FastSurferVINN: Building resolution-independence into deep learning segmentation methods—A solution for HighRes brain MRI

Henschel, Leonie; Kügler, David; Reuter, Martin

doi:10.1016/j.neuroimage.2022.118933

Cited by 29 publications

(34 citation statements)

References 67 publications

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“…Our work decreased the specificity by 3.2% using a 4-class AD detection. Taking sensitivity into consideration, a biased detection model was built in [15]; • Accuracy: Our work improved the accuracy by 1.80-40.1% compared with [15,17,18]. Comparing the result with [16], our work was 0.622% less accurate, however, we have formulated the AD detection model as four-class, in contrast to the two-class model and smaller size of the dataset in [16].…”

Section: Oasis-2mentioning

confidence: 91%

“…The remaining works [15][16][17][18] formulated the problem as binary AD detection, where the detector only determines if the participant suffers from AD (without the information of the severity); • Features and algorithms: Works [15,17] separated the feature extraction and AD detection into two parts using two algorithms. Our work and [16,18] formulated the feature extraction and AD detection with one algorithm; • Type of cross-validation: Work [13] did not employ cross-validation, which may result in insufficiency in hyperparameter tuning and evaluation of the model overfitting. Threefold cross-validation was adopted in our work, whereas fivefold cross-validation was used in [12,14].…”

Section: Oasis-2mentioning

confidence: 99%

“…The discussion of the methodology and results of the existing works is separated based on each dataset. The works are [ 11 , 12 , 13 , 14 ] for OASIS-1, [ 15 , 16 , 17 , 18 ] for OASIS-2, and [ 19 , 20 , 21 , 22 ] for OASIS-3.…”

Section: Introductionmentioning

confidence: 99%

“…The achieved accuracy was 68.8%. A voxel-sized independent neural network was used to build a binary AD detection model [ 18 ]. A subset of OASIS-2 was used for a preliminary study, where an accuracy of 88.2% was concluded.…”

Section: Introductionmentioning

confidence: 99%

“…Only a portion of the dataset was considered in the model implementation and performance analysis in works [13,14,16,18,19]; • Some of the existing works did not employ cross-validation [13,[15][16][17][18][19][20][21][22] and improperly defined the ratio of cross-validation [11] in the performance evaluation and analysis of the AD detection models. The trained models without cross-validation may not be designed with optimal sets of hyperparameters and may be more prone to model overfitting;…”

mentioning

confidence: 99%

See 4 more Smart Citations

An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning

Chui¹,

Gupta

Alhalabi

et al. 2022

Diagnostics

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Alzheimer’s disease (AD) is the most common type (>60%) of dementia and can wreak havoc on the psychological and physiological development of sufferers and their carers, as well as the economic and social development. Attributed to the shortage of medical staff, automatic diagnosis of AD has become more important to relieve the workload of medical staff and increase the accuracy of medical diagnoses. Using the common MRI scans as inputs, an AD detection model has been designed using convolutional neural network (CNN). To enhance the fine-tuning of hyperparameters and, thus, the detection accuracy, transfer learning (TL) is introduced, which brings the domain knowledge from heterogeneous datasets. Generative adversarial network (GAN) is applied to generate additional training data in the minority classes of the benchmark datasets. Performance evaluation and analysis using three benchmark (OASIS-series) datasets revealed the effectiveness of the proposed method, which increases the accuracy of the detection model by 2.85–3.88%, 2.43–2.66%, and 1.8–40.1% in the ablation study of GAN and TL, as well as the comparison with existing works, respectively.

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Section: Oasis-2mentioning

confidence: 91%

Section: Oasis-2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning

Chui¹,

Gupta

Alhalabi

et al. 2022

Diagnostics

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Sensitivity of Advanced Magnetic Resonance Imaging to Progression over Six Months in Early Spinocerebellar Ataxia

Rezende,

Petit,

Park

et al. 2024

Movement Disorders

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BackgroundClinical trials for upcoming disease‐modifying therapies of spinocerebellar ataxias (SCA), a group of rare movement disorders, lack endpoints sensitive to early disease progression, when therapeutics will be most effective. In addition, regulatory agencies emphasize the importance of biological outcomes.ObjectivesREADISCA, a transatlantic clinical trial readiness consortium, investigated whether advanced multimodal magnetic resonance imaging (MRI) detects pathology progression over 6 months in preataxic and early ataxic carriers of SCA mutations.MethodsA total of 44 participants (10 SCA1, 25 SCA3, and 9 controls) prospectively underwent 3‐T MR scanning at baseline and a median [interquartile range] follow‐up of 6.2 [5.9–6.7] months; 44% of SCA participants were preataxic. Blinded analyses of annual changes in structural, diffusion MRI, MR spectroscopy, and the Scale for Assessment and Rating of Ataxia (SARA) were compared between groups using nonparametric testing. Sample sizes were estimated for 6‐month interventional trials with 50% to 100% treatment effect size, leveraging existing large cohort data (186 SCA1, 272 SCA3) for the SARA estimate.ResultsRate of change in microstructural integrity (decrease in fractional anisotropy, increase in diffusivities) in the middle cerebellar peduncle, corona radiata, and superior longitudinal fasciculus significantly differed in SCAs from controls (P < 0.005), with high effect sizes (Cohen's d = 1–2) and moderate‐to‐high responsiveness (|standardized response mean| = 0.6–0.9) in SCAs. SARA scores did not change, and their rate of change did not differ between groups.ConclusionsDiffusion MRI is sensitive to disease progression at very early‐stage SCA1 and SCA3 and may provide a >5‐fold reduction in sample sizes relative to SARA as endpoint for 6‐month‐long trials. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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Identifying and Combating Bias in Segmentation Networks by Leveraging Multiple Resolutions

Henschel

Kügler

Andrews

et al. 2022

Lecture Notes in Computer Science

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FastSurferVINN: Building resolution-independence into deep learning segmentation methods—A solution for HighRes brain MRI

Cited by 29 publications

References 67 publications

An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning

An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning

Sensitivity of Advanced Magnetic Resonance Imaging to Progression over Six Months in Early Spinocerebellar Ataxia

Identifying and Combating Bias in Segmentation Networks by Leveraging Multiple Resolutions

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