Deep Learning Approaches for Imaging-Based Automated Segmentation of Tuberous Sclerosis Complex.

ABSTRACT: The present study presents a novel approach for identifying epileptogenic tubers in patients with tuberous sclerosis complex (TSC) and automating tuber segmentation using a three-dimensional convolutional neural network (3D CNN). The study retrospectively included 31 TSC patients whose lesions were manually annotated from multiparametric neuroimaging data. Epileptogenic tubers were determined via presurgical evaluation and stereoelectroencephalography recording. Neuroimaging metrics were extracted and compared between epileptogenic and non-epileptogenic tubers. Additionally, five datasets with different preprocessing strategies were used to construct and train 3D CNNs for automated tuber segmentation. The normalized positron emission tomography (PET) metabolic value was significantly lower

SUBMITTER: Zhao X 

PROVIDER: S-EPMC10856546 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature