CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans

Accurate and fast extraction of lung volumes from computed tomography (CT) scans remains in a great demand in the clinical environment because the available methods fail to provide a generic solution due to wide anatomical variations of lungs and existence of pathologies. Manual annotation, current gold standard, is time consuming and often subject to human bias. On the other hand, current state-of-the-art fully automated lung segmentation methods fail to make their way into the clinical practice due to their inability to efficiently incorporate human input for handling misclassifications and praxis. This paper presents a lung annotation tool for CT images that is interactive, efficient, and robust. The proposed annotation tool produces an "as accurate as possible" initial annotation based on the fuzzy-connectedness image segmentation, followed by efficient manual fixation of the initial extraction if deemed necessary by the practitioner. To provide maximum flexibility to the users, our annotation tool is supported in three major operating systems (Windows, Linux, and the Mac OS X). The quantitative results comparing our free software with commercially available lung segmentation tools show higher degree of consistency and precision of our software with a considerable potential to enhance the performance of routine clinical tasks.Comment: 4 pages, 6 figures; to appear in the proceedings of 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014

Painterly rendering techniques: A state-of-the-art review of current approaches

In this publication we will look at the different methods presented over the past few decades which attempt to recreate digital paintings. While previous surveys concentrate on the broader subject of non-photorealistic rendering, the focus of this paper is firmly placed on painterly rendering techniques. We compare different methods used to produce different output painting styles such as abstract, colour pencil, watercolour, oriental, oil and pastel. Whereas some methods demand a high level of interaction using a skilled artist, others require simple parameters provided by a user with little or no artistic experience. Many methods attempt to provide more automation with the use of varying forms of reference data. This reference data can range from still photographs, video, 3D polygonal meshes or even 3D point clouds. The techniques presented here endeavour to provide tools and styles that are not traditionally available to an artist. Copyright © 2012 John Wiley & Sons, Ltd

Multimedia information technology and the annotation of video

The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning

Differences of size and shape of active and inactive X-chromosome domains in human amniotic fluid cell nuclei

It is a widely held belief that the inactive X-chromosome (Xi) in female cell nuclei is strongly condensed as compared to the largely decondensed active X-chromosome (Xa). We have reconsidered this problem and painted X-chromosome domains in nuclei of subconfluent, female and male human amniotic fluid cell cultures (46, XX and 46, XY) by chromosomal in situ suppression (CISS) hybridization with biotinylated human X-chromosome specific library DNA. FITC-conjugated avidin was used for probe detection and nuclei were counterstained with propidium iodide (PI). The shape of these nuclei resembling flat ellipsoids or elliptical cylinders makes them suitable for both two-dimensional (2D) and three-dimensional (3D) analyses. 2D analyses of Xi- and Xa-domains were performed in 34 female cell nuclei by outlining of the painted domains using a camera lucida. Identification of the sex chromatin body in DAPI-stained nuclei prior to CISS-hybridization was confirmed by its colocalization with one of the two painted X-domains. In 31 of the 34 nuclei the area AXi for the inactive X-domain was smaller than the area AXa for the active domain (mean ratio AXa/AXi = 1.9 ± 0.8 SD, range 1.0-4.3). The signed rank test showed a highly significant (P r(Xi) demonstrating a generally more elongated structure of Xa. For 3D analysis a confocal scanning laser fluorescence microscope (CSLFM) was used. Ten to 20 light optical sections (PI-image, FITC-image) were registered with equal spacings (approx. 0.4 m). A thresholding procedure was applied to determine the PI-labeled nuclear and FITC-labeled X-domain areas in each section. Estimated slice volumes were used to compute total nuclear and X-domain volumes. In a series of 35 female nuclei most domains extended from the top to the bottom nuclear sections. The larger of the two X-chromosome domains comprised (3.7 ± 1.7 S.D.)% of the nuclear volume. A mean ratio of 1.2 ± 0.2 SD (range 1.1-2.3) was found for the volumes of the larger and the smaller X-domains in these female nuclei. In a series of 27 male amniotic fluid cell nuclei the relative X-chromosome domain volume comprised (4.0 ± 2.6 S.D.)%. These findings indicate that differences in the 3D expansion of active and inactive X-chromosome domains are less pronounced than previously thought. A current model suggests that chromosome domains consist of a compact core surrounded by loosely coiled outer chromatin fiber loops. The latter fraction may be considerably larger in Xa- as compared to Xi-domains. We suggest that the interactive outlining procedure used in the 2D analyses included the loosely structured domain periphery more accurately, while the threshold algorithm applied to light optical sections delineated the more compact core of the domains, leading to smaller and more similar volume estimates of Xa and Xi. Present limitations of nuclear and chromosome domain volume measurements using confocal laser scanning microscopy are discussed

ColorSketch: A Drawing Assistant for Generating Color Sketches from Photos

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Calipso: Physics-based Image and Video Editing through CAD Model Proxies

We present Calipso, an interactive method for editing images and videos in a physically-coherent manner. Our main idea is to realize physics-based manipulations by running a full physics simulation on proxy geometries given by non-rigidly aligned CAD models. Running these simulations allows us to apply new, unseen forces to move or deform selected objects, change physical parameters such as mass or elasticity, or even add entire new objects that interact with the rest of the underlying scene. In Calipso, the user makes edits directly in 3D; these edits are processed by the simulation and then transfered to the target 2D content using shape-to-image correspondences in a photo-realistic rendering process. To align the CAD models, we introduce an efficient CAD-to-image alignment procedure that jointly minimizes for rigid and non-rigid alignment while preserving the high-level structure of the input shape. Moreover, the user can choose to exploit image flow to estimate scene motion, producing coherent physical behavior with ambient dynamics. We demonstrate Calipso's physics-based editing on a wide range of examples producing myriad physical behavior while preserving geometric and visual consistency.Comment: 11 page

Learning to Predict Image-based Rendering Artifacts with Respect to a Hidden Reference Image

Image metrics predict the perceived per-pixel difference between a reference image and its degraded (e. g., re-rendered) version. In several important applications, the reference image is not available and image metrics cannot be applied. We devise a neural network architecture and training procedure that allows predicting the MSE, SSIM or VGG16 image difference from the distorted image alone while the reference is not observed. This is enabled by two insights: The first is to inject sufficiently many un-distorted natural image patches, which can be found in arbitrary amounts and are known to have no perceivable difference to themselves. This avoids false positives. The second is to balance the learning, where it is carefully made sure that all image errors are equally likely, avoiding false negatives. Surprisingly, we observe, that the resulting no-reference metric, subjectively, can even perform better than the reference-based one, as it had to become robust against mis-alignments. We evaluate the effectiveness of our approach in an image-based rendering context, both quantitatively and qualitatively. Finally, we demonstrate two applications which reduce light field capture time and provide guidance for interactive depth adjustment.Comment: 13 pages, 11 figure