Automatic Segmentation and Quantitative Analysis of Brain CT Volume in 2-Year-Olds Using Deep Learning Model

Fengjun Xi ¹ Liyun Tu ² Feng Zhou ² Yanjie Zhou ² Jun Ma ^1* Yun Peng ^3*

• ¹ Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
• ² School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
• ³ Imaging Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China

Objective Our research aims to develop an automated method for segmenting brain CT images in healthy 2-year-old children using the ResU-Net deep learning model. Building on this model, we aim to quantify the volumes of specific brain regions and establish a normative reference database for clinical and research applications. Methods In this retrospective study, we included 1,487 head CT scans of 2-year-old children showing normal radiological findings, which were divided into training (n=1041) and testing (n=446) sets. We preprocessed the Brain CT images by resampling, intensity normalization, and skull stripping. Then, we trained the ResU-Net model on the training set and validated it on the validation set. In addition, we compared the performance of the ResU-Net model with different kernel sizes (3×3×3 and 1×3×3 convolution kernels) against the baseline model, which was the standard 3D U-Net. The performance of the model was evaluated using the Dice similarity score. Once the segmentation model was established, we derived the regional volume parameters. We then conducted statistical analyses to evaluate differences in brain volumes by sex and hemisphere, and performed a Spearman correlation analysis to assess the relationship between brain volume and age. Results