On the representation of cells in bone marrow pathology by a scalar field: propagation through serial sections, co-localization and spatial interaction analysis

Background: Immunohistochemical analysis of cellular interactions in the bone marrow in situ is demanding, due to its heterogeneous cellular composition, the poor delineation and overlap of functional compartments and highly complex immunophenotypes of several cell populations (e.g. regulatory T-cells) that require immunohistochemical marker sets for unambiguous characterization. To overcome these difficulties, we herein present an approach to describe objects (e.g. cells, bone trabeculae) by a scalar field that can be propagated through registered images of serial histological sections. Methods: The transformation of objects within images (e.g. cells) to a scalar field was performed by convolution of the object’s centroids with differently formed radial basis function (e.g. for direct or indirect spatial interaction). On the basis of such a scalar field, a summation field described distributed objects within an image. Results: After image registration i) colocalization analysis could be performed on basis scalar field, which is propagated through registered images, and - due to the shape of the field – were barely prone to matching errors and morphological changes by different cutting levels; ii) furthermore, depending on the field shape the colocalization measurements could also quantify spatial interaction (e.g. direct or paracrine cellular contact); ii) the field-overlap, which represents the spatial distance, of different objects (e.g. two cells) could be calculated by the histogram intersection. Conclusions: The description of objects (e.g. cells, cell clusters, bone trabeculae etc.) as a field offers several possibilities: First, co-localization of different markers (e.g. by immunohistochemical staining) in serial sections can be performed in an automatic, objective and quantifiable way. In contrast to multicolour staining (e.g. 10-colour immunofluorescence) the financial and technical requirements are fairly minor. Second, the approach allows searching for different types of spatial interactions (e.g. direct and indirect cellular interaction) between objects by taking field shape into account (e.g. thin vs. broad). Third, by describing spatially distributed groups of objects as summation field, it gives cluster definition that relies rather on the bare object distance than on the modelled spatial cellular interaction

Polyphonic sonification of electrocardiography signals for diagnosis of cardiac pathologies

Kather JN, Hermann T, Bukschat Y, Kramer T, Schad LR, Zöllner FG. Polyphonic sonification of electrocardiography signals for diagnosis of cardiac pathologies. Scientific Reports. 2017;7(1): 44549.Electrocardiography (ECG) data are multidimensional temporal data with ubiquitous applications in the clinic. Conventionally, these data are presented visually. It is presently unclear to what degree data sonification (auditory display), can enable the detection of clinically relevant cardiac pathologies in ECG data. In this study, we introduce a method for polyphonic sonification of ECG data, whereby different ECG channels are simultaneously represented by sound of different pitch. We retrospectively applied this method to 12 samples from a publicly available ECG database. We and colleagues from our professional environment then analyzed these data in a blinded. Based on these analyses, we found that the sonification technique can be intuitively understood after a short training session. On average, the correct classification rate for observers trained in cardiology was 78%, compared to 68% and 50% for observers not trained in cardiology or not trained in medicine at all, respectively. These values compare to an expected random guessing performance of 25%. Strikingly, 27% of all observers had a classification accuracy over 90%, indicating that sonification can be very successfully used by talented individuals. These findings can serve as a baseline for potential clinical applications of ECG sonification

Enhancing protein-protein docking by new approaches to protein flexibility and scoring of docking hypotheses

Zöllner FG. Enhancing protein-protein docking by new approaches to protein flexibility and scoring of docking hypotheses. Bielefeld (Germany): Bielefeld University; 2004.Für das Verständnis von biologischen Funktionen können Proteindockingverfahren angewandt werden. Die Simulation der Interaktion von Proteinen ermöglicht einen Einblick in die Mechanismen dieser Funktionen. Viele Dockingansätze modellieren Proteine als feste Körper. Proteine sind jedoch flexibel. Besonders während des Dockens verändert sich ihre Konformation, um eine höhere Passgenauigkeit zu erzielen. Um die Ergebnisse von Dockingvorhersagen zu verbessern, muss diese Flexibilität modelliert werden. In dieser Dissertation wird ein Klassifikationsansatz beschrieben, um flexible und starre Seitenketten von Aminosäuren zu unterscheiden. Merkmale werden berechnet, um die Flexibilität zu modellieren. Als Klassifikator wird eine Support Vector Machine eingesetzt. Es lassen sich gute Klassifikationsergebnisse erzielen. Die Klassifikationsergebnisse wurden zudem im Dockingsystem ElMaR evaluiert. Im Vergleich zum Docking ohne Flexibilitätsinformationen werden für fast alle Testfälle Verbesserungen erzielt. Ein anderes Problem im Bereich Proteindocking ist die Unterscheidung von richtigen und falschen Vorhersagen. In dieser Arbeit soll die Bewertung von Dockinghypothesen des ElMaR-Systems verbessert werden. Der hier vorgestellte Ansatz beruht auf Relevance Feedback. Für verschiedene Testfälle kann das Gewichtungsschema verbessert werden, so dass eine bessere Bewertung möglich ist. Eine Adaptierung der modifizierten Gewichte auf Testfälle derselben Enzymklasse zeigt ebenfalls Verbesserungen in der Bewertung.Protein docking is important for understanding the biological functions of proteins. Simulating the interaction between proteins can give insights to the mechanisms behind these functions. In many docking systems proteins are modelled as rigid bodies but in nature proteins behave differently. Especially during docking proteins change their conformation to fit together optimally. In order to enhance docking results the flexibility of amino acid side chains has to be incorporated. Within the scope of this thesis, a classification approach to discriminate flexible and rigid side chains is described. In order to model the flexibility, features are calculated and a support vector machine is trained. A classification of side chains can be done at high accuracy. The gained flexibility information is evaluated using the docking system ElMaR. Using the flexibility information shows improvements for most of the used test cases compared to docking them without using any information about the flexibility of the structures. Another problem in the field of protein docking is the discrimination of true and false docking predictions. In this work, the improvement of scoring docking hypotheses is addressed. Here, a relevance feedback approach is proposed to enhance the scoring of the ElMaR docking system. For different test cases the weighting scheme could be improved so that true and false docking predictions could be discriminated at higher accuracy. An adaptation of these weights to a larger set of test cases belonging to the same enzyme class shows improvements, too

Supplementary Material for "Polyphonic sonification of electrocardiography signals for diagnosis of cardiac pathologies"

Kather JN, Hermann T, Bukschat Y, Kramer T, Schad LR, Zöllner FG. Supplementary Material for "Polyphonic sonification of electrocardiography signals for diagnosis of cardiac pathologies". Bielefeld University; 2017.#### S1: Normal ECG sample <img src="https://pub.uni-bielefeld.de/download/2908653/2908701" width="40%" height="40%" style="float:right;" > Description: This supplementary material contains ECG data sonification of a healthy control. * [s0306lrem.wav](https://pub.uni-bielefeld.de/download/2908653/2908702) contains sound data for a six-channel data set, 10 seconds * [s0306lrem.png](https://pub.uni-bielefeld.de/download/2908653/2908701) contains the corresponding visual data, 10 seconds, sampling rate of 1000 Hz #### S2: Incremental signal Description: This supplementary material contains an incremental number of channels from a pathological (STEMI) ECG data sonification <img src="https://pub.uni-bielefeld.de/download/2908653/2908709" width="40%" height="40%" style="float:right;" > - [incremental_01.wav](https://pub.uni-bielefeld.de/download/2908653/2908703) only channel III (lowest), 10 seconds - [incremental_02.wav](https://pub.uni-bielefeld.de/download/2908653/2908704) channel III and aVF, 10 seconds - [incremental_03.wav](https://pub.uni-bielefeld.de/download/2908653/2908705) channel III and aVF and II, 10 seconds - [incremental_04.wav](https://pub.uni-bielefeld.de/download/2908653/2908706) channel III and aVF and II and -aVR, 10 seconds - [incremental_05.wav](https://pub.uni-bielefeld.de/download/2908653/2908707) channels III through I, 10 seconds - [incremental_06.wav](https://pub.uni-bielefeld.de/download/2908653/2908708) all channels, 10 seconds - [incremental_signal.png](https://pub.uni-bielefeld.de/download/2908653/2908709) shows the corresponding visual data, 10 seconds, sampling rate of 257 Hz #### S3: Pathological ECG samples Description: This supplementary material contains sonified ECG data of four pathological samples, corresponding to Figure 2 in the main manuscript. All samples are 10 seconds in length (original sound file in sub-folder “original”, amplified sound file in sub-folder “amplified”). - [Sample_I04m_STEMI.wav](https://pub.uni-bielefeld.de/download/2908653/2908710) ST-elevation myocardial infarction - [Sample_I37m_PVC.wav](https://pub.uni-bielefeld.de/download/2908653/2908711) Premature ventricular contraction - [Sample_I50m_AF.wav](https://pub.uni-bielefeld.de/download/2908653/2908712) Atrial fibrillation - [Sample_I51m_Bigeminy.wav](https://pub.uni-bielefeld.de/download/2908653/2908713) Bigeminy #### S4: Flowchart of the algorithm File: [S4_sonification_procedure_schematic.pdf](https://pub.uni-bielefeld.de/download/2908653/2908714) Description: This PDF is a detailed flowchart of the algorithm including all relevant parameters. This can be used to implement our proposed method in any programming language. #### S5: Observer performance during data analysis File: [S5_performance_data.xls](https://pub.uni-bielefeld.de/download/2908653/2908715) Description: The spreadsheet contains all results of the data analysis by 22 blinded observers in three groups. S1 to S12 refer to the ECG samples, the number in each cell in these columns shows the classification by the observer. Correct classifications are shown in green, errors are shown in red. “Instrument” denotes whether the observer had been actively playing an instrument for three or more years at any time during their life. #### S6 Source code of Polyphonic ECG Sonification (in SuperCollider3) File: [S6_Polyphonic-ECG-Sonification.zip](https://pub.uni-bielefeld.de/download/2908653/2908761) Description: The zipped folder contains the Supercollider3 source code, two data files in csv format and the resulting sonifications for Polyphonic ECG Sonification. Instructions for how to render the sonifications are given in the source code header. #### S7 Source code of Polyphonic ECG Sonification (Matlab) File: [S7_Polyphonic-ECG-Sonification-matlab.zip](https://pub.uni-bielefeld.de/download/2908653/2908760) Description: This file contains the source code in matlab which was also used for the rendering of sonifications in the study

New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images.

BACKGROUND:Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions. METHODS AND RESULTS:In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin-3,3'-Diaminobenzidine (DAB) images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images. VALIDATION:To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images. CONTEXT:Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics

Acceleration of Magnetic Resonance Fingerprinting Reconstruction Using Denoising and Self-Attention Pyramidal Convolutional Neural Network

Magnetic resonance fingerprinting (MRF) based on echo-planar imaging (EPI) enables whole-brain imaging to rapidly obtain T1 and T2* relaxation time maps. Reconstructing parametric maps from the MRF scanned baselines by the inner-product method is computationally expensive. We aimed to accelerate the reconstruction of parametric maps for MRF-EPI by using a deep learning model. The proposed approach uses a two-stage model that first eliminates noise and then regresses the parametric maps. Parametric maps obtained by dictionary matching were used as a reference and compared with the prediction results of the two-stage model. MRF-EPI scans were collected from 32 subjects. The signal-to-noise ratio increased significantly after the noise removal by the denoising model. For prediction with scans in the testing dataset, the mean absolute percentage errors between the standard and the final two-stage model were 3.1%, 3.2%, and 1.9% for T1, and 2.6%, 2.3%, and 2.8% for T2* in gray matter, white matter, and lesion locations, respectively. Our proposed two-stage deep learning model can effectively remove noise and accurately reconstruct MRF-EPI parametric maps, increasing the speed of reconstruction and reducing the storage space required by dictionaries

Examples of digitally re-stained H&E images.

<p>(A-D) Normal lymph node tissue, (E-H) colorectal carcinoma tissue, (I-L) aspergillus, (M-P) breast cancer tissue surrounded by lymph node tissue. For each sample, the original image, the re-stained image and the original and resulting color map are shown. The color map used for re-staining was orange (#FFAD00)—blue (#006EFF). Sizes are: (A) 742 ∗ 742<i>μ</i>m, (E) 742 ∗ 742<i>μ</i>m, (N) 594 ∗ 594<i>μ</i>m. (I) had no specified size (image source see List B in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0145572#pone.0145572.s004" target="_blank">S1 File</a>).</p

An optimized approach for color deconvolution based on principal component analysis minimizes the deconvolution residual.

<p>A) the image pixels of a given blue—brown immunostained image are plotted in the space of optical density color channels (OD R = red, OD G = green, OD B = blue). The commonly used standard color deconvolution vectors define a plane that roughly, but not optimally, approximates the data set (darker plane). The brighter plane shows the optimal plane containing the first and the second principal component vector of the actual image pixel dataset. B) Quantification of mean square error of the residual channel after color deconvolution with different deconvolution vectors. Boxes = 25th to 75th percentile, line = median, whiskers = most extreme data points except outliers, ‘+’ = outliers.</p

Foreground-background contrast in phantom images can be markedly increased by applying a new color map.

<p>A-F) A phantom image was colored in one of six bivariate color maps, the panel title indicate the hexadecimal codes of the foreground and background color (F represents a typical blue—brown standard color map while A-E represent new color combinations). G) 225 combinations of 15 distinct colors were pairwisely compared. The grayscale intensity and overlayed number indicate the perceptual contrast of phantom images that were digitally stained with the respective color map (numbers: mean ± standard deviation). All measurements were normalized to the maximum contrast. Reading example: The standard brown (#B58C70) on blue (#5C5FA1) color map resulted in a perceptual contrast that was 39% ± 7% of the maximally achieved contrast.</p

Digital re-staining markedly increases perceptual contrast in N = 596 actual histological images.

<p>Perceptual contrast before (gray boxes) and after (black boxes) color map improvement was measured in eight sets of histological images of human solid tumors (see ‘Materials and Methods’, total N = 596 samples). For all datasets, a pronounced increase of perceptual contrast can be seen. Boxes = 25th to 75th percentile, line = median, whiskers = most extreme data points except outliers, ‘+’ = outliers. The color map used for re-staining was blue (#006EFF)—orange (#FFAD00).</p