Dietlmeier Julia, Greenberg Benjamin, He Wenxuan, Wilson Teresa, Xing Rubing, Hill Jordan, Fettig Adrienne, Otto Madeline, Rounsavill Teyhana, Reiss Lina A J, Yi Jingang, O'Connor Noel E, Burwood George W S
IEEE Trans Biomed Eng. 2025 Jul;72(7):2218-2228. doi: 10.1109/TBME.2025.3537868.
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.
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.
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.
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.
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.
人工耳蜗（CI）是通过电刺激听神经来恢复听力的仿生假体。采用电声刺激（EAS）的混合式人工耳蜗将残余低频声学听力与人工耳蜗电刺激相结合。植入物存在时形成的耳蜗内纤维化可能会妨碍残余听力功能，并逐渐降低电声刺激的效果。因此，研究啮齿动物耳蜗纤维化的形成是一个转化医学目标，旨在减轻纤维化负担并改善人工耳蜗患者的治疗效果。
作为一项正在进行的聚焦于植入物诱导纤维化研究的一部分，我们生成并注释了来自长期植入豚鼠的光学相干断层扫描（OCT）图像的新数据集。利用计算机视觉方法，在该模型中以高分辨率和可重复性客观评估纤维化负担，是一个明显的应用案例。
我们展示了几种最先进的语义分割模型的结果，并使用一个新的手动分割OCT图像库比较了它们在识别耳蜗纤维化和其他相关注释方面的效果。
我们发现，通过使用对著名的UNET架构进行修改后的版本（我们称之为2D-OCT-UNET），在放大后的OCT输入分辨率上运行，可实现最佳性能。
我们首次成功地将计算机视觉技术应用于有纤维化的植入耳蜗的OCT数据集。正如我们在实验部分所验证的，使用这个深度学习模型，可以可靠地进行耳蜗纤维化负担计算。
