@inproceedings{PalmSiegmundSemmelmannetal., author = {Palm, Christoph and Siegmund, Heiko and Semmelmann, Matthias and Grafe, Claudia and Evert, Matthias and Schroeder, Josef A.}, title = {Interactive Computer-assisted Approach for Evaluation of Ultrastructural Cilia Abnormalities}, series = {Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, California, United States, 27 February - 3 March, SPIE Proceedings 97853N, 2016, ISBN 9781510600201}, booktitle = {Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, California, United States, 27 February - 3 March, SPIE Proceedings 97853N, 2016, ISBN 9781510600201}, doi = {10.1117/12.2214976}, pages = {7}, abstract = {Introduction - Diagnosis of abnormal cilia function is based on ultrastructural analysis of axoneme defects, especialy the features of inner and outer dynein arms which are the motors of ciliar motility. Sub-optimal biopsy material, methodical, and intrinsic electron microscopy factors pose difficulty in ciliary defects evaluation. We present a computer-assisted approach based on state-of-the-art image analysis and object recognition methods yielding a time-saving and efficient diagnosis of cilia dysfunction. Method - The presented approach is based on a pipeline of basal image processing methods like smoothing, thresholding and ellipse fitting. However, integration of application specific knowledge results in robust segmentations even in cases of image artifacts. The method is build hierarchically starting with the detection of cilia within the image, followed by the detection of nine doublets within each analyzable cilium, and ending with the detection of dynein arms of each doublet. The process is concluded by a rough classification of the dynein arms as basis for a computer-assisted diagnosis. Additionally, the interaction possibilities are designed in a way, that the results are still reproducible given the completion report. Results - A qualitative evaluation showed reasonable detection results for cilia, doublets and dynein arms. However, since a ground truth is missing, the variation of the computer-assisted diagnosis should be within the subjective bias of human diagnosticians. The results of a first quantitative evaluation with five human experts and six images with 12 analyzable cilia showed, that with default parameterization 91.6\% of the cilia and 98\% of the doublets were found. The computer-assisted approach rated 66\% of those inner and outer dynein arms correct, where all human experts agree. However, especially the quality of the dynein arm classification may be improved in future work.}, subject = {Zilie}, language = {en} } @misc{SchroederSemmelmannSiegmundetal., author = {Schroeder, Josef A. and Semmelmann, Matthias and Siegmund, Heiko and Grafe, Claudia and Evert, Matthias and Palm, Christoph}, title = {Improved interactive computer-assisted approach for evaluation of ultrastructural cilia abnormalities}, series = {Ultrastructural Pathology}, volume = {41}, journal = {Ultrastructural Pathology}, number = {1}, doi = {10.1080/01913123.2016.1270978}, pages = {112 -- 113}, subject = {Zilie}, language = {en} } @inproceedings{CantalloubeGomezGonzalezAbsiletal., author = {Cantalloube, Faustine and Gomez-Gonzalez, Carlos and Absil, Olivier and Cantero, Carles and Bacher, Regis and Bonse, Markus and Bottom, Michael and Dahlqvist, Carl-Henrik and Desgrange, C{\´e}lia and Flasseur, Olivier and Fuhrmann, Thomas and Henning, Thomas H. and Jensen-Clem, Rebecca and Kenworthy, Matthew and Mawet, Dimitri and Mesa, Dino and Meshkat, Tiffany and Mouillet, David and M{\"u}ller, Andr{\´e} and Nasedkin, Evert and Pairet, Benoit and Pi{\´e}rard, S{\´e}bastien and Ruffio, Jean-Baptiste and Samland, Matthias and Stone, Jordan and van Droogenbroeck, Marc}, title = {Exoplanet imaging data challenge: benchmarking the various image processing methods for exoplanet detection}, series = {Adaptive Optics Systems VII : 14-22 December 2020, online only, United States}, booktitle = {Adaptive Optics Systems VII : 14-22 December 2020, online only, United States}, editor = {Schmidt, Dirk and Schreiber, Laura and Vernet, Elise}, publisher = {SPIE}, isbn = {9781510636835}, doi = {10.1117/12.2574803}, pages = {321}, abstract = {The Exoplanet Imaging Data Challenge is a community-wide effort meant to offer a platform for a fair and common comparison of image processing methods designed for exoplanet direct detection. For this purpose, it gathers on a dedicated repository (Zenodo), data from several high-contrast ground-based instruments worldwide in which we injected synthetic planetary signals. The data challenge is hosted on the CodaLab competition platform, where participants can upload their results. The specifications of the data challenge are published on our website https://exoplanet-imaging-challenge.github.io/. The first phase, launched on the 1st of September 2019 and closed on the 1st of October 2020, consisted in detecting point sources in two types of common data-set in the field of high-contrast imaging: data taken in pupil-tracking mode at one wavelength (subchallenge 1, also referred to as ADI) and multispectral data taken in pupil-tracking mode (subchallenge 2, also referred to as ADI+mSDI). In this paper, we describe the approach, organisational lessons-learnt and current limitations of the data challenge, as well as preliminary results of the participants' submissions for this first phase. In the future, we plan to provide permanent access to the standard library of data sets and metrics, in order to guide the validation and support the publications of innovative image processing algorithms dedicated to high-contrast imaging of planetary systems.}, language = {en} }