Browsing by Author "Driel, Marc A. van"

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    Visual Analytics in Digital Pathology: Challenges and Opportunities
    (The Eurographics Association, 2019) Corvò, Alberto; Westenberg, Michel A.; Wimberger-Friedl, Reinhold; Fromme, Stephan; Peeters, Michel M. R.; Driel, Marc A. van; Wijk, Jarke J. van; Kozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgia
    The advances in high-throughput digitization, digital pathology systems, and quantitative image analysis opened new horizons in pathology. The diagnostic work of the pathologists and their role is likely to be augmented with computer-assistance and more quantitative information at hand. The recent success of artificial intelligence (AI) and computer vision methods demonstrated that in the coming years machines will support pathologists in typically tedious and highly subjective tasks and also in better patient stratification. In spite of clear future improvements in the diagnostic workflow, questions on how to effectively support the pathologists and how to integrate current data sources and quantitative information still persist. In this context, Visual Analytics (VA) - as the discipline that aids users to solve complex problems with an interactive and visual approach - can play a vital role to support the cognitive skills of pathologists and the large volumes of data available. To identify the main opportunities to employ VA in digital pathology systems, we conducted a survey with 20 pathologists to characterize the diagnostic practice and needs from a user perspective. From our findings, we discuss how VA can leverage quantitative image data to empower pathologists with new advanced digital pathology systems.
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    Visual Analytics in Histopathology Diagnostics: a Protocol-Based Approach
    (The Eurographics Association, 2018) Corvò, Alberto; Westenberg, Michel A.; Driel, Marc A. van; Wijk, Jarke J.van; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
    Computer-Aided-Diagnosis (CAD) systems supporting the diagnostic process are widespread in radiology. Digital Pathology is still behind in the introduction of such solutions. Several studies investigated pathologists' behavior but only a few aimed to improve the diagnostic and report process with novel applications. In this work we designed and implemented a first protocol-based CAD viewer supported by visual analytics. The system targets the optimization of the diagnostic workflow in breast cancer diagnosis by means of three image analysis features that belong to the standard grading system (Nottingham Histologic Grade). A pathologist's routine was tracked during the examination of breast cancer tissue slides and diagnostic traces were analyzed from a qualitative perspective. Accordingly, a set of generic requirements was elicited to define the design and the implementation of the CAD-Viewer. A first qualitative evaluation conducted with five pathologists shows that the interface suffices the diagnostic workflow and diminishes the manual effort. We present promising evidence of the usefulness of our CAD-viewer and opportunities for its extension and integration in clinical practice. As a conclusion, the findings demonstrate that it is feasibile to optimize the Nottingham Grading workflow and, generally, the histological diagnosis by integrating computational pathology data with visual analytics techniques.