Zobrazeno 1 - 10
of 303
pro vyhledávání: '"Waldstein, Sebastian"'
Autor:
Asgari, Rhona, Waldstein, Sebastian, Schlanitz, Ferdinand, Baratsits, Magdalena, Schmidt-Erfurth, Ursula, Bogunović, Hrvoje
The presence of drusen is the main hallmark of early/intermediate age-related macular degeneration (AMD). Therefore, automated drusen segmentation is an important step in image-guided management of AMD. There are two common approaches to drusen segme
Externí odkaz:
http://arxiv.org/abs/1912.05404
Autor:
Rivail, Antoine, Schmidt-Erfurth, Ursula, Vogl, Wolf-Dieter, Waldstein, Sebastian M., Riedl, Sophie, Grechenig, Christoph, Wu, Zhichao, Bogunović, Hrvoje
Longitudinal imaging is capable of capturing the static ana\-to\-mi\-cal structures and the dynamic changes of the morphology resulting from aging or disease progression. Self-supervised learning allows to learn new representation from available larg
Externí odkaz:
http://arxiv.org/abs/1910.09420
Autor:
Asgari, Rhona, Orlando, José Ignacio, Waldstein, Sebastian, Schlanitz, Ferdinand, Baratsits, Magdalena, Schmidt-Erfurth, Ursula, Bogunović, Hrvoje
Automated drusen segmentation in retinal optical coherence tomography (OCT) scans is relevant for understanding age-related macular degeneration (AMD) risk and progression. This task is usually performed by segmenting the top/bottom anatomical interf
Externí odkaz:
http://arxiv.org/abs/1906.07679
Autor:
Seeböck, Philipp, Orlando, José Ignacio, Schlegl, Thomas, Waldstein, Sebastian M., Bogunović, Hrvoje, Klimscha, Sophie, Langs, Georg, Schmidt-Erfurth, Ursula
Diagnosis and treatment guidance are aided by detecting relevant biomarkers in medical images. Although supervised deep learning can perform accurate segmentation of pathological areas, it is limited by requiring a-priori definitions of these regions
Externí odkaz:
http://arxiv.org/abs/1905.12806
Autor:
Seeböck, Philipp, Romo-Bucheli, David, Waldstein, Sebastian, Bogunović, Hrvoje, Orlando, José Ignacio, Gerendas, Bianca S., Langs, Georg, Schmidt-Erfurth, Ursula
Optical coherence tomography (OCT) has become the most important imaging modality in ophthalmology. A substantial amount of research has recently been devoted to the development of machine learning (ML) models for the identification and quantificatio
Externí odkaz:
http://arxiv.org/abs/1901.08379
Autor:
Orlando, José Ignacio, Seeböck, Philipp, Bogunović, Hrvoje, Klimscha, Sophie, Grechenig, Christoph, Waldstein, Sebastian, Gerendas, Bianca S., Schmidt-Erfurth, Ursula
Publikováno v:
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
In this paper, we introduce a Bayesian deep learning based model for segmenting the photoreceptor layer in pathological OCT scans. Our architecture provides accurate segmentations of the photoreceptor layer and produces pixel-wise epistemic uncertain
Externí odkaz:
http://arxiv.org/abs/1901.07929
Autor:
Seeböck, Philipp, Waldstein, Sebastian M., Klimscha, Sophie, Bogunovic, Hrvoje, Schlegl, Thomas, Gerendas, Bianca S., Donner, René, Schmidt-Erfurth, Ursula, Langs, Georg
The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection and exploitation of findings that are known a priori after annotation o
Externí odkaz:
http://arxiv.org/abs/1810.13404
Autor:
Schlegl, Thomas, Bogunovic, Hrvoje, Klimscha, Sophie, Seeböck, Philipp, Sadeghipour, Amir, Gerendas, Bianca, Waldstein, Sebastian M., Langs, Georg, Schmidt-Erfurth, Ursula
The automatic detection of disease related entities in retinal imaging data is relevant for disease- and treatment monitoring. It enables the quantitative assessment of large amounts of data and the corresponding study of disease characteristics. The
Externí odkaz:
http://arxiv.org/abs/1805.03278
Autor:
Schlegl, Thomas, Seeböck, Philipp, Waldstein, Sebastian M., Schmidt-Erfurth, Ursula, Langs, Georg
Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection. High
Externí odkaz:
http://arxiv.org/abs/1703.05921
Autor:
Rashid, Adnan, Waldstein, Sebastian M., Gerendas, Bianca S., Bogunovic, Hrvoje, Wahle, Andreas, Lee, Kyungmoo, Wang, Kai, Simader, Christian, Abramoff, Michael D., Schmidt-Erfurth, Ursula, Sonka, Milan
PURPOSE: Establishing and obtaining consistent quantitative indices of retinal thickness from a variety of clinically used Spectral-Domain Optical Coherence Tomography scanners. DESIGN: Retinal images from five Spectral-Domain Optical Coherence Tomog
Externí odkaz:
http://arxiv.org/abs/1612.06442