A complete list of our publications are available on Google Scholar, please find below our highlighted work (all Open Access).


Kist, A. M., Dürr S, Schützenberger A, and Döllinger M. OpenHSV: An open platform for laryngeal high-speed videoendoscopy. Scientific reports, 11 (2021), 13760.
Article / Code / Docs / Award

Commercial available systems for laryngeal high-speed videoendoscopy have not been further developed lately, are closed-source and have only very limited analysis capacities. With OpenHSV, we provide a novel, award-winning, open hard- and software platform with DNN-powered online analysis.

Kist, A. M., and Michael Döllinger. Efficient biomedical image segmentation on Edge TPUs. Accepted as short paper at Medical Imaging with Deep Learning (MIDL), 2021

We highlight at MIDL our work on semantic segmentation using Edge TPUs.

Kist, A. M., Zilker J., Döllinger M., & Semmler M. Feature-based image registration in structured light endoscopy. Accepted as full paper at Medical Imaging with Deep Learning (MIDL), 2021
Article / Code

Structured light endoscopy is a 3D-imaging method. However, the assignment of a projected laser grid to its reference is still tricky. We propose a Deep Learning-based image registration approach that achieves 91% accuracy on an ex vivo dataset.

Kist, A. M., Gómez P., Dubrovskiy D., Schlegel O., Kunduk M., Echternach M., Patel RR., Semmler M., Bohr C., Dürr S., Schützenberger A., & Döllinger M. A Deep Learning Enhanced Novel Software Tool for Laryngeal Dynamics Analysis. J Speech Lang Hear R, 64 (6), 1889-1903.

The analysis of high-speed videoendoscopy data is crucial for voice quantification. In this paper, we describe the Glottis Analysis Tools (GAT). GAT has been actively developed in C# since 2010 and is used by dozens of labs worldwide.


Kist, A. M., and Michael Döllinger. Efficient Biomedical Image Segmentation on EdgeTPUs at Point of Care. IEEE Access 8 (2020): 139356-139366.

Deep neural networks are changing the way of biomedical diagnosis. For image segmentation, they can be very large and slow, especially on CPUs. In our recent study, we show that we can improve the glottis segmentation inference speed >79x fold by optimization a popular biomedical segmentation network (U-Net) and porting it to the inexpensive EdgeTPU Hardware Accelerator.

Kist, A. M., Zilker, J., Gómez, P., Schützenberger, A., & Döllinger, M. (2020). Rethinking glottal midline detection. Scientific reports10(1), 1-15.
Article / Code

Symmetry is important in vocal fold motion. The identification of the glottal midline is crucial to derive symmetry from the glottal area. Here, we evaluate different approaches to determine the glottal midline and suggest a multi-task architecture, GlottisNet, that predicts both simultaneously, glottis segmentation and glottal midline.

Gómez, P.*, Kist, A. M.*, Schlegel, P., Berry, D. A., Chhetri, D. K., Dürr, S., … & Döllinger, M. (2020). BAGLS, a multihospital benchmark for automatic glottis segmentation. Scientific data7(1), 1-12.
Article / Code / Dataset

Glottis segmentation is a key component for analyzing the vocal fold vibrations. With BAGLS, we provide the first open, multihospital dataset for training and evaluating deep neural networks.


Kist, A. M., & Portugues, R. (2019). Optomotor swimming in larval zebrafish is driven by global whole-field visual motion and local light-dark transitions. Cell Reports29(3), 659-670.

Larval zebrafish swim when they perceive a whole-field moving stimulus. However, the underlying features that drive optomotor swimming remain elusive. Here, we show that larval zebrafish are pre-dominantly driven by local light-dark transitions.

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