ScrollyVis: Interactive visual authoring of guided dynamic narratives for scientific scrollytelling

Visual stories are an effective and powerful tool to convey specific information to a diverse public. Scrollytelling is a recent visual storytelling technique extensively used on the web, where content appears or changes as users scroll up or down a page. By employing the familiar gesture of scrolling as its primary interaction mechanism, it provides users with a sense of control, exploration and discoverability while still offering a simple and intuitive interface. In this paper, we present a novel approach for authoring, editing, and presenting data-driven scientific narratives using scrollytelling. Our method flexibly integrates common sources such as images, text, and video, but also supports more specialized visualization techniques such as interactive maps as well as scalar field and mesh data visualizations. We show that scrolling navigation can be used to traverse dynamic narratives and demonstrate how it can be combined with interactive parameter exploration. The resulting system consists of an extensible web-based authoring tool capable of exporting stand-alone stories that can be hosted on any web server. We demonstrate the power and utility of our approach with case studies from several of diverse scientific fields and with a user study including 12 participants of diverse professional backgrounds. Furthermore, an expert in creating interactive articles assessed the usefulness of our approach and the quality of the created stories

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

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

Careful Dissection of the Distal Ureter Is Highly Important in Nerve-sparing Radical Pelvic Surgery A 3D Reconstruction and Immunohistochemical Characterization of the Vesical Plexus

Radical hysterectomy with pelvic lymphadenectomy (RHL) is the preferred treatment for early-stage cervical cancer. Although oncological outcome is good with regard to recurrence and survival rates, it is well known that RHL might result in postoperative bladder impairments due to autonomic nerve disruption. The pelvic autonomic network has been extensively studied, but the anatomy of nerve fibers branching off the inferior hypogastric plexus to innervate the bladder is less known. Besides, the pathogenesis of bladder dysfunction after RHL is multifactorial but remains unclear. We studied the 3-dimensional anatomy and neuroanatomical composition of the vesical plexus and describe implications for RHL. Six female adult cadaveric pelvises were macroscopically dissected. Additionally, a series of 10 female fetal pelvises (embryonic age, 10-22 weeks) was studied. Paraffin-embedded blocks were transversely sliced in 8-μm sections. (Immuno) histological analysis was performed with hematoxylin and eosin, azan, and antibodies against S-100 (Schwann cells), tyrosine hydroxylase (postganglionic sympathetic fibers), and vasoactive intestinal peptide (postganglionic parasympathetic fibers). The results were 3-dimensionally visualized. The vesical plexus formed a group of nerve fibers branching off the ventral part of the inferior hypogastric plexus to innervate the bladder. In all adult and fetal specimens, the vesical plexus was closely related to the distal ureter and located in both the superficial and deep layers of the vesicouterine ligament. Efferent nerve fibers belonging to the vesical plexus predominantly expressed tyrosine hydroxylase and little vasoactive intestinal peptide. The vesical plexus is located in both layers of the vesicouterine ligament and has a very close relationship with the distal ureter. Complete mobilization of the ureter in RHL might cause bladder dysfunction due to sympathetic and parasympathetic denervation. Hence, the distal ureter should be regarded as a risk zone in which the vesical plexus can be damage

AR-Assisted Craniotomy Planning for Tumour Resection

Craniotomy is a procedure where neurosurgeons open the patient’s skull to gain direct access to the brain. The craniotomy’s position defines the access path from the skull surface to the tumour and, consequently, the healthy brain tissue to be removed to reach the tumour. This is a complex procedure where a neurosurgeon is required to mentally reconstruct spatial relations of important brain structures to avoid removing them as much as possible. We propose a visualisation method using Augmented Reality to assist in the planning of a craniotomy. The goal of this study is to visualise important brain structures aligned with the physical position of the patient and to allow a better perception of the spatial relations of the structures. Additionally, a heat map was developed that is projected on top of the skull to provide a quick overview of the structures between a chosen location on the skull and the tumour. In the experiments, tracking accuracy was assessed, and colour maps were assessed for use in an AR device. Additionally, we conducted a user study amongst neurosurgeons and surgeons from other fields to evaluate the proposed visualisation using a phantom head. Most participants indeed agree that the visualisation can assist in planning a craniotomy and feedback on future improvements towards the clinical scenario was collected. (see https://www.acm.org/publications/class-2012

PerSleep: A Visual Analytics Approach for Performance Assessment of Sleep Staging Models

Machine learning is becoming increasingly popular in the medical domain. In the near future, clinicians expect predictive models to support daily tasks such as diagnosis and prognostic analysis. For this reason, it is utterly important to evaluate and compare the performance of such models so that clinicians can safely rely on them. In this paper, we focus on sleep staging wherein machine learning models can be used to automate or support sleep scoring. Evaluation of these models is complex because sleep is a natural process, which varies among patients. For adoption in clinical routine, it is important to understand how the models perform for different groups of patients. Moreover, models can be trained to recognize different characteristics in the data, and model developers need to understand why and how performance of the different models varies. To address these challenges, we present a visual analytics approach to evaluate the performance of predictive models on sleep staging and to help experts better understand these models with respect to patient data (e.g., conditions, medication, etc.). We illustrate the effectiveness of our approach by comparing multiple models trained on real-world sleep staging data with experts

GLANCE: Visual Analytics for Monitoring Glaucoma Progression

Deep learning is increasingly used in the field of glaucoma research. Although deep learning models can achieve high accuracy, issues with trust, interpretability, and practical utility form barriers to adoption in clinical practice. In this study, we explore whether and how visualizations of deep learning-based measurements can be used for glaucoma management in the clinic. Through iterative design sessions with ophthalmologists, vision researchers, and manufacturers of optical coherence tomography (OCT) instruments, we distilled four main tasks, and designed a visualization tool that incorporates a visual field (VF) prediction model to provide clinical decision support in managing glaucoma progression. The tasks are: (1) assess reliability of a prediction, (2) understand why the model made a prediction, (3) alert to features that are relevant, and (4) guide future scheduling of VFs. Our approach is novel in that it considers utility of the system in a clinical context where time is limited. With use cases and a pilot user study, we demonstrate that our approach can aid clinicians in clinical management decisions and obtain appropriate trust in the system. Taken together, our work shows how visual explanations of automated methods can augment clinicians' knowledge and calibrate their trust in DL-based measurements during clinical decision making

Understanding Lymphatic Drainage Pathways of the Ovaries to Predict Sites for Sentinel Nodes in Ovarian Cancer

OBJECTIVE: In ovarian cancer, detection of sentinel nodes is an upcoming procedure. Perioperative determination of the patient’s sentinel node(s) might prevent a radical lymphadenectomy and associated morbidity. It is essential to understand the lymphatic drainage pathways of the ovaries, which are surprisingly up till now poorly investigated, to predict the anatomical regions where sentinel nodes can be found. We aimed to describe the lymphatic drainage pathways of the human ovaries including their compartmental fascia borders. METHODS: A series of 3 human female fetuses and tissues samples from 1 human cadaveric specimen were studied. Immunohistochemical analysis was performed on paraffin-embedded transverse sections (8 or 10 μm) using antibodies against Lyve-1, S100, and α-smooth muscle actin to identify the lymphatic endothelium, Schwann, and smooth muscle cells, respectively. Three-dimensional reconstructions were created. RESULTS: Two major and 1 minor lymphatic drainage pathways from the ovaries were detected. One pathway drained via the proper ligament of the ovaries (ovarian ligament) toward the lymph nodes in the obturator fossa and the internal iliac artery. Another pathway drained the ovaries via the suspensory ligament (infundibulopelvic ligament) toward the para-aortic and paracaval lymph nodes. A third minor pathway drained the ovaries via the round ligament to the inguinal lymph nodes. Lymph vessels draining the fallopian tube all followed the lymphatic drainage pathways of the ovaries. CONCLUSIONS: The lymphatic drainage pathways of the ovaries invariably run via the suspensory ligament (infundibulopelvic ligament) and the proper ligament of the ovaries (ovarian ligament), as well as through the round ligament of the uterus. Because ovarian cancer might spread lymphogenously via these routes, the sentinel node can be detected in the para-aortic and paracaval regions, obturator fossa and surrounding internal iliac arteries, and inguinal regions. These findings support the strategy of injecting tracers in both ovarian ligaments to identify sentinel nodes