发表机构
Insight Research Ireland Centre for Data Analytics, Dublin City University; Rutgers University; Oregon Health & Science University（都柏林城市大学爱尔兰洞察研究数据中心; 罗格斯大学; 俄勒冈健康与科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对人工耳蜗植入导致的残余听力损失问题，构建新型带纤维化的植入耳蜗OCT数据集，采用改进的UNET模型（2D-OCT-UNET）实现纤维化量化，为相关研究提供了可靠工具与数据支持。
AI 中文摘要
目的：人工耳蜗（CIs）是通过电刺激听觉神经恢复听力的仿生假体。混合人工耳蜗（Hybrid CIs）采用电声刺激（EAS），将残余低频声学听力与人工耳蜗电刺激相结合。植入物引发的耳蜗内纤维化可能会阻碍残余听力功能，并逐渐降低电声刺激的疗效。因此，研究啮齿动物的耳蜗纤维化形成以减少纤维化负担、改善人工耳蜗患者预后是一项转化研究目标。方法：作为正在进行的植入物诱导纤维化研究的一部分，我们生成并注释了一组来自慢性植入豚鼠的新型光学相干断层扫描（OCT）图像数据集。在该模型中客观评估纤维化负担，具有高分辨率和可重复性，这为计算机视觉方法提供了明确的应用场景。结果：我们使用新的手动分割OCT图像库，展示了多种最先进的语义分割模型的结果，并比较了它们识别耳蜗纤维化及其他相关注释的效能。结论：我们发现，采用著名的UNET架构的改进版本（我们将其命名为2D-OCT-UNET），对放大后的OCT输入分辨率进行操作，可实现最佳性能。意义：我们首次成功将计算机视觉技术应用于带有纤维化的植入耳蜗OCT数据集。正如我们在实验部分所验证的，使用该深度学习模型可可靠地计算耳蜗纤维化负担。数据集和项目代码可在以下网址获取：this https URL
英文摘要
Objective: Cochlear implants (CIs) are bionic prostheses that restores hearing via electrical stimulation of the auditory nerve. Hybrid CIs, which use electroacoustic stimulation (EAS), combine residual low-frequency acoustic hearing with CI electrical stimulation. Intracochlear fibrosis, which forms in response to the presence of the implant, may impede residual hearing function and gradually reduce the efficacy of EAS. It is therefore a translational objective to study the formation of cochlear fibrosis in rodents, with the goal of reducing fibrotic burden and improving outcomes for CI patients. Methods: We generate and annotate a novel dataset of optical coherence tomography (OCT) images from chronically implanted guinea pigs as part of an ongoing study focused on implant induced fibrosis. Objectively assessing fibrotic burden in this model, with high resolution and repeatability, presents an obvious use case for computer vision methods. Results: We present the results of several state-of-the-art semantic segmentation models and compare their efficacy for identifying cochlear fibrosis and other relevant annotations, using a new library of manually segmented OCT images. Conclusions: We find that the best performance is achieved by using a modified version of the well-known UNET architecture (which we term 2D-OCT-UNET) that operates on the upscaled OCT input resolution. Significance: For the first time, we have successfully applied computer vision techniques to an OCT dataset of implanted cochleae with fibrosis. Using this deep learning model, the cochlear fibrotic burden calculation can be reliably carried out as we verify in our experimental section. The dataset and the project code are available at: https://github.com/juliadietlmeier/CF-OCT-segmentation
CommentsCopyright 2026 IEEE. Personal use of this material is permitted. Citation/DOI: 10.1109/TBME.2025.3537868
Journal refIEEE Transactions on Biomedical Engineering, 72(7), pp. 2218-2228, July 2025
