Shape Information Improves the Cross-Cohort Performance of Deep Learning-Based Segmentation of the Hippocampus

Brusini, Irene; Lindberg, Olof; Smedby, Örjan; Westman, Eric; Wang, Chunliang

doi:10.3389/fnins.2020.00015

Cited by 23 publications

(19 citation statements)

References 47 publications

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“…U-Net, developed in 2015, is a CNN-based architecture for biomedical image segmentation that performs segmentation by classifying at every pixel/voxel (Ronneberger et al, 2015 ). Many studies have used U-Nets to perform semantic segmentation in MRI (Wang et al, 2019 ; Wu et al, 2019 ; Brusini et al, 2020 ; Henschel et al, 2020 ; Zhang et al, 2021 ) and few in CT (Van De Leemput et al, 2019 ; Akkus et al, 2020 ). An influential and challenging aspect of deep learning-based studies, especially segmentation-based tasks is the selection of data and labels used for training (Willemink et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Two-Dimensional- and Three-Dimensional-Based U-Net Architectures for Brain Tissue Classification in One-Dimensional Brain CT

Srikrishna

Heckemann

Pereira

et al. 2022

Front. Comput. Neurosci.

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Brain tissue segmentation plays a crucial role in feature extraction, volumetric quantification, and morphometric analysis of brain scans. For the assessment of brain structure and integrity, CT is a non-invasive, cheaper, faster, and more widely available modality than MRI. However, the clinical application of CT is mostly limited to the visual assessment of brain integrity and exclusion of copathologies. We have previously developed two-dimensional (2D) deep learning-based segmentation networks that successfully classified brain tissue in head CT. Recently, deep learning-based MRI segmentation models successfully use patch-based three-dimensional (3D) segmentation networks. In this study, we aimed to develop patch-based 3D segmentation networks for CT brain tissue classification. Furthermore, we aimed to compare the performance of 2D- and 3D-based segmentation networks to perform brain tissue classification in anisotropic CT scans. For this purpose, we developed 2D and 3D U-Net-based deep learning models that were trained and validated on MR-derived segmentations from scans of 744 participants of the Gothenburg H70 Cohort with both CT and T1-weighted MRI scans acquired timely close to each other. Segmentation performance of both 2D and 3D models was evaluated on 234 unseen datasets using measures of distance, spatial similarity, and tissue volume. Single-task slice-wise processed 2D U-Nets performed better than multitask patch-based 3D U-Nets in CT brain tissue classification. These findings provide support to the use of 2D U-Nets to segment brain tissue in one-dimensional (1D) CT. This could increase the application of CT to detect brain abnormalities in clinical settings.

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Section: Introductionmentioning

confidence: 99%

Comparison of Two-Dimensional- and Three-Dimensional-Based U-Net Architectures for Brain Tissue Classification in One-Dimensional Brain CT

Srikrishna

Heckemann

Pereira

et al. 2022

Front. Comput. Neurosci.

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“…This result was expected, considering the following two aspects jointly: (1) CC segmentation on FLAIR scans was already found to be the most challenging task of the present work; (2) the networks were trained not only on cohorts that differ from those used for validation but also using a lower amount of data (2/3 of the dataset instead of 9/10). In the future, this issue may be addressed by applying further changes to the architecture; for example, adding CC shape prior information as an additional input to the network, an approach that was found to improve the segmentation accuracy in previous MRI segmentation studies 28,29 …”

Section: Discussionmentioning

confidence: 99%

Automatic deep learning multicontrast corpus callosum segmentation in multiple sclerosis

Brusini

Plattén

Ouellette

et al. 2022

Journal of Neuroimaging

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Background and Purpose Corpus callosum (CC) atrophy is predictive of future disability in multiple sclerosis (MS). However, current segmentation methods are either labor‐ or computationally intensive. We therefore developed an automated deep learning‐based CC segmentation tool and hypothesized that its output would correlate with disability. Methods A cohort of 631 MS patients (449 females, baseline age 41 ± 11 years) with both 3‐dimensional T1‐weighted and T2‐weighted fluid‐attenuated inversion recovery (FLAIR) MRI was used for the development. Data from 204 patients were manually segmented to train convolutional neural networks in extracting the midsagittal intracranial and CC areas. Remaining data were used to compare segmentations with FreeSurfer and benchmark the outputs with regard to clinical correlations. A 1.5 and 3 Tesla reproducibility cohort of 9 MS patients evaluated the segmentation robustness. Results The deep learning‐based tool was accurate in selecting the appropriate slice for segmentation (98% accuracy within 3 mm of the manual ground truth) and segmenting the CC (Dice coefficient .88‐.91) and intracranial areas (.97‐.98). The accuracy was lower with higher atrophy. Reproducibility was excellent (intraclass correlation coefficient > .90) for T1‐weighted scans and moderate‐good for FLAIR (.74‐.75). Segmentations were associated with baseline and future (average follow‐up time 6‐7 years) Expanded Disability Status Scale (ρ = –.13 to –.24) and Symbol Digit Modalities Test (r = .18‐.29) scores. Conclusions We present a fully automatic deep learning‐based CC segmentation tool optimized to modern imaging in MS with clinical correlations on par with computationally expensive alternatives.

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“…Meanwhile, an automated sub-cortical brain structure segmentation approach based on a CNN architecture outperformed state-of-the-art techniques such as Free Surfer on the Internet Brain Segmentation Repository (IBSR 18) dataset [29]. A DL-based hippocampus segmentation framework embedding statistical shape of the hippocampus as "context information" into DNN was proposed and tested on image data of AD, MCI, and CN subjects from two cohorts from ADNI and AddNeuroMed, leading to improved segmentation accuracy in cross-cohort validation [30]. Notably, DL can be used as a feature extractor before classification tasks reducing the need for rigid segmentation in preprocessing: a multiple dense CNN was used on an ADNI dataset, including 199 AD patients, 403 MCI, and 229 CN.…”

Section: Neuroimaging Classification and Segmentationmentioning

confidence: 99%

Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence

Termine

Fabrizio

Strafella

et al. 2021

JPM

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In the big data era, artificial intelligence techniques have been applied to tackle traditional issues in the study of neurodegenerative diseases. Despite the progress made in understanding the complex (epi)genetics signatures underlying neurodegenerative disorders, performing early diagnosis and developing drug repurposing strategies remain serious challenges for such conditions. In this context, the integration of multi-omics, neuroimaging, and electronic health records data can be exploited using deep learning methods to provide the most accurate representation of patients possible. Deep learning allows researchers to find multi-modal biomarkers to develop more effective and personalized treatments, early diagnosis tools, as well as useful information for drug discovering and repurposing in neurodegenerative pathologies. In this review, we will describe how relevant studies have been able to demonstrate the potential of deep learning to enhance the knowledge of neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases through the integration of all sources of biomedical data.

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Shape Information Improves the Cross-Cohort Performance of Deep Learning-Based Segmentation of the Hippocampus

Cited by 23 publications

References 47 publications

Comparison of Two-Dimensional- and Three-Dimensional-Based U-Net Architectures for Brain Tissue Classification in One-Dimensional Brain CT

Comparison of Two-Dimensional- and Three-Dimensional-Based U-Net Architectures for Brain Tissue Classification in One-Dimensional Brain CT

Automatic deep learning multicontrast corpus callosum segmentation in multiple sclerosis

Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence

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