Blar i forfatter Artikler, rapporter og annet (fysikk og teknologi) "Acuña Maldonado, Sebastian Andres"

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    • Adaptive fluctuation imaging captures rapid subcellular dynamics 

      Opstad, Ida Sundvor; Ströhl, Florian; Birgisdottir, Åsa birna; Acuña Maldonado, Sebastian Andres; Kalstad, Trine; Myrmel, Truls; Agarwal, Krishna; Ahluwalia, Balpreet Singh (Journal article; Tidsskriftartikkel, 2019-07-22)
      In this work we have explored the live-cell friendly nanoscopy method Multiple Signal Classification Algorithm (MUSICAL) for multi-colour imaging of various organelles and sub-cellular structures in the cardiomyoblast cell line H2c9. We have tested MUSICAL for fast (up to 230Hz), multi-colour time-lapse sequences of various sub-cellular structures (mitochondria, endoplasmic reticulum, microtubules, ...

    • Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning 

      Jadhav, Suyog; Acuña Maldonado, Sebastian Andres; Opstad, Ida Sundvor; Ahluwalia, Balpreet Singh; Agarwal, Krishna; Prasad, Dilip K. (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-12-08)
      Image denoising or artefact removal using deep learning is possible in the availability of supervised training dataset acquired in real experiments or synthesized using known noise models. Neither of the conditions can be fulfilled for nanoscopy (super-resolution optical microscopy) images that are generated from microscopy videos through statistical analysis techniques. Due to several physical ...

    • Fluorescence fluctuation-based super-resolution microscopy using multimodal waveguided illumination 

      Opstad, Ida Sundvor; Hansen, Daniel Henry; Acuña Maldonado, Sebastian Andres; Ströhl, Florian; Priyadarshi, Anish; Tinguely, Jean-Claude; Dullo, Firehun Tsige; Dalmo, Roy Ambli; Seternes, Tore; Ahluwalia, Balpreet Singh; Agarwal, Krishna (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-07-19)
      Photonic chip-based total internal reflection fluorescence microscopy (c-TIRFM) is an emerging technology enabling a large TIRF excitation area decoupled from the detection objective. Additionally, due to the inherent multimodal nature of wide waveguides, it is a convenient platform for introducing temporal fluctuations in the illumination pattern. The fluorescence fluctuation-based nanoscopy technique ...

    • MusiJ: an ImageJ plugin for video nanoscopy 

      Acuña Maldonado, Sebastian Andres; Ströhl, Florian; Opstad, Ida Sundvor; Ahluwalia, Balpreet S.; Agarwal, Krishna (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-04-14)
      We present an open-source implementation of the fluctuation-based nanoscopy method MUSICAL for ImageJ. This implementation improves the algorithm’s computational efficiency and takes advantage of multi-threading to provide orders of magnitude faster reconstructions than the original MATLAB implementation. In addition, the plugin is capable of generating super-resolution videos from large stacks of ...

    • Photonic-chip: a multimodal imaging tool for histopathology 

      Villegas, Luis; Dubey, Vishesh Kumar; Tinguely, Jean-Claude; Coucheron, David Andre; Priyadarshi, Anish; Acuña Maldonado, Sebastian Andres; Agarwal, Krishna; Mateos, Jose M; Nystad, Mona; Hovd, Aud-Malin Karlsson; Fenton, Kristin Andreassen; Ahluwalia, Balpreet Singh (Conference object; Konferansebidrag, 2021-04)
      We propose the photonic-chip as a multimodal imaging platform for histopathological assessment, allowing large fields-of-view across diverse microscopy methods including total internal reflection fluorescence and single-molecule localization.

    • Soft thresholding schemes for multiple signal classification algorithm 

      Acuña Maldonado, Sebastian Andres; Opstad, Ida Sundvor; Godtliebsen, Fred; Ahluwalia, Balpreet Singh; Agarwal, Krishna (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-10-28)
      Multiple signal classification algorithm (MUSICAL) exploits temporal fluctuations in fluorescence intensity to perform super-resolution microscopy by computing the value of a super-resolving indicator function across a fine sample grid. A key step in the algorithm is the separation of the measurements into signal and noise subspaces, based on a single user-specified parameter called the threshold. ...