{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["33(3)"],"submitter":["Opfer R"],"funding":["Universitätsklinikum Hamburg-Eppendorf (UKE)"],"pubmed_abstract":["Objectives
To develop an automatic method for accurate and robust thalamus segmentation in T1w-MRI for widespread clinical use without the need for strict harmonization of acquisition protocols and/or scanner-specific normal databases.Methods
A three-dimensional convolutional neural network (3D-CNN) was trained on 1975 T1w volumes from 170 MRI scanners using thalamus masks generated with FSL-FIRST as ground truth. Accuracy was evaluated with 18 manually labeled expert masks. Intra- and inter-scanner test-retest stability were assessed with 477 T1w volumes of a single healthy subject scanned on 123 MRI scanners. The sensitivity of 3D-CNN-based volume estimates for the detection of thalamus atrophy was tested with 127 multiple sclerosis (MS) patients and a normal database com"],"journal":["European radiology"],"pagination":["1852-1861"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9935653"],"repository":["biostudies-literature"],"pubmed_title":["Automatic segmentation of the thalamus using a massively trained 3D convolutional neural network: higher sensitivity for the detection of reduced thalamus volume by improved inter-scanner stability."],"pmcid":["PMC9935653"],"pubmed_authors":["Kruger J","Buchert R","Opfer R","Spies L","Ostwaldt AC","Kitzler HH","Schippling S"],"additional_accession":[]},"is_claimable":false,"name":"Automatic segmentation of the thalamus using a massively trained 3D convolutional neural network: higher sensitivity for the detection of reduced thalamus volume by improved inter-scanner stability.","description":"Objectives
To develop an automatic method for accurate and robust thalamus segmentation in T1w-MRI for widespread clinical use without the need for strict harmonization of acquisition protocols and/or scanner-specific normal databases.Methods
A three-dimensional convolutional neural network (3D-CNN) was trained on 1975 T1w volumes from 170 MRI scanners using thalamus masks generated with FSL-FIRST as ground truth. Accuracy was evaluated with 18 manually labeled expert masks. Intra- and inter-scanner test-retest stability were assessed with 477 T1w volumes of a single healthy subject scanned on 123 MRI scanners. The sensitivity of 3D-CNN-based volume estimates for the detection of thalamus atrophy was tested with 127 multiple sclerosis (MS) patients and a normal database com","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Mar","modification":"2025-04-22T11:59:06.888Z","creation":"2024-10-18T18:19:16.96Z"},"accession":"S-EPMC9935653","cross_references":{"pubmed":["36264314"],"doi":["10.1007/s00330-022-09170-y"]}}