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Abstract #5109

Automated Hippocampus Segmentation from CT Scans Using Hippocampus Dual Decoder Network (HDD-Net)

Wonjun Son1, Ji Young Lee2, Sung Jun Ahn3, and Hyunyeol Lee1
1School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Korea, Republic of, 2Department of Radiology, Seoul St. Mary's Hospital, Catholic University College of Medicine, Seoul, Korea, Republic of, 3Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea, Republic of

Synopsis

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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