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.
@cite_0 @cite_29 @cite_10 @cite_18
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Background: Digital slides obtained from whole slide imaging (WSI) platforms are typically viewed in two dimensions using desktop personal computer monitors or more recently on mobile devices. To the best of our knowledge, we are not aware of any studies viewing digital pathology slides in a virtual reality (VR) environment. VR technology enables users to be artificially immersed in and interact with a computer-simulated world. Oculus Rift is among the world's first consumer-targeted VR headsets, intended primarily for enhanced gaming. Our aim was to explore the use of the Oculus Rift for examining digital pathology slides in a VR environment. Methods: An Oculus Rift Development Kit 2 (DK2) was connected to a 64-bit computer running Virtual Desktop software. Glass slides from twenty randomly selected lymph node cases (ten with benign and ten malignant diagnoses) were digitized using a WSI scanner. Three pathologists reviewed these digital slides on a 27-inch 5K display and with the Oculus Rift after a 2-week washout period. Recorded endpoints included concordance of final diagnoses and time required to examine slides. The pathologists also rated their ease of navigation, image quality, and diagnostic confidence for both modalities. Results: There was 90 diagnostic concordance when reviewing WSI using a 5K display and Oculus Rift. The time required to examine digital pathology slides on the 5K display averaged 39 s (range 10-120 s), compared to 62 s with the Oculus Rift (range 15-270 s). All pathologists confirmed that digital pathology slides were easily viewable in a VR environment. The ratings for image quality and diagnostic confidence were higher when using the 5K display. Conclusion: Using the Oculus Rift DK2 to view and navigate pathology whole slide images in a virtual environment is feasible for diagnostic purposes. However, image resolution using the Oculus Rift device was limited. Interactive VR technologies such as the Oculus Rift are novel tools that may be of use in digital pathology. 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. Abstract With the rise in whole slide scanner technology, large numbers of tissue slides are being scanned and represented and archived digitally. While digital pathology has substantial implications for telepathology, second opinions, and education there are also huge research opportunities in image computing with this new source of “big data”. It is well known that there is fundamental prognostic data embedded in pathology images. The ability to mine “sub-visual” image features from digital pathology slide images, features that may not be visually discernible by a pathologist, offers the opportunity for better quantitative modeling of disease appearance and hence possibly improved prediction of disease aggressiveness and patient outcome. However the compelling opportunities in precision medicine offered by big digital pathology data come with their own set of computational challenges. Image analysis and computer assisted detection and diagnosis tools previously developed in the context of radiographic images are woefully inadequate to deal with the data density in high resolution digitized whole slide images. Additionally there has been recent substantial interest in combining and fusing radiologic imaging and proteomics and genomics based measurements with features extracted from digital pathology images for better prognostic prediction of disease aggressiveness and patient outcome. Again there is a paucity of powerful tools for combining disease specific features that manifest across multiple different length scales. The purpose of this review is to discuss developments in computational image analysis tools for predictive modeling of digital pathology images from a detection, segmentation, feature extraction, and tissue classification perspective. We discuss the emergence of new handcrafted feature approaches for improved predictive modeling of tissue appearance and also review the emergence of deep learning schemes for both object detection and tissue classification. We also briefly review some of the state of the art in fusion of radiology and pathology images and also combining digital pathology derived image measurements with molecular “omics” features for better predictive modeling. The review ends with a brief discussion of some of the technical and computational challenges to be overcome and reflects on future opportunities for the quantitation of histopathology. Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4 [95 CI, 64.3 -80.4 ]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95 CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.