Keywords: Analysis/Processing, Analysis/Processing, Hippocampus segmentation
Motivation: Computed tomography (CT) imaging has been widely used in clinical practice, but its low contrast for the hippocampus makes it challenging to achieve accurate segmentation of the structure.
Goal(s): In this study, we developed a novel deep learning (DL) model, termed HDD-Net, for automatic hippocampal segmentation from CT images.
Approach: HDD-Net features dual, parallel decoders sensitizing hippocampal regions and their boundaries, respectively, and feature fusion module and cross loss that assist inter-decoder interactions.
Results: HDD-Net outperforms existing U-Net-based models in terms of Dice and IOU. Statistical analyses revealed good agreement between CT- and MRI-derived hippocampal volumes.
Impact: CT-based hippocampal segmentation via HDD-Net would be a promising alternative to conventional MRI-based procedures, and is expected to find a number of applications, for example, studies on Alzheimer’s disease.
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