Browsing by Author "Karayannis, Theofanis"

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Enhancing Two-Photon Images for Anatomical Visualisation Using Super-Resolution
(IEEE, 2022) Aydeniz, Burhan; Metin, Safa Can; Turkan, Mehmet; Unay, Devrim; Karayannis, Theofanis; Argunsah, Ali Ozgur
Two-photon Laser Scanning Microscopy (2P-LSM) is a technique used to image the living tissue with relatively high spatio-temporal resolution. However, the time-series images are often corrupted with Poisson-Gaussian noise and deteriorated with motion artifacts. This paper deals with the problem of enhancing 2P-LSM images to reconstruct high quality and high spatial-resolution outputs using the observed time-series stack of low-resolution images. The proposed technique consists of several components including noise filtering, image registration, cell detection and focus measurement, and clustering for a joint denoising and super-resolution. Extensive experiments demonstrate that the proposed method results in gratifying output images containing apparent and clear cell forms at different focus levels.
Citation - WoS: 12
Citation - Scopus: 14
An interactive time series image analysis software for dendritic spines
(Nature Portfolio, 2022) Argunsah, Ali Ozgur; Erdil, Ertunc; Ghani, Muhammad Usman; Ramiro-Cortes, Yazmin; Hobbiss, Anna F.; Karayannis, Theofanis; Cetin, Mujdat
Live fluorescence imaging has demonstrated the dynamic nature of dendritic spines, with changes in shape occurring both during development and in response to activity. The structure of a dendritic spine correlates with its functional efficacy. Learning and memory studies have shown that a great deal of the information stored by a neuron is contained in the synapses. High precision tracking of synaptic structures can give hints about the dynamic nature of memory and help us understand how memories evolve both in biological and artificial neural networks. Experiments that aim to investigate the dynamics behind the structural changes of dendritic spines require the collection and analysis of large time-series datasets. In this paper, we present an open-source software called SpineS for automatic longitudinal structural analysis of dendritic spines with additional features for manual intervention to ensure optimal analysis. We have tested the algorithm on in-vitro, in-vivo, and simulated datasets to demonstrate its performance in a wide range of possible experimental scenarios.