Accurate segmentation of organs at risk (OARs) in head and neck CT scans is essential for effective radiotherapy planning. Although U-Net based deep learning models have achieved strong performance, small or anatomically complex OARs remain challenging to segment accurately. This study investigates how image slicing (cropping) influences segmentation accuracy and efficiency when using a Residual U-Net for head and neck OAR segmentation. A total of 63 CT scans from a public dataset and an institutional dataset were used. Slice-based preprocessing was applied by cropping regions surrounding target masks. Residual U-Net models were trained and tested using both full-size and sliced (cropped) images for 41 OARs. Segmentation accuracy was evaluated using Dice Similarity Coefficient (DSC) and Intersection over Union (IoU). Additional experiments incorporating dropout layers and longer training epochs were performed to improve optic chiasm and optic nerves segmentation. Slice-based networks achieved a 4.1% increase in IoU and a 3.2% increase in Dice score compared with full-size networks. For 11 complex structures, IoU and Dice scores improved by 10.9% and 9.0%, respectively. The standard deviation of both metrics decreased, indicating more consistent performance. Slice-based networks also demonstrated 130% faster training and about 8× faster prediction times. Adding dropout layers and extending training epochs further improved segmentation of the optic chiasm and optic nerves. Slice-based preprocessing combined with a Residual U-Net architecture improves segmentation accuracy and computational efficiency in head and neck CT imaging. This approach shows strong potential for practical use in radiotherapy planning.