Stay focused: An eye-tracking study on reading computer network graphics

Reicht es aus, wenn Lehrende Zeit und Energie darauf verwenden, qualitativ hochwertige Lehr- und Lernmaterialien zu erstellen, oder sollten sie auch (mehr) Zeit darauf verwenden, den Studierenden das effektive und effiziente Lesen bzw. Betrachten dieses Materials zu vermitteln? Darf davon ausgegangen werden, dass die Studierenden diese Fähigkeiten ohnehin bereits mitbringen? Im Rahmen einer qualitativen Eye-Tracking-Studie mit Novizen und Experten wurde diese Fragestellung am Beispiel einer Rechnernetze-Grafik untersucht. Mit Hilfe eines eigenentwickelten Werkzeugs zur Generierung von Areas-of-Interest-Sequenzdiagrammen wurden die gewonnenen Messergebnisse ausgewertet. Im Resultat zeigten sich deutliche Unterschiede hinsichtlich der Betrachtungsweisen. Nicht nur das Vorgehen, sondern auch die Informationsaufnahme unterschieden sich prägnant. Im Folgenden werden die Studie, der theoretische Hintergrund, die gewonnenen Ergebnisse sowie das eigenentwickelte Analysewerkzeug für Messungen aus Eye-Tracking-Studien vorgestellt.Is it enough for educators to spend time and energy, creating high-quality teaching and learning materials, or should educators also spend (more) time teaching students how to effectively and efficiently read and view these materials? Can students be expected to bring these skills with them anyway? This question was investigated in a qualitative eye-tracking study with novices and experts using a computer network graphic as an example. With the help of a self-developed tool for the generation of Areas-of-Interest sequence charts, the obtained measurement results got evaluated. The results showed apparent differences concerning the approaches. Not only the procedure but also the information acquisition differed significantly. In the following, we present the study, its theoretical background, obtained results, as well as a self-developed analysis tool for eye-tracking studies

GBM Volumetry using the 3D Slicer Medical Image Computing Platform

Volumetric change in glioblastoma multiforme (GBM) over time is a critical factor in treatment decisions. Typically, the tumor volume is computed on a slice-by-slice basis using MRI scans obtained at regular intervals. (3D)Slicer – a free platform for biomedical research – provides an alternative to this manual slice-by-slice segmentation process, which is significantly faster and requires less user interaction. In this study, 4 physicians segmented GBMs in 10 patients, once using the competitive region-growing based GrowCut segmentation module of Slicer, and once purely by drawing boundaries completely manually on a slice-by-slice basis. Furthermore, we provide a variability analysis for three physicians for 12 GBMs. The time required for GrowCut segmentation was on an average 61% of the time required for a pure manual segmentation. A comparison of Slicer-based segmentation with manual slice-by-slice segmentation resulted in a Dice Similarity Coefficient of 88.43 ± 5.23% and a Hausdorff Distance of 2.32 ± 5.23 mm

Repeatability of Multiparametric Prostate MRI Radiomics Features

In this study we assessed the repeatability of the values of radiomics features for small prostate tumors using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI) images. The premise of radiomics is that quantitative image features can serve as biomarkers characterizing disease. For such biomarkers to be useful, repeatability is a basic requirement, meaning its value must remain stable between two scans, if the conditions remain stable. We investigated repeatability of radiomics features under various preprocessing and extraction configurations including various image normalization schemes, different image pre-filtering, 2D vs 3D texture computation, and different bin widths for image discretization. Image registration as means to re-identify regions of interest across time points was evaluated against human-expert segmented regions in both time points. Even though we found many radiomics features and preprocessing combinations with a high repeatability (Intraclass Correlation Coefficient (ICC) > 0.85), our results indicate that overall the repeatability is highly sensitive to the processing parameters (under certain configurations, it can be below 0.0). Image normalization, using a variety of approaches considered, did not result in consistent improvements in repeatability. There was also no consistent improvement of repeatability through the use of pre-filtering options, or by using image registration between timepoints to improve consistency of the region of interest localization. Based on these results we urge caution when interpreting radiomics features and advise paying close attention to the processing configuration details of reported results. Furthermore, we advocate reporting all processing details in radiomics studies and strongly recommend making the implementation available

The NCI Imaging Data Commons as a platform for reproducible research in computational pathology

Background and Objectives: Reproducibility is a major challenge in developing machine learning (ML)-based solutions in computational pathology (CompPath). The NCI Imaging Data Commons (IDC) provides >120 cancer image collections according to the FAIR principles and is designed to be used with cloud ML services. Here, we explore its potential to facilitate reproducibility in CompPath research. Methods: Using the IDC, we implemented two experiments in which a representative ML-based method for classifying lung tumor tissue was trained and/or evaluated on different datasets. To assess reproducibility, the experiments were run multiple times with separate but identically configured instances of common ML services. Results: The AUC values of different runs of the same experiment were generally consistent. However, we observed small variations in AUC values of up to 0.045, indicating a practical limit to reproducibility. Conclusions: We conclude that the IDC facilitates approaching the reproducibility limit of CompPath research (i) by enabling researchers to reuse exactly the same datasets and (ii) by integrating with cloud ML services so that experiments can be run in identically configured computing environments.Comment: 13 pages, 5 figures; improved manuscript, new experiments with P100 GP

DCMQI: An open source library for standardized communication of quantitative image analysis results using DICOM

Quantitative analysis of clinical image data is an active area of research that holds promise for precision medicine, early assessment of treatment response, and objective characterization of the disease. Interoperability, data sharing, and the ability to mine the resulting data are of increasing importance, given the explosive growth in the number of quantitative analysis methods being proposed. The Digital Imaging and Communications in Medicine (DICOM) standard is widely adopted for image and metadata in radiology. dcmqi (DICOM for Quantitative Imaging) is a free, open source library that implements conversion of the data stored in commonly used research formats into the standard DICOM representation. dcmqi source code is distributed under BSD-style license. It is freely available as a precompiled binary package for every major operating system, as a Docker image, and as an extension to 3D Slicer. Installation and usage instructions are provided in the GitHub repository at https://github.com/qiicr/dcmqi

SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research

International audienceDiffusion magnetic resonance imaging (dMRI) is the only non-invasive method for mapping white matter connections in the brain. We describe SlicerDMRI, a software suite that enables visualization and analysis of dMRI for neuroscientific studies and patient-specific anatomical assessment. SlicerDMRI has been successfully applied in multiple studies of the human brain in health and disease, and here we especially focus on its cancer research applications. As an extension module of the 3D Slicer medical image computing platform, the SlicerDMRI suite enables dMRI analysis in a clinically relevant multimodal imaging workflow. Core SlicerDMRI functionality includes diffusion tensor estimation, white matter tractography with single and multi-fiber models, and dMRI quantification. SlicerDMRI supports clinical DICOM and research file formats, is open-source and cross-platform, and can be installed as an extension to 3D Slicer (www.slicer.org). More information, videos, tutorials, and sample data are available at dmri.slicer.org