3-D Reconstruction of Historical Documents using an X-Ray C-Arm CT System - Stromer, Daniel; Christlein, Vincent; Anton, Gisela; Kugler, Patrick; Maier, Andreas (Proceedings of the 31th conference on Image and Vision Computing New Zealand 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.3dreco_2
Keywords: X-Ray imaging; Iron gall ink reconstruction; Historical Document Analysis

3-D Reconstruction of Iron Gall Ink Writings - Stromer, Daniel; Schön, Tobias; Holub, Wolfgang; Maier, Andreas (Proceedings of 7th Conference on Industrial Computed Tomography (iCT 2017), 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.3dreco_4

Abstract: Automatic Dementia Screening and Scoring by Applying Deep Learning on Clock-drawing Tests - Chen, Shuqing; Stromer, Daniel; Alnasser Alabdalrahim, Harb; Schwab, Stefan; Weih, Markus; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-09

Abstract: Maximum A-posteriori Signal Recovery for OCT Angiography Image Generation - Husvogt, Lennart; Ploner, Stefan; Chen, Siyu; Stromer, Daniel; Schottenhamml, Julia; Alibhai, Yasin; Moult, Eric; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-09

Approximation der Projektionsmatrizen einer C-Bogen 3D-Fahrt anhand der Odometriedaten - Stromer, Daniel; Maier, Andreas (Bildverarbeitung für die Medizin 2014, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.approx
Keywords: Calibration; C-arm

Approximation of the projection matrices of a C-arc 3D-drive based on the odometry data Approximation der projektionsmatrizen einer C-Bogen 3D-Fahrt anhand der odometriedaten - Stromer, Daniel; Maier, Andreas (Informatik aktuell, 2014)
CRIS-Team Scopus Importer:2022-08-05

Automatic Classification of Neuromuscular Diseases in Children Using Photoacoustic Imaging - Schlereth, Maja; Stromer, Daniel; Breininger, Katharina; Wagner, Alexandra; Tan, Lina; Maier, Andreas; Knieling, Ferdinand (Informatik aktuell, 2022)
Neuromuscular diseases (NMDs) cause a significant burden for both healthcare systems and society. They can lead to severe progressive muscle weakness, muscle degeneration, contracture, deformity and progressive disability. The NMDs evaluated in this study often manifest in early childhood. As subtypes of disease, e.g. Duchenne muscular dystropy (DMD) and spinal muscular atrophy (SMA), are difficult to differentiate at the beginning and worsen quickly, fast and reliable differential diagnosis is crucial. Photoacoustic and ultrasound imaging has shown great potential to visualize and quantify the extent of different diseases. The addition of automatic classification of such image data could further improve standard diagnostic procedures.We compare deep learning-based 2-class and 3-class classifiers based on VGG16 for differentiating healthy from diseased muscular tissue. This work shows promising results with high accuracies above 0.86 for the 3-class problem and can be used as a proof of concept for future approaches for earlier diagnosis and therapeutic monitoring of NMDs.<br
CRIS-Team Scopus Importer:2022-05-06

Automatic dementia screening and scoring by applying deep learning on clock-drawing tests - Chen, Shuqing; Stromer, Daniel; Alnasser Alabdalrahim, Harb; Schwab, Stefan; Weih, Markus; Maier, Andreas (Scientific Reports, 2020)
Dementia is one of the most common neurological syndromes in the world. Usually, diagnoses are made based on paper-and-pencil tests and scored depending on personal judgments of experts. This technique can introduce errors and has high inter-rater variability. To overcome these issues, we present an automatic assessment of the widely used paper-based clock-drawing test by means of deep neural networks. Our study includes a comparison of three modern architectures: VGG16, ResNet-152, and DenseNet-121. The dataset consisted of 1315 individuals. To deal with the limited amount of data, which also included several dementia types, we used optimization strategies for training the neural network. The outcome of our work is a standardized and digital estimation of the dementia screening result and severity level for an individual. We achieved accuracies of 96.65% for screening and up to 98.54% for scoring, overcoming the reported state-of-the-art as well as human accuracies. Due to the digital format, the paper-based test can be simply scanned by using a mobile device and then be evaluated also in areas where there is a staff shortage or where no clinical experts are available.<br

Bausteine auf dem Weg zu einer virtuellen Zeitmanschiene – ein Editionsprojekt über lange Dauer - Maier, Andreas; Stromer, Daniel; Christlein, Vincent; Bell, Peter (Magazin für digitale Editionswissenschaften, 2019)
Keywords: European Time Machine; Digital Humanities

Browsing Through Closed Books: Evaluation of Pre-processing Methods for Page Extraction of a 3-D CT Book Volume - Stromer, Daniel; Christlein, Vincent; Schön, Tobias; Holub, Wolfgang; Maier, Andreas (Proceedings of the 2nd International Conference on Advanced Materials Research and Manufacturing Technologies, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.browsi_0

Browsing Through Closed Books: Fully Automatic Book Page Extraction from a 3-D X-ray CT Volume - Stromer, Daniel; Christlein, Vincent; Schön, Tobias; Holub, Wolfgang; Maier, Andreas (The 14th IAPR International Conference on Document Analysis and Recognition, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.browsi_66

Browsing through sealed historical manuscripts by using 3-D computed tomography with low-brilliance X-ray sources - Stromer, Daniel; Christlein, Vincent; Martindale, Christine; Zippert, Patrick; Haltenberger, Eric; Hausotte, Tino; Maier, Andreas (Scientific Reports, 2018)
Severely damaged historical documents are extremely fragile. In many cases, their secrets remain concealed beneath their cover. Recently, non-invasive digitization approaches based on 3-D scanning have demonstrated the ability to recover single pages or letters without the need to open the manuscripts. This can even be achieved using conventional micro-CTs without the need for synchrotron hardware. However, not all manuscripts may be suited for such techniques due to their material and X-ray properties. In order to recommend which manuscripts and which inks are best suited for such a process, we investigate six inks that were commonly used in ancient times: malachite, three types of iron gall, Tyrian purple, and buckthorn. Image contrast is explored over the complete pipeline, from the X-ray CT scan and page extraction to the virtual flattening of the page image. We demonstrate, that all inks containing metallic particles are visible in the output, a decrease of the X-ray energy enhances the readability, and that the visibility highly depends on the X-ray attenuation of the ink’s metallic ingredients and their concentration. Based on these observations, we give recommendations on how to select the appropriate imaging parameters.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.browsi

Comparison of SART and eTV Reconstruction for increased C-arm CT Volume Coverage by proper Detector Rotation in Liver Imaging - Stromer, Daniel; Amrehn, Mario; Huang, Yixing; Kugler, Patrick; Bauer, Sebastian; Lauritsch, Günter; Maier, Andreas (Proceedings of the 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2016)
In this work, we present a method to increase the lateral field-of-view of a C-arm CT system by rotating the detector such that the diagonal of the detector lies on the u-axis of the detector's coordinate system. We investigated three different 3-D scan trajectories for liver imaging of an obese patient (waist circumference 130 cm) — a Short Scan, a Large Volume Scan and a Helical Large Volume Scan. We reconstructed a data set of the Visible Human Project with the SART and the eTV algorithm. Tests revealed that the coverage was increased with the presented method by 25.3 % for the Short Scan and 28.5 % for the Large Volume Scan. Performing helical scans compensated the axial data loss. The two implemented iterative approaches both provide acceptable results, with the eTV algorithm reducing the RMSE compared to SART by about 29 %. Given a liver imaging task, the rotated detector is able to image the entire liver section of the abdomen with a single Large Volume Sca
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.compar_1
Keywords: Diamond Scan; eTV; Field-of-View Enlargement; Iterative Reconstruction; iTV; SART

Compressed sensing for optical coherence tomography angiography volume generation - Husvogt, Lennart; Ploner, Stefan; Stromer, Daniel; Schottenhamml, Julia; Moult, Eric; Fujimoto, James G.; Maier, Andreas (Informatik aktuell, 2020)
Optical coherence tomography angiography (OCTA) is an increasingly popular modality for imaging of the retinal vasculature. Repeated optical coherence tomography (OCT) scans of the retina allow the computation of motion contrast to display the retinal vasculature. To the best of our knowledge, we present the first application of compressed sensing for the generation of OCTA volumes. Using a probabilistic signal model for the computation of OCTA volumes and a 3D median filter, it is possible to perform compressed sensing reconstruction of OCTA volumes while suppressing noise. The presented approach was tested on a ground truth, averaged from ten individual OCTA volumes. Average reductions of the mean squared error of 9:67% were achieved when comparing reconstructed OCTA images to the stand-alone application of a 3D median filter.
CRIS-Team Scopus Importer:2020-04-21

Correction propagation for user-assisted optical coherence tomography segmentation: general framework and application to Bruch’s membrane segmentation - Stromer, Daniel; Moult, Eric M.; Chen, Siyu; Waheed, Nadja K.; Maier, Andreas; Fujimoto, James G. (Biomedical Optics Express, 2020)
Optical coherence tomography (OCT) is a commonly used ophthalmic imaging modality. While OCT has traditionally been viewed cross-sectionally (i.e., as a sequence of B-scans), higher A-scan rates have increased interest in en face OCT visualization and analysis. The recent clinical introduction of OCT angiography (OCTA) has further spurred this interest, with chorioretinal OCTA being predominantly displayed via en face projections. Although en face visualization and quantitation are natural for many retinal features (e.g., drusen and vasculature), it requires segmentation. Because manual segmentation of volumetric OCT data is prohibitively laborious in many settings, there has been significant research and commercial interest in developing automatic segmentation algorithms. While these algorithms have achieved impressive results, the variability of image qualities and the variety of ocular pathologies cause even the most robust automatic segmentation algorithms to err. In this study, we develop a user-assisted segmentation approach, complementary to fully-automatic methods, wherein correction propagation is used to reduce the burden of manually correcting automatic segmentations. The approach is evaluated for Bruch’s membrane segmentation in eyes with advanced age-related macular degeneration.<br
Keywords: Angiography; Image processing; Ophthalmic imaging; Optical coherence tomography; Retinal nerve fiber layer; Segmentation

Data Completeness Estimation for 3D C-Arm Scans with Rotated Detector to Enlarge the Lateral Field-of-View - Stromer, Daniel; Kugler, Patrick; Bauer, Sebastian; Lauritsch, Günter; Maier, Andreas (Bildverarbeitung für die Medizin 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.dataco_2
Keywords: Field-of-View Enlargement; Detector Rotation

Deep learning for terahertz image denoising in nondestructive historical document analysis - Dutta, Balaka; Root, Konstantin; Ullmann, Ingrid; Wagner, Fabian; Mayr, Martin; Seuret, Mathias; Thies, Mareike; Stromer, Daniel; Christlein, Vincent; Schür, Jan; Maier, Andreas; Huang, Yixing (Scientific Reports, 2022)
Historical documents contain essential information about the past, including places, people, or events. Many of these valuable cultural artifacts cannot be further examined due to aging or external influences, as they are too fragile to be opened or turned over, so their rich contents remain hidden. Terahertz (THz) imaging is a nondestructive 3D imaging technique that can be used to reveal the hidden contents without damaging the documents. As noise or imaging artifacts are predominantly present in reconstructed images processed by standard THz reconstruction algorithms, this work intends to improve THz image quality with deep learning. To overcome the data scarcity problem in training a supervised deep learning model, an unsupervised deep learning network (CycleGAN) is first applied to generate paired noisy THz images from clean images (clean images are generated by a handwriting generator). With such synthetic noisy-to-clean paired images, a supervised deep learning model using Pix2pixGAN is trained, which is effective to enhance real noisy THz images. After Pix2pixGAN denoising, 99% characters written on one-side of the Xuan paper can be clearly recognized, while 61% characters written on one-side of the standard paper are sufficiently recognized. The average perceptual indices of Pix2pixGAN processed images are 16,83, which is very close to the average perceptual index 16.19 of clean handwriting images. Our work has important value for THz-imaging-based nondestructive historical document analysis.<br
Keywords: Deep learning; Terahertz imaging; Historical document analysis; Nondestructive testing

Dose Reduction for Historical Books Digitization by 3-D X-Ray CT - Stromer, Daniel; Christlein, Vincent; Huang, Yixing; Zippert, Patrick; Helmecke, Eric; Hausotte, Tino; Maier, Andreas (Proceedings of 8th Conference on Industrial Computed Tomography (iCT 2018), 2018)
UnivIS-Import:2018-02-22:Pub.2018.tech.IMMD.IMMD5.dosere_1

Enhanced Crack Segmentation (eCS): A Reference Algorithm for Segmenting Cracks in Multicrystalline Silicon Solar Cells - Stromer, Daniel; Vetter, Andreas; Özkan, Hasan Can; Probst, Christian; Maier, Andreas (IEEE Journal of Photovoltaics, 2019)
The annually produced quantity of solar modules has steadily increased over the past decades. Rising production speeds and the associated high throughput of wafers, cells, and modules will make an automatized quality inspection mandatory. In the case of visual optical inspection, automatized quality control by using machine vision is already possible. To localize cracks in solar cells, luminescence imaging is used, where several approaches for an automatized inspection exist, but a standard solution for an automatized inspection algorithm is not yet available. This is, in particular, true for multicrystalline solar cells, where the grainy structures in the luminescence images are hard to distinguish from small cracks. Another obstacle in automatic crack analysis is that reference segmentation algorithms are generally not publicly available. Accordingly, a new algorithm can hardly be compared by ranking it to an existing standard. In this paper, we adapted the vesselness algorithm for automatic processing of electroluminescence images of multicrystalline silicon solar cells. Segmentation of cracks in multicrystalline solar cells with the proposed enhanced crack segmentation algorithm shows very promising results on the used database compared with three different commonly used approaches. Furthermore, the segmentation code is made publicly available, and we propose that this algorithm may serve as a reference algorithm, sparking further progress in automatized crack segmentation for multicrystalline silicon solar cells.
CRIS-Team Scopus Importer:2019-05-07
Keywords: Crack detection; crack segmentation; electroluminescence (EL) imaging; multicrystalline solar cell imaging; photovoltaic (PV)

Fast, Robust and Efficient Extraction of Book Pages from a 3-D X-ray CT Volume - Stromer, Daniel; Christlein, Vincent; Schön, Tobias; Holub, Wolfgang; Maier, Andreas (Proceedings of the 14th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.fastro

Image Quality Analysis of Limited Angle Tomography Using the Shift-Variant Data Loss Model - Huang, Yixing; Lauritsch, Günter; Amrehn, Mario; Taubmann, Oliver; Haase, Viktor; Stromer, Daniel; Huang, Xiaolin; Maier, Andreas (Bildverarbeitung für die Medizin 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.imageq_5

Initial Investigations Towards Non-invasive Monitoring of Chronic Wound Healing Using Deep Learning and Ultrasound Imaging - Schlereth, Maja; Stromer, Daniel; Mantri, Yash; Tsujimoto, Jason; Breininger, Katharina; Maier, Andreas; Anderson, Caesar; Garimella, Pranav S.; Jokerst, Jesse V. (Informatik aktuell, 2022)
Chronic wounds including diabetic and arterial/venous insufficiency injuries have become a major burden for healthcare systems worldwide. Demographic changes suggest that wound care will play an even bigger role in the coming decades. Predicting and monitoring response to therapy in wound care is currently largely based on visual inspection with little information on the underlying tissue. Thus, there is an urgent unmet need for innovative approaches that facilitate personalized diagnostics and treatments at the point-of-care. It has been recently shown that ultrasound imaging can monitor response to therapy in wound care, but this work required onerous manual image annotations. In this study we present initial results of a deep learning-based automatic segmentation of cross-sectional wound size in ultrasound images and identify requirements and challenges for future research on this application. Evaluation of the segmentation results underscores the potential of the proposed deep learning approach to complement non-invasive imaging with Dice scores of 0.34 (U-Net, FCN) and 0.27 (ResNet-U-Net) but also highlights the need for improving robustness further.We conclude that deep learning-supported analysis of non-invasive ultrasound images is a promising area of research to automatically extract cross-sectional wound size and depth information with potential value in monitoring response to therapy.<br
CRIS-Team Scopus Importer:2022-05-06

Maximum a posteriori signal recovery for optical coherence tomography angiography image generation and denoising - Husvogt, Lennart; Ploner, Stefan; Chen, Siyu; Stromer, Daniel; Schottenhamml, Julia; Alibhai, A. Yasin; Moult, Eric; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (Biomedical Optics Express, 2021)
Optical coherence tomography angiography (OCTA) is a novel and clinically promising imaging modality to image retinal and sub-retinal vasculature. Based on repeated optical coherence tomography (OCT) scans, intensity changes are observed over time and used to compute OCTA image data. OCTA data are prone to noise and artifacts caused by variations in flow speed and patient movement. We propose a novel iterative maximum a posteriori signal recovery algorithm in order to generate OCTA volumes with reduced noise and increased image quality. This algorithm is based on previous work on probabilistic OCTA signal models and maximum likelihood estimates. Reconstruction results using total variation minimization and wavelet shrinkage for regularization were compared against an OCTA ground truth volume, merged from six co-registered single OCTA volumes. The results show a significant improvement in peak signal-to-noise ratio and structural similarity. The presented algorithm brings together OCTA image generation and Bayesian statistics and can be developed into new OCTA image generation and denoising algorithm
Keywords: OCT; OCTA; optical coherence tomography; optical coherence tomography angiography; baysian; reconstruction

Non-Destructive Digitization of Soiled Historical Chinese Bamboo Scrolls - Stromer, Daniel; Christlein, Vincent; Huang, Xiaolin; Zippert, Patrick; Helmecke, Eric; Hausotte, Tino; Maier, Andreas (2018 13th IAPR Workshop on Document Analysis Systems (DAS), 2018)
For about 2000 years, no paper was used as a media in China but writings and drawings were captured on bamboo and wooden slips. Several slips were bound together with strips and rolled up to a scroll. The writings and drawings were either brushed or even carved into the wood. Those documents are very precious for culture inheritance and research, but due to aging processes, the discovered pieces are sometimes in a poor condition and also soiled. Because cleaning the slips is not only challenging but also writings could be erased, we developed a method to digitize such historical documents without the need of cleaning. We perform a 3-D X-ray micro-CT scan resulting in a 3-D volume of the complete document. With our approach, we were able to investigate the scroll without any manual labor (e.g. unwrapping or cleaning). We showed that the method also works for heavily soiled scrolls where nothing is readable with the naked eye. This can help conservators to store all writings before they may be erased by the cleaning process. Finally, we present a manual technique to virtually unwrap and post-process the documents resulting in a 2-D image of all bamboo slips.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.nondes
Keywords: Bamboo and wooden slips; 3-D X-ray CT; Historical Chinese Documents; Document Digitization

Non-invasive Imaging Methods for Digital Humanities, Medicine, and Quality Assessment - Stromer, Daniel (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019)
Keywords: Book CT; Digital humanities; Non-invasive imaging; Optical coherence tomography; Quality assessment; Scroll CT; Segmentation

Segmentation of Fat and Fascias in Canine Ultrasound Images - Rybakov, Oleksiy; Stromer, Daniel; Mischewski, Irina; Maier, Andreas (Bildverarbeitung für die Medizin 2018 (BVM 2018), 2018)
The connective tissue between fat and muscle termed fascia has been of interest to the recent clinical and biological research. However, in the canine and human medicine, the anatomic knowledge is still limited. To analyze the superficial fascia in canine medicine, a database with around 200 ultrasound images of one dog has been created. The superficial fascia contains fat compartments and is closely connected to the surrounding structures such as the skin's dermis and the epimysium of the muscles. This work proposes a semi-automatic and fully-automatic segmentation algorithm separating the different layers of ultrasound images of canines. Both algorithms were evaluated on a set of 24 expert-labeled images achieving high accuracy scores up to 95.9
Keywords: fascia; segmentation; ultrasound; canine; fat; open-source; GUI

Virtual cleaning and unwrapping of non-invasively digitized soiled bamboo scrolls - Stromer, Daniel; Christlein, Vincent; Huang, Xiaolin; Zippert, Patrick; Hausotte, Tino; Maier, Andreas (Scientific Reports, 2019)
In ancient China, symbols and drawings captured on bamboo and wooden slips were used as main communication media. Those documents are very precious for cultural heritage and research, but due to aging processes, the discovered pieces are sometimes in a poor condition and contaminated by soil. Manual cleaning of excavated slips is a demanding and time-consuming task in which writings can be accidentally deleted. To counter this, we propose a novel approach based on conventional 3-D X-ray computed tomography to digitize such historical documents without before manual cleaning. By applying a virtual cleaning and unwrapping algorithm, the entire scroll surface is remapped into 2-D such that the hidden content becomes readable. We show that the technique also works for heavily soiled scrolls, enabling an investigation of the content by the naked eye without the need for manual labor. This digitization also allows for recovery of potentially erased writings and reconstruction of the original spatial information.
CRIS-Team WoS Importer:2019-03-06