcelationmentioning

confidence: 99%

Volumetric corpus callosum segmentation integrated to inCCsight software for supporting DTI-based studies

Alcantara

Rodrigues

Julio

et al. 2023

Medical Imaging 2023: Computer-Aided Diagnosis

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The Corpus Callosum is the major interhemispheric commisure and, because of its highly organized fibers, it is often studied using diffusion tensor images (DTI). A firstnecessary step for CC studies is its segmentation, preferably automated. Since most available softwares are not able to perform CC volumetric segmentation, and the only ones that do it, are based on T1-weighted images and not DTI, this work presents the extension of an open-source software, called inCCsight, incorporating a DTI-based CC volumetric segmentation method into it. The software is open-source and offers the possibility of incorporating customized plots and integrating other segmentation and/or parcellation methods by the user.

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“…Four of the implemented schemes are geometric: Witelson, 11 Hofer & Frahm, 12 Chao 13 and one based on Freesurfer. The last one, proposed by Cover et al 14 is the only data-driven method.…”

Section: Parcelationmentioning

confidence: 99%

Volumetric corpus callosum segmentation integrated to inCCsight software for supporting DTI-based studies

Alcantara

Rodrigues

Julio

et al. 2023

Medical Imaging 2023: Computer-Aided Diagnosis

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“…Nevertheless, this method suffers from sensitivity to parameters selection. In order to overcome its limitations, Cover extended the Rittner method with some important changes [12]. Practically, the author replaced all steps except the first step in order to lead to a more robust data-driven method.…”

Section: Related Workmentioning

confidence: 99%

“…On the one hand, the manual CC segmentation methods require strongly visual effort, specialized training skill, and are time-consuming processes. On the other hand, several geometrical methods for the CC parcellation have been proposed such as Witelson and Hofer methods [12]. However, these methods cannot be satisfactorily validated due to the lack of qualitative parameters and reference standards.…”

Section: Introductionmentioning

confidence: 99%

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

Jlassi

Barhoumi

Maktouf

2020

Lecture Notes in Computer Science

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In this paper, we introduce an unsupervised method for the parcellation of the Corpus Callosum (CC) from MRI images. Since there are no visible landmarks within the structure that explicit its parcels, non-geometric CC parcellation is a challenging task especially that almost of proposed methods are geometric or data-based. In fact, in order to subdivide the CC from brain sagittal MRI scans, we adopt the probabilistic neural network as a clustering technique. Then, we use a cluster validity measure based on the maximum entropy (Vmep) to obtain the optimal number of classes. After that, we obtain the isolated CC that we parcel automatically using SLIC (Simple Linear Iterative Clustering) as superpixel segmentation technique. The obtained results on two challenging public datasets prove the performance of the proposed method against geometric methods from the state of the art. Indeed, as best as we know, it is the first work that investigates the validation of a CC parcellation method on ground-truth datasets using many objective metrics.

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“…Structural magnetic resonance (MR) imaging is a critical component in many clinical and research brain studies (Chalavi et al, 2012 ; Kim et al, 2014 ; Fillmore et al, 2015 ). These and similar studies allow for the analysis and quantification of brain tissues and important brain structures (Cover et al, 2017 ; Moeskops et al, 2018 ; Smith et al, 2019 ), and for the accurate detection of brain pathology (or abnormalities) (Sandeep et al, 2006 ; Kim et al, 2014 ; Lian et al, 2021 ). Improvements in MR acquisition techniques over the past few years have allowed for the detection of increasingly smaller structures and more subtle abnormalities.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets

Bento

Fantini

Park

et al. 2022

Front. Neuroinform.

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Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the training, validation, and testing of advanced deep learning (DL)-based automated tools, including structural magnetic resonance (MR) image-based diagnostic and treatment monitoring approaches. When assembling a number of smaller datasets to form a larger dataset, understanding the underlying variability between different acquisition and processing protocols across the aggregated dataset (termed “batch effects”) is critical. The presence of variation in the training dataset is important as it more closely reflects the true underlying data distribution and, thus, may enhance the overall generalizability of the tool. However, the impact of batch effects must be carefully evaluated in order to avoid undesirable effects that, for example, may reduce performance measures. Batch effects can result from many sources, including differences in acquisition equipment, imaging technique and parameters, as well as applied processing methodologies. Their impact, both beneficial and adversarial, must be considered when developing tools to ensure that their outputs are related to the proposed clinical or research question (i.e., actual disease-related or pathological changes) and are not simply due to the peculiarities of underlying batch effects in the aggregated dataset. We reviewed applications of DL in structural brain MR imaging that aggregated images from neuroimaging datasets, typically acquired at multiple sites. We examined datasets containing both healthy control participants and patients that were acquired using varying acquisition protocols. First, we discussed issues around Data Access and enumerated the key characteristics of some commonly used publicly available brain datasets. Then we reviewed methods for correcting batch effects by exploring the two main classes of approaches: Data Harmonization that uses data standardization, quality control protocols or other similar algorithms and procedures to explicitly understand and minimize unwanted batch effects; and Domain Adaptation that develops DL tools that implicitly handle the batch effects by using approaches to achieve reliable and robust results. In this narrative review, we highlighted the advantages and disadvantages of both classes of DL approaches, and described key challenges to be addressed in future studies.

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Data-Driven Corpus Callosum Parcellation Method Through Diffusion Tensor Imaging

Cited by 12 publications

References 40 publications

Volumetric corpus callosum segmentation integrated to inCCsight software for supporting DTI-based studies

Volumetric corpus callosum segmentation integrated to inCCsight software for supporting DTI-based studies

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

Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets

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