Fastsurfer-a fast and accurate deep learning based neuroimaging pipeline
Traditional neuroimage analysis pipelines involve computationally intensive, time-
consuming optimization steps, and thus, do not scale well to large cohort studies with …
consuming optimization steps, and thus, do not scale well to large cohort studies with …
Transfer learning using convolutional neural network architectures for brain tumor classification from MRI images
Brain tumor classification is very important in medical applications to develop an effective
treatment. In this paper, we use brain contrast-enhanced magnetic resonance images (CE …
treatment. In this paper, we use brain contrast-enhanced magnetic resonance images (CE …
Deep learning for the fully automated segmentation of the inner ear on MRI
A Vaidyanathan, MFJA van der Lubbe… - Scientific reports, 2021 - nature.com
Segmentation of anatomical structures is valuable in a variety of tasks, including 3D
visualization, surgical planning, and quantitative image analysis. Manual segmentation is …
visualization, surgical planning, and quantitative image analysis. Manual segmentation is …
Subty** of mild cognitive impairment using a deep learning model based on brain atrophy patterns
Trajectories of cognitive decline vary considerably among individuals with mild cognitive
impairment (MCI). To address this heterogeneity, subty** approaches have been …
impairment (MCI). To address this heterogeneity, subty** approaches have been …
[HTML][HTML] MicroRNAs as potential biomarkers for diagnosis of major depressive disorder and influence of antidepressant treatment
B Martinez, PV Peplow - NeuroMarkers, 2024 - Elsevier
We performed a PubMed search for microRNA biomarkers in major depressive disorder
(MDD) and found 25 original research articles on studies performed with human patients …
(MDD) and found 25 original research articles on studies performed with human patients …
Explainable anatomical shape analysis through deep hierarchical generative models
Quantification of anatomical shape changes currently relies on scalar global indexes which
are largely insensitive to regional or asymmetric modifications. Accurate assessment of …
are largely insensitive to regional or asymmetric modifications. Accurate assessment of …
Hippocampus segmentation on epilepsy and Alzheimer's disease studies with multiple convolutional neural networks
Background: Hippocampus segmentation on magnetic resonance imaging is of key
importance for the diagnosis, treatment decision and investigation of neuropsychiatric …
importance for the diagnosis, treatment decision and investigation of neuropsychiatric …
A non-invasive, automated diagnosis of Menière's disease using radiomics and machine learning on conventional magnetic resonance imaging: A multicentric, case …
MFJA van der Lubbe, A Vaidyanathan, M de Wit… - La radiologia …, 2022 - Springer
Purpose This study investigated the feasibility of a new image analysis technique
(radiomics) on conventional MRI for the computer-aided diagnosis of Menière's disease …
(radiomics) on conventional MRI for the computer-aided diagnosis of Menière's disease …
[HTML][HTML] Identification of microRNA-9 linking the effects of childhood maltreatment on depression using amygdala connectivity
C He, Y Bai, Z Wang, D Fan, Q Wang, X Liu, H Zhang… - Neuroimage, 2021 - Elsevier
Childhood maltreatment (CM) is regarded as an important risk factor for major depressive
disorder (MDD). However, the neural links corresponding to the process of early CM …
disorder (MDD). However, the neural links corresponding to the process of early CM …
[HTML][HTML] A sco** review of automatic and semi-automatic MRI segmentation in human brain imaging
Introduction AI-based segmentation techniques in brain MRI have revolutionized
neuroimaging by enhancing the accuracy and efficiency of brain structure analysis. These …
neuroimaging by enhancing the accuracy and efficiency of brain structure analysis. These …