Search Results for author: Jan Sellner

Found 9 papers, 5 papers with code

Handling Geometric Domain Shifts in Semantic Segmentation of Surgical RGB and Hyperspectral Images

1 code implementation27 Aug 2024 Silvia Seidlitz, Jan Sellner, Alexander Studier-Fischer, Alessandro Motta, Berkin Özdemir, Beat P. Müller-Stich, Felix Nickel, Lena Maier-Hein

Our comprehensive validation on six different OOD datasets, comprising 600 RGB and hyperspectral imaging (HSI) cubes from 33 pigs, each annotated with 19 classes, reveals a large performance drop in SOA organ segmentation models on geometric OOD data.

Organ Segmentation Scene Segmentation +2

New spectral imaging biomarkers for sepsis and mortality in intensive care

1 code implementation19 Aug 2024 Silvia Seidlitz, Katharina Hölzl, Ayca von Garrel, Jan Sellner, Stephan Katzenschlager, Tobias Hölle, Dania Fischer, Maik von der Forst, Felix C. F. Schmitt, Markus A. Weigand, Lena Maier-Hein, Maximilian Dietrich

The predictive performance improves substantially when additional clinical data is incorporated, leading to an AUROC of up to 0. 94 (95 % CI [0. 92; 0. 96]) for sepsis and 0. 84 (95 % CI [0. 78; 0. 89]) for mortality.

Semantic segmentation of surgical hyperspectral images under geometric domain shifts

1 code implementation20 Mar 2023 Jan Sellner, Silvia Seidlitz, Alexander Studier-Fischer, Alessandro Motta, Berkin Özdemir, Beat Peter Müller-Stich, Felix Nickel, Lena Maier-Hein

According to a comprehensive validation on six different OOD data sets comprising 600 RGB and hyperspectral imaging (HSI) cubes from 33 pigs semantically annotated with 19 classes, we demonstrate a large performance drop of SOA organ segmentation networks applied to geometric OOD data.

Organ Segmentation Scene Segmentation +2

Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding

no code implementations7 Oct 2022 Constantin Seibold, Simon Reiß, Saquib Sarfraz, Matthias A. Fink, Victoria Mayer, Jan Sellner, Moon Sung Kim, Klaus H. Maier-Hein, Jens Kleesiek, Rainer Stiefelhagen

To exploit anatomical structures in this scenario, we present a sophisticated automatic pipeline to gather and integrate human bodily structures from computed tomography datasets, which we incorporate in our PAXRay: A Projected dataset for the segmentation of Anatomical structures in X-Ray data.

Anatomy Phrase Grounding

Robust deep learning-based semantic organ segmentation in hyperspectral images

1 code implementation9 Nov 2021 Silvia Seidlitz, Jan Sellner, Jan Odenthal, Berkin Özdemir, Alexander Studier-Fischer, Samuel Knödler, Leonardo Ayala, Tim J. Adler, Hannes G. Kenngott, Minu Tizabi, Martin Wagner, Felix Nickel, Beat P. Müller-Stich, Lena Maier-Hein

To address this gap in the literature, we are investigating the following research questions based on hyperspectral imaging (HSI) data of pigs acquired in an open surgery setting: (1) What is an adequate representation of HSI data for neural network-based fully automated organ segmentation, especially with respect to the spatial granularity of the data (pixels vs. superpixels vs. patches vs. full images)?

Deep Learning Image Segmentation +5

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