<![CDATA[Image segmentation plays a crucial role in medical analysis, particularly in accurately identifying anatomical structures. In dental implant planning, the identification of the Inferior Alveolar Nerve (IAN) is critical to avoid complications resulting from nerve injury. However, the manual annotation process on CBCT images is time-consuming and labor-intensive. Recent studies utilizing deep learning for IAN segmentation in 3D images often face two main challenges: limited availability of annotated data and high computational requirements.To address these challenges, this study proposes a more efficient segmentation approach based on 2.5D images. We implemented a U-Net architecture enhanced with attention gates to improve the model's focus on relevant nerve structures and increase segmentation accuracy. Furthermore, to maximize performance, predictions from multiple models were combined using ensemble learning techniques, which enhance robustness and final accuracy by leveraging the predictive strengths of diverse training samples.Experimental results demonstrate that the proposed approach achieves an average Dice score of 87.7%. These findings indicate that the combination of an attention-enhanced U-Net, the use of 2.5D imaging, and ensemble learning effectively yields accurate IAN segmentation while providing a practical solution to the challenges of data scarcity and computational complexity.]]>
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