PubMed Mentions
The links below are to publications on PubMed referring to Anima. This list is gathered weekly from PubMed automatically and was last updated on August 29, 2026.
| Publication/References | |
| White matter abnormalities in depression: A categorical and phenotypic diffusion MRI study. Description: Coloigner, Julie, et al. White matter abnormalities in depression: A categorical and phenotypic diffusion MRI study. ''Neuroimage Clin''. 2019; '''22''': 101710 | |
| Regional brain development analysis through registration using anisotropic similarity, a constrained affine transformation. Description: Legouhy, Antoine, et al. Regional brain development analysis through registration using anisotropic similarity, a constrained affine transformation. ''PLoS One''. 2020; '''15''' (2):e0214174 | |
| A Clinically-Compatible Workflow for Computer-Aided Assessment of Brain Disease Activity in Multiple Sclerosis Patients. Description: Combes, Benoit, et al. A Clinically-Compatible Workflow for Computer-Aided Assessment of Brain Disease Activity in Multiple Sclerosis Patients. ''Front Med (Lausanne)''. 2021; '''8''': 740248 | |
| Multiple sclerosis lesions segmentation from multiple experts: The MICCAI 2016 challenge dataset. Description: Commowick, Olivier, et al. Multiple sclerosis lesions segmentation from multiple experts: The MICCAI 2016 challenge dataset. ''Neuroimage''. 2021 Dec 1; '''244''': 118589 | |
| Improving the detection of new lesions in multiple sclerosis with a cascaded 3D fully convolutional neural network approach. Description: Salem, Mostafa, et al. Improving the detection of new lesions in multiple sclerosis with a cascaded 3D fully convolutional neural network approach. ''Front Neurosci''. 2022; '''16''': 1007619 | |
| New lesion segmentation for multiple sclerosis brain images with imaging and lesion-aware augmentation. Description: Basaran, Berke Doga, et al. New lesion segmentation for multiple sclerosis brain images with imaging and lesion-aware augmentation. ''Front Neurosci''. 2022; '''16''': 1007453 | |
| New MS lesion segmentation with deep residual attention gate U-Net utilizing 2D slices of 3D MR images. Description: Sarica, Beytullah, et al. New MS lesion segmentation with deep residual attention gate U-Net utilizing 2D slices of 3D MR images. ''Front Neurosci''. 2022; '''16''': 912000 | |
| New multiple sclerosis lesion segmentation and detection using pre-activation U-Net. Description: Ashtari, Pooya, et al. New multiple sclerosis lesion segmentation and detection using pre-activation U-Net. ''Front Neurosci''. 2022; '''16''': 975862 | |
| Triplanar U-Net with lesion-wise voting for the segmentation of new lesions on longitudinal MRI studies. Description: Hitziger, Sebastian, et al. Triplanar U-Net with lesion-wise voting for the segmentation of new lesions on longitudinal MRI studies. ''Front Neurosci''. 2022; '''16''': 964250 | |
| Perturbed neurochemical and microstructural organization in a mouse model of prenatal opioid exposure: A multi-modal magnetic resonance study. Description: Shahid, Syed Salman, et al. Perturbed neurochemical and microstructural organization in a mouse model of prenatal opioid exposure: A multi-modal magnetic resonance study. ''PLoS One''. 2023; '''18''' (7):e0282756 | |
| Volumetric and microstructural abnormalities of the amygdala in focal epilepsy with varied levels of SUDEP risk. Description: Legouhy, Antoine, et al. Volumetric and microstructural abnormalities of the amygdala in focal epilepsy with varied levels of SUDEP risk. ''medRxiv''. 2023 Apr 4; | |
| Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord. Description: Snoussi, Haykel, et al. Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord. ''Brain Behav''. 2023 Nov; '''13''' (11):e3159 | |
| LST-AI: a Deep Learning Ensemble for Accurate MS Lesion Segmentation. Description: Wiltgen, Tun, et al. LST-AI: a Deep Learning Ensemble for Accurate MS Lesion Segmentation. ''medRxiv''. 2023 Nov 24; | |
| Estimating the synaptic density deficit in Alzheimer's disease using multi-contrast CEST imaging. Description: Shahid, Syed Salman, et al. Estimating the synaptic density deficit in Alzheimer's disease using multi-contrast CEST imaging. ''PLoS One''. 2024; '''19''' (3):e0299961 | |
| LST-AI: A deep learning ensemble for accurate MS lesion segmentation. Description: Wiltgen, Tun, et al. LST-AI: A deep learning ensemble for accurate MS lesion segmentation. ''Neuroimage Clin''. 2024; '''42''': 103611 | |
| State rumination predicts inhibitory control failures and dysregulation of default, salience, and cognitive control networks in youth at risk of depressive relapse: Findings from the RuMeChange trial. Description: Roberts, Henrietta, et al. State rumination predicts inhibitory control failures and dysregulation of default, salience, and cognitive control networks in youth at risk of depressive relapse: Findings from the RuMeChange trial. ''J Affect Disord Rep''. 2024 Apr; '''16''': | |
| Motor tract lesion mapping from the brain to the lower spinal cord in people with relapsing-remitting multiple sclerosis: exploring the association between lesion severity and functional consequences by limb. Description: Gaubert, Malo, et al. Motor tract lesion mapping from the brain to the lower spinal cord in people with relapsing-remitting multiple sclerosis: exploring the association between lesion severity and functional consequences by limb. ''Brain Commun''. 2026; '''8''' (3):fcag140 | |
| Association Between Motor Pathway Damage and Motor Deficit in Upper and Lower Limb in People With MS. Description: Liffran, Mathilde, et al. Association Between Motor Pathway Damage and Motor Deficit in Upper and Lower Limb in People With MS. ''Ann Clin Transl Neurol''. 2026 May 8; | |
| Performances of experts and automated methods on new multiple sclerosis lesions detection: insights from the MSSeg2 challenge. Description: Masson, Arthur, et al. Performances of experts and automated methods on new multiple sclerosis lesions detection: insights from the MSSeg2 challenge. ''Sci Rep''. 2026 May 21; '''16''' (1): |