Digital whole-slide images of pathological tissue samples have recently become feasible for use within routine diagnostic practice. These gigapixel sized images enable pathologists to perform reviews using computer workstations instead of microscopes. Existing workstations visualize scanned images by providing a zoomable image space that reproduces the capabilities of the microscope. This paper presents a novel visualization approach that enables filtering of the scale-space according to color preference. The visualization method reveals diagnostically important patterns that are otherwise not visible. The paper demonstrates how this approach has been implemented into a fully functional prototype that lets the user navigate the visualization parameter space in real time. The prototype was evaluated for two common clinical tasks with eight pathologists in a within-subjects study. The data reveal that task efficiency increased by 15 using the prototype, with maintained accuracy. By analyzing behavioral strategies, it was possible to conclude that efficiency gain was caused by a reduction of the panning needed to perform systematic search of the images. The prototype system was well received by the pathologists who did not detect any risks that would hinder use in clinical routine.
Previous visualization work for pathology imaging has focused on improving the speed of viewing these gigapixel images @cite_29 , or dealing with 3D-stacks of microscopic images @cite_0 . For the day to day needs of a pathology lab, commercially available scanners can capture, stitch and organize digital slides into tiled pyramidal images in minutes @cite_10 , and digital viewers that can closely reproduce the experience of using a microscope @cite_18 . However, it is important to keep viewing latency down, where the bottlenecks typically are slow hard drives @cite_29 and slow network response times.
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This paper presents the first volume visualization system that scales to petascale volumes imaged as a continuous stream of high-resolution electron microscopy images. Our architecture scales to dense, anisotropic petascale volumes because it: (1) decouples construction of the 3D multi-resolution representation required for visualization from data acquisition, and (2) decouples sample access time during ray-casting from the size of the multi-resolution hierarchy. Our system is designed around a scalable multi-resolution virtual memory architecture that handles missing data naturally, does not pre-compute any 3D multi-resolution representation such as an octree, and can accept a constant stream of 2D image tiles from the microscopes. A novelty of our system design is that it is visualization-driven: we restrict most computations to the visible volume data. Leveraging the virtual memory architecture, missing data are detected during volume ray-casting as cache misses, which are propagated backwards for on-demand out-of-core processing. 3D blocks of volume data are only constructed from 2D microscope image tiles when they have actually been accessed during ray-casting. We extensively evaluate our system design choices with respect to scalability and performance, compare to previous best-of-breed systems, and illustrate the effectiveness of our system for real microscopy data from neuroscience. Histology is the study of the structure of biological tissue using microscopy techniques. As digital imaging technology advances, high resolution microscopy of large tissue volumes is becoming feasible; however, new interactive tools are needed to explore and analyze the enormous datasets. In this paper we present a visualization framework that specifically targets interactive examination of arbitrarily large image stacks. Our framework is built upon two core techniques: display-aware processing and GPU-accelerated texture compression. With display-aware processing, only the currently visible image tiles are fetched and aligned on-the-fly, reducing memory bandwidth and minimizing the need for time-consuming global pre-processing. Our novel texture compression scheme for GPUs is tailored for quick browsing of image stacks. We evaluate the usability of our viewer for two histology applications: digital pathology and visualization of neural structure at nanoscale-resolution in serial electron micrographs. Digital pathology promises a number of benefits in efficiency in surgical pathology, yet the longer time required to review a virtual slide than a glass slide currently represents a significant barrier to the routine use of digital pathology. We aimed to create a novel workstation that enables pathologists to view a case as quickly as on the conventional microscope. The Leeds Virtual Microscope (LVM) was evaluated using a mixed factorial experimental design. Twelve consultant pathologists took part, each viewing one long cancer case (12-25 slides) on the LVM and one on a conventional microscope. Total time taken and diagnostic confidence were similar for the microscope and LVM, as was the mean slide viewing time. On the LVM, participants spent a significantly greater proportion of the total task time viewing slides and revisited slides more often. The unique design of the LVM, enabling real-time rendering of virtual slides while providing users with a quick and intuitive way to navigate within and between slides, makes use of digital pathology in routine practice a realistic possibility. With further practice with the system, diagnostic efficiency on the LVM is likely to increase yet more.