Search Results for author: Janek Gröhl

Found 12 papers, 2 papers with code

Distribution-informed and wavelength-flexible data-driven photoacoustic oximetry

1 code implementation21 Mar 2024 Janek Gröhl, Kylie Yeung, Kevin Gu, Thomas R. Else, Monika Golinska, Ellie V. Bunce, Lina Hacker, Sarah E. Bohndiek

We used a long short-term memory network to implement a wavelength-flexible network architecture and proposed the Jensen-Shannon divergence to predict the most suitable training dataset.

Moving beyond simulation: data-driven quantitative photoacoustic imaging using tissue-mimicking phantoms

1 code implementation11 Jun 2023 Janek Gröhl, Thomas R. Else, Lina Hacker, Ellie V. Bunce, Paul W. Sweeney, Sarah E. Bohndiek

We show that training on simulated data results in artefacts and biases in the estimates, reinforcing the existence of a domain gap between simulation and experiment.

Semantic segmentation of multispectral photoacoustic images using deep learning

no code implementations20 May 2021 Melanie Schellenberg, Kris Dreher, Niklas Holzwarth, Fabian Isensee, Annika Reinke, Nicholas Schreck, Alexander Seitel, Minu D. Tizabi, Lena Maier-Hein, Janek Gröhl

Due to the intuitive representation of high-dimensional information, such a preprocessing algorithm could be a valuable means to facilitate the clinical translation of photoacoustic imaging.

Segmentation Semantic Segmentation +1

Tattoo tomography: Freehand 3D photoacoustic image reconstruction with an optical pattern

no code implementations10 Nov 2020 Niklas Holzwarth, Melanie Schellenberg, Janek Gröhl, Kris Dreher, Jan-Hinrich Nölke, Alexander Seitel, Minu D. Tizabi, Beat P. Müller-Stich, Lena Maier-Hein

Methods: In this paper, we present a novel approach to 3D reconstruction of PAT data (Tattoo tomography) that does not require an external tracking system and can smoothly be integrated into clinical workflows.

3D Reconstruction Image Reconstruction

Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging

no code implementations10 Nov 2020 Jan-Hinrich Nölke, Tim Adler, Janek Gröhl, Thomas Kirchner, Lynton Ardizzone, Carsten Rother, Ullrich Köthe, Lena Maier-Hein

Multispectral photoacoustic imaging (PAI) is an emerging imaging modality which enables the recovery of functional tissue parameters such as blood oxygenation.

Uncertainty Quantification

Deep learning for biomedical photoacoustic imaging: A review

no code implementations5 Nov 2020 Janek Gröhl, Melanie Schellenberg, Kris Dreher, Lena Maier-Hein

Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables spatially resolved imaging of optical tissue properties up to several centimeters deep in tissue, creating the potential for numerous exciting clinical applications.

Image Reconstruction Translation

Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks

no code implementations8 Mar 2019 Tim J. Adler, Lynton Ardizzone, Anant Vemuri, Leonardo Ayala, Janek Gröhl, Thomas Kirchner, Sebastian Wirkert, Jakob Kruse, Carsten Rother, Ullrich Köthe, Lena Maier-Hein

Assessment of the specific hardware used in conjunction with such algorithms, however, has not properly addressed the possibility that the problem may be ill-posed.

Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI)

no code implementations15 Feb 2019 Janek Gröhl, Thomas Kirchner, Tim Adler, Lena Maier-Hein

In this work, we tackle the challenge by employing learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI) to obtain quantitative estimates for blood oxygenation.

Context encoding enables machine learning-based quantitative photoacoustics

no code implementations12 Jun 2017 Thomas Kirchner, Janek Gröhl, Lena Maier-Hein

Real-time monitoring of functional tissue parameters, such as local blood oxygenation, based on optical imaging could provide groundbreaking advances in the diagnosis and interventional therapy of various diseases.

BIG-bench Machine Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.