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arXiv AI By Julia Dietlmeier, Benjamin Greenberg, Wenxuan He, Teresa Wilson, Rubing Xing, Jordan Hill, Adrienne Fettig, Madeline Otto, Teyhana Rounsavill, Lina A. J. Reiss, Jingang Yi, Noel E. O'Connor, George W. S. Burwood

Towards Investigating Residual Hearing Loss: Quantification of Fibrosis in a Novel Cochlear OCT Dataset

Read the original on arXiv AI →

The study introduces a new optical coherence tomography (OCT) dataset of chronically implanted guinea pig cochleae to quantify intracochlear fibrosis, a factor that can impair residual hearing in cochlear implant users. Researchers applied several semantic segmentation models, finding that a modified UNET architecture (2D‑OCT‑UNET) on upscaled images achieved the best performance in identifying fibrosis and related structures. The dataset and code are publicly available for further research.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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