Historical documents contain essential information about the past, including places, people, or events.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 Our work has important value for THz-imaging-based nondestructive historical document analysis.

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.

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.

Optical coherence tomography angiography (OCTA) is a clinically promising modality to image retinal vasculature. For this end, optical coherence tomography (OCT) volumes are repeatedly scanned and intensity changes over time are used to compute OCTA images. Because of patient movement and variations in blood ow, OCTA data are prone to noise.

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.

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.

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 algorithms.

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.

Inhalt: Andreas Maier, Daniel Stromer, Vincent Christlein, Peter Bell: Bausteine auf dem Weg zu einer virtuellen Zeitmanschiene – ein Editionsprojekt uber lange Dauer Klaus Meyer-Wegener: Semantic Web – Eine kurze Einfuhrung Andreas Kuczera: Mit graphbasierter Edition zur semantischen Multidimensionalitat Jorg Wettlaufer: Nachhaltigkeit und Langzeitverfugbarkeit von digitalen Editionen im Semantic Web

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.

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.

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 canine. Both algorithms were evaluated on a set of 24 expert-labeled images achieving high accuracy scores up to 95.9%.

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.

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.

It is often the case that a document can not be opened, page-turned or touched anymore due to damages caused by aging processes, moisture or fire. To counter this, special imaging systems can be used. One of our earlier work revealed that a common 3-D X-ray micro-CT scanner is well suited for imaging and reconstructing historical documents written with iron gall ink - an ink consisting of metallic particles. We acquired a volume of a self-made book without opening or page-turning with a single 3-D scan. However, when investigating the reconstructed volume, we faced the problem of a proper automatic extraction of single pages within the volume in an acceptable time without losing information of the writings. Within this work, we evaluate different appropriate pre-processing methods with respect to computation time and accuracy which are decisive for a proper extraction of book pages from the reconstructed X-ray volume and the subsequent ink identification. The different methods were tested for an extreme case with low resolution, noisy input data and wavy pages. Finally, we present results of the page extraction after applying the evaluated methods.

When digitizing or investigating historical documents, it is often the case that a document can not be opened, page-turned or touched anymore. Damages such as moisture or fire and aging processes disallow browsing through a book. To address these particular cases, our earlier work showed that Micro-CT X-ray scanners are able to image documents written with iron gall ink. A self-made book consisting of ten hand written pages was scanned and investigated without opening or page-turning. However, when analyzing the reconstruction results, we faced the problem of a proper automatic page segmentation and 2-D mapping within the volume in an acceptable time without losing information of the writings. The main problem is that the pages can be arbitrary deformed or squeezed together. In this paper, we present a fully automatic algorithm for the segmentation and extraction of book pages from the original 3-D volume. Our method delivers high quality results for our book model and can be easily adapted to other imaging modalities. We show that it performs well even for an extreme case with low resolution input data and wavy pages. To keep it simple for users, our algorithm works without any need of prior information or user interactions.

Corruptions such as aging-processes or moisture make it often impossible to digitize historical books or scrolls with common digitization approaches. 3-D X-ray CT is a non-destructive method which can provide a look inside those documents. Current CT scans use a full-circle trajectory with a large number of projections and high exposure times. However, there are historical goods that may suffer from high radiation dose. In this work, we present an evaluation of a 3-D X-ray CT scan with three reduced projection sizes reconstructed with four common algorithms compared to the mentioned high dose approach. For our experiments, we used a book with 22 pages and a leather cover. Every page has writings made with iron gall ink. We show that we can reduce the number of projections by at least 85 and up to 92.5 percent without severe loss of information on the book’s writings. The reconstructed volumes are compared with regard to common similarity measures as well as visual outputs of a selected 2-D mapped page.

Investigating historical documents makes it necessary to use special imaging systems. We already showed in an earlier work that it is possible to use common 3-D X-ray CT scanners for the reconstruction of historical documents written with iron gall ink. Our tests were based on a self-made book which was scanned and investigated without opening or pageturning. However, when analyzing the reconstruction results, we faced the problem of a proper automatic extraction of single pages within the volume in an acceptable time without losing information of the writings. In this paper, we present a robust and efficient algorithm for the extraction of book pages from the original 3-D volume. This step is a necessary prerequisite for a possible identification of the writing. Our method delivers high quality results for our book model and can be easily adapted to other imaging modalities. We show that it performs well even for an extreme case with low resolution input data and wavy pages.

Some historical writings cannot be investigated anymore without causing damage by touching or browsing them. Our work shows a non-invasive method for the 3-D reconstruction of historical writings and drawings written with an historical ink consisting of metallic particles. Since the 5th century Iron Gall Ink is in use as an indelible ink and is still in use. Modern, adaptable X-ray scanners used for material testing are able to image this ink such that it can be differentiated from the cellulose-based paper. The first part of our works proofs this assumption by taking single shot X-ray acquisitions. The second part focuses on a novel method that is able to image all pages of a book by a single circular X-ray CT scan followed by a 3-D reconstruction. Applying this method makes it possible to image complete books, writings or drawings without the need of page-turning or touching them anymore.

In this paper, we describe a method to enlarge the field-ofview of those scan modes by rotating the detector such that instead of the detector width the diagonal of the detector limits the lateral field-of-view for a Short and two Large Volume Scan trajectories. After implementation of the modifications we obtain a gain of 25.8% in field-of-view diameter accompanied by a simultaneous loss of height of about 50 %. The coverage is increased by 20% for the Short Scan and by 16.7% for the Large Volume Scans. After introducing a detector shift trade-off we still increase the coverage field-of-view width while compensating the axial loss. Also a reduced source-to-detector distance has been investigated, which further increases the coverage. Finally, a Helical Large Volume Scan trajectory was simulated leading to the same width gain and coverage but increasing the height by 20.8% for the maximal shift and 33.3% for the trade-off version in comparison to a standard Large Volume Scan.