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@cppape.bsky.social

🔬 CochleaNet has been published in Molecular Therapy 🎉 CochleaNet provides deep learning-based analysis for cochlear connectomics and gene therapy validation using high-resolution isotropic light-sheet microscopy. Our goal: quantitative therapy validation for research on hearing restoration.

Constantin Pape

@cppape.bsky.social

Gene therapies for deafness are moving towards the clinic, we provide a new tool for their pre-clinical analysis, by comprehsive quantification of SGNs, IHCs, and ribbon synapses, to analyze tonotopy, cell types, and therapy outcomes. It is also applicable to other species and other microscopy data.

Constantin Pape

@cppape.bsky.social

CochleaNet is open source, so you can apply it on your data! Check out the code and manuscript for more information: 💻 github.com/computationa... 📄 doi.org/10.1016/j.ym...

github.comGitHub - computational-cell-analytics/cochlea-net: Deep learning-based image analysis for cochleae imaged in light-sheet microscopyDeep learning-based image analysis for cochleae imaged in light-sheet microscopy - computational-cell-analytics/cochlea-net

Constantin Pape

@cppape.bsky.social

This project was only possible through an amazing collaboration between Auditory Neuroscience #MoserLab, Computer Science #PapeLab, and Light Sheet Microscopy #HuiskenLab. A big thank you to Lennart and Aleyna who lead this effort, Martin who drove the software design, and all other co-authors!

26/10/26, 7:48 am0 reposts2 likes

Constantin Pape

@cppape.bsky.social

The project was supported by the SFB 1690 for Disease Mechanisms and Functional Restoration of Sensory and Motor Systems, MBExC, the Cluster of Excellence Multiscale Bioimaging, and the EKFZ-OT - Else Kröner Fresenius Center for Optogenetic Therapies.