Uncertainty-aware Visualization in Medical Imaging - A Survey

Medical imaging (image acquisition, image transformation, and image visualization) is a standard tool for clinicians in order to make diagnoses, plan surgeries, or educate students. Each of these steps is affected by uncertainty, which can highly influence the decision-making process of clinicians. Visualization can help in understanding and communicating these uncertainties. In this manuscript, we aim to summarize the current state-of-the-art in uncertainty-aware visualization in medical imaging. Our report is based on the steps involved in medical imaging as well as its applications. Requirements are formulated to examine the considered approaches. In addition, this manuscript shows which approaches can be combined to form uncertainty-aware medical imaging pipelines. Based on our analysis, we are able to point to open problems in uncertainty-aware medical imaging

Ten Open Challenges in Medical Visualization

The medical domain has been an inspiring application area in visualization research for many years already, but many open challenges remain. The driving forces of medical visualization research have been strengthened by novel developments, for example, in deep learning, the advent of affordable VR technology, and the need to provide medical visualizations for broader audiences. At IEEE VIS 2020, we hosted an Application Spotlight session to highlight recent medical visualization research topics. With this article, we provide the visualization community with ten such open challenges, primarily focused on challenges related to the visualization of medical imaging data. We first describe the unique nature of medical data in terms of data preparation, access, and standardization. Subsequently, we cover open visualization research challenges related to uncertainty, multimodal and multiscale approaches, and evaluation. Finally, we emphasize challenges related to users focusing on explainable AI, immersive visualization, P4 medicine, and narrative visualization.acceptedVersio

Towards an Image-based Indicator for Peripheral Artery Disease Classification and Localization

Peripheral Artery Disease (PAD) is an often occurring problem caused by narrowed veins. With this type of disease, mostly the legs receive an insufficient supply of blood to sustain their functions. This can result in an amputation of extremities or strokes. In order to quantify the risks, doctors consult a classification table which is based on the pain response of a patient. This classification is subjective and does not indicate the exact origin of the PAD symptoms. Resulting from this, complications can occur unprompted. We present the first results for an image-based indicator assisting medical doctors in estimating the stage of PAD and its location. Therefore, a segmentation tree is utilized to compare the changes in a healthy versus diseased leg. We provide a highlighting mechanism that allows users to review the location of changes in selected structures. To show the effectiveness of the presented approach, we demonstrate a localization of the PAD and show how the presented technique can be utilized for a novel image-based indicator of PAD stages

Visual Analytics of Cascaded Bottlenecks in Planar Flow Networks

Finding bottlenecks and eliminating them to increase the overall flow of a network often appears in real world applications, such as production planning, factory layout, flow related physical approaches, and even cyber security. In many cases, several edges can form a bottleneck (cascaded bottlenecks). This work presents a visual analytics methodology to analyze these cascaded bottlenecks. The methodology consists of multiple steps: identification of bottlenecks, identification of potential improvements, communication of bottlenecks, interactive adaption of bottlenecks, and a feedback loop that allows users to adapt flow networks and their resulting bottlenecks until they are satisfied with the flow network configuration. To achieve this, the definition of a minimal cut is extended to identify network edges that form a (cascaded) bottleneck. To show the effectiveness of the presented approach, we applied the methodology to two flow network setups and show how the overall flow of these networks can be improved

Uncertainty in humanities network visualization

Network visualization is one of the most widely used tools in digital humanities research. The idea of uncertain or “fuzzy” data is also a core notion in digital humanities research. Yet network visualizations in digital humanities do not always prominently represent uncertainty. In this article, we present a mathematical and logical model of uncertainty as a range of values which can be used in network visualizations. We review some of the principles for visualizing uncertainty of different kinds, visual variables that can be used for representing uncertainty, and how these variables have been used to represent different data types in visualizations drawn from a range of non-humanities fields like climate science and bioinformatics. We then provide examples of two diagrams: one in which the variables displaying degrees of uncertainty are integrated/pinto the graph and one in which glyphs are added to represent data certainty and uncertainty. Finally, we discuss how probabilistic data and what-if scenarios could be used to expand the representation of uncertainty in humanities network visualizations

Image Processing under Uncertainty

Novel image processing techniques have been in development for decades, but most of these techniques are barely used in real world applications. This results in a gap between image processing research and real-world applications; this thesis aims to close this gap. In an initial study, the quantification, propagation, and communication of uncertainty were determined to be key features in gaining acceptance for new image processing techniques in applications. This thesis presents a holistic approach based on a novel image processing pipeline, capable of quantifying, propagating, and communicating image uncertainty. This work provides an improved image data transformation paradigm, extending image data using a flexible, high-dimensional uncertainty model. Based on this, a completely redesigned image processing pipeline is presented. In this pipeline, each step respects and preserves the underlying image uncertainty, allowing image uncertainty quantification, image pre-processing, image segmentation, and geometry extraction. This is communicated by utilizing meaningful visualization methodologies throughout each computational step. The presented methods are examined qualitatively by comparing to the Stateof- the-Art, in addition to user evaluation in different domains. To show the applicability of the presented approach to real world scenarios, this thesis demonstrates domain-specific problems and the successful implementation of the presented techniques in these domains

Image Processing under Uncertainty

Novel image processing techniques have been in development for decades, but most of these techniques are barely used in real world applications. This results in a gap between image processing research and real-world applications; this thesis aims to close this gap. In an initial study, the quantification, propagation, and communication of uncertainty were determined to be key features in gaining acceptance for new image processing techniques in applications. This thesis presents a holistic approach based on a novel image processing pipeline, capable of quantifying, propagating, and communicating image uncertainty. This work provides an improved image data transformation paradigm, extending image data using a flexible, high-dimensional uncertainty model. Based on this, a completely redesigned image processing pipeline is presented. In this pipeline, each step respects and preserves the underlying image uncertainty, allowing image uncertainty quantification, image pre-processing, image segmentation, and geometry extraction. This is communicated by utilizing meaningful visualization methodologies throughout each computational step. The presented methods are examined qualitatively by comparing to the Stateof- the-Art, in addition to user evaluation in different domains. To show the applicability of the presented approach to real world scenarios, this thesis demonstrates domain-specific problems and the successful implementation of the presented techniques in these domains

Uncertainty-Awareness in Open Source Visualization Solutions

The popularity of open source tools is constantly increasing, as they offer the possibility to quickly create and use visualizations of arbitrary data sources. As the positive effects of uncertainty communication to all kinds of visualizations were discussed over the past years in the academic world, this work examines the uncertaintyawareness of open source solutions. Through a categorization and classification of available tools, this paper identifies the problems in uncertainty-awareness of available open source solutions. To solve this problem, a new paradigm of data handling that extends raw datasets by its uncertainty is suggeste

Teaching Image Processing and Visualization Principles to Medicine Students

Although image processing becomes increasingly important in most applications such as medicine, image processing and visualization is usually not a part of the medical education and therefore not widely spread in clinical daily routine. Contrary to students from computer science, medical students are usually not familiar to computational models or algorithms and require a different view of the algorithms instead of knowing each computational detail. To solve this problem this paper presents the concept of a lecture that aims to impart image processing and visualization principals for students in medicine in order to pioneer a higher acceptance and propagation of image processing techniques in clinical daily routine