Search Results for author: Steffen Kappler

Found 4 papers, 0 papers with code

Metal-conscious Embedding for CBCT Projection Inpainting

no code implementations29 Nov 2022 Fuxin Fan, Yangkong Wang, Ludwig Ritschl, Ramyar Biniazan, Marcel Beister, Björn Kreher, Yixing Huang, Steffen Kappler, Andreas Maier

The existence of metallic implants in projection images for cone-beam computed tomography (CBCT) introduces undesired artifacts which degrade the quality of reconstructed images.

Metal Artifact Reduction

Simulation-Driven Training of Vision Transformers Enabling Metal Segmentation in X-Ray Images

no code implementations17 Mar 2022 Fuxin Fan, Ludwig Ritschl, Marcel Beister, Ramyar Biniazan, Björn Kreher, Tristan M. Gottschalk, Steffen Kappler, Andreas Maier

Since the generation of high quality clinical training is a constant challenge, this study proposes to generate simulated X-ray images based on CT data sets combined with self-designed computer aided design (CAD) implants and make use of convolutional neural network (CNN) and vision transformer (ViT) for metal segmentation.

Segmentation

Automated detection and quantification of COVID-19 airspace disease on chest radiographs: A novel approach achieving radiologist-level performance using a CNN trained on digital reconstructed radiographs (DRRs) from CT-based ground-truth

no code implementations13 Aug 2020 Eduardo Mortani Barbosa Jr., Warren B. Gefter, Rochelle Yang, Florin C. Ghesu, Si-Qi Liu, Boris Mailhe, Awais Mansoor, Sasa Grbic, Sebastian Piat, Guillaume Chabin, Vishwanath R S., Abishek Balachandran, Sebastian Vogt, Valentin Ziebandt, Steffen Kappler, Dorin Comaniciu

Purpose: To leverage volumetric quantification of airspace disease (AD) derived from a superior modality (CT) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to: 1) train a convolutional neural network to quantify airspace disease on paired CXRs; and 2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19.

Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation

no code implementations11 Dec 2019 Dominik Eckert, Sulaiman Vesal, Ludwig Ritschl, Steffen Kappler, Andreas Maier

In this study, we propose a deep learning method based on Convolutional Neural Networks (CNNs) for mammogram denoising to improve the image quality.

Data Augmentation Denoising

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