Search Results for author: Sandy Engelhardt

Found 16 papers, 8 papers with code

Posterior temperature optimized Bayesian models for inverse problems in medical imaging

1 code implementation2 Feb 2022 Max-Heinrich Laves, Malte Tölle, Alexander Schlaefer, Sandy Engelhardt

In POTOBIM, we optimize both the parameters of the prior distribution and the posterior temperature with respect to reconstruction accuracy using Bayesian optimization with Gaussian process regression.

Image Denoising Variational Inference

Comparison of Evaluation Metrics for Landmark Detection in CMR Images

1 code implementation25 Jan 2022 Sven Koehler, Lalith Sharan, Julian Kuhm, Arman Ghanaat, Jelizaveta Gordejeva, Nike K. Simon, Niko M. Grell, Florian André, Sandy Engelhardt

In this work, we extended the public ACDC dataset with additional labels of the right ventricular insertion points and compare different variants of a heatmap-based landmark detection pipeline.

Point detection through multi-instance deep heatmap regression for sutures in endoscopy

1 code implementation16 Nov 2021 Lalith Sharan, Gabriele Romano, Julian Brand, Halvar Kelm, Matthias Karck, Raffaele De Simone, Sandy Engelhardt

Method: In this work, we formulate the suture detection task as a multi-instance deep heatmap regression problem, to identify entry and exit points of sutures.

Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation

1 code implementation14 Jul 2021 Lalith Sharan, Gabriele Romano, Sven Koehler, Halvar Kelm, Matthias Karck, Raffaele De Simone, Sandy Engelhardt

In this use case, it is of paramount importance to display objects like needles, sutures or instruments consistent in both domains while altering the style to a more tissue-like appearance.

Translation Unsupervised Image-To-Image Translation

How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalise to rare congenital heart diseases for surgical planning?

1 code implementation10 Feb 2020 Sven Koehler, Animesh Tandon, Tarique Hussain, Heiner Latus, Thomas Pickardt, Samir Sarikouch, Philipp Beerbaum, Gerald Greil, Sandy Engelhardt, Ivo Wolf

Our results confirm that current deep learning models can achieve excellent results (left ventricle dice of $0. 951\pm{0. 003}$/$0. 941\pm{0. 007}$ train/validation) within a single data collection.

Towards Augmented Reality-based Suturing in Monocular Laparoscopic Training

no code implementations19 Jan 2020 Chandrakanth Jayachandran Preetha, Jonathan Kloss, Fabian Siegfried Wehrtmann, Lalith Sharan, Carolyn Fan, Beat Peter Müller-Stich, Felix Nickel, Sandy Engelhardt

Reliable 3D localization of needle and instruments in real time could be used to augment the scene with additional parameters that describe their quantitative geometric relation, e. g. the relation between the estimated needle plane and its rotation center and the instrument.

Depth Estimation

Cross-Domain Conditional Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training

no code implementations24 Jun 2019 Sandy Engelhardt, Lalith Sharan, Matthias Karck, Raffaele De Simone, Ivo Wolf

Such concepts are able to generate realistic representations of phantoms learned from real intraoperative endoscopic sequences.

Image Generation

Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries

no code implementations10 Jun 2018 Sandy Engelhardt, Raffaele De Simone, Peter M. Full, Matthias Karck, Ivo Wolf

Though, application of this approach to continuous video frames can result in flickering, which turned out to be especially prominent for this application.

Computers and Society

Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features

1 code implementation3 Jul 2017 Fabian Isensee, Paul Jaeger, Peter M. Full, Ivo Wolf, Sandy Engelhardt, Klaus H. Maier-Hein

We evaluated our method on the ACDC dataset (4 pathology groups, 1 healthy group) and achieve dice scores of 0. 945 (LVC), 0. 908 (RVC) and 0. 905 (LVM) in a cross-validation over the training set (100 cases) and 0. 950 (LVC), 0. 923 (RVC) and 0. 911 (LVM) on the test set (50 cases).

General Classification Time Series

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