Whole Slide Imaging (WSI) has become an important topic during the last decade. Even though significant progress in both medical image processing and computational resources has been achieved, there are still problems in WSI that need to be solved. A major challenge is the scan size. The dimensions of digitized tissue samples may exceed 100,000 by 100,000 pixels causing memory and efficiency obstacles for real-time processing. The main contribution of this work is representing a WSI by selecting a small number of patches for algorithmic processing (e.g., indexing and search). As a result, we reduced the search time and storage by various factors between ( @math ), while losing only a few percentages in the patch retrieval accuracy. A self-organizing map (SOM) has been applied on local binary patterns (LBP) and deep features of the KimiaPath24 dataset in order to cluster patches that share the same characteristics. We used a Gaussian mixture model (GMM) to represent each class with a rather small ( @math ) portion of patches. The results showed that LBP features can outperform deep features. By selecting only @math of all patches after SOM clustering and GMM patch selection, we received @math accuracy for retrieval of the best match, while the maximum accuracy (using all patches) was @math .
Tissue examination under a microscope reveals important information to render accurate diagnosis and thus, provide effective treatment for different diseases @cite_19 . DP offers several opportunities and also presents challenges to the image processing architectures @cite_9 . Presently, only a small fraction of glass slides are digitized @cite_5 , but even if WSI was more widely available, there are a number of technical issues that would need to be addressed for their effective usage. One of the main challenges is data management and storage @cite_19 . Most importantly, the large dimensions of the WSI files require a large amount of memory and a expensive computational power.
@cite_19 @cite_9 @cite_5
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Digital whole slide imaging (WSI) is an emerging technology for pathology interpretation; however, little is known about pathologists’ practice patterns or perceptions regarding WSI. A national sample (N = 252) of pathologists from New Hampshire, Vermont, Washington, Oregon, Arizona, Alaska, Maine, and Minnesota were surveyed in this cross-sectional study (2011–2013). The survey included questions on pathologists’ experience, WSI practice patterns, and perceptions using a six-point Likert scale. Agreement was summarized with descriptive statistics to characterize pathologists’ use and perceptions of WSI. The majority of participating pathologists were males (63 ) between 40 and 59 years of age (70 ) and not affiliated with an academic medical center (72 ). Experience with WSI was reported by 49 . Types of use reported included CME board exams teaching (28 ), tumor board clinical conference (22 ), archival purposes (6 ), consultative diagnosis (4 ), research (4 ), and other uses (12 ). Most respondents (79 ) agreed that accurate diagnoses can be made with this technology, and that WSI is useful for obtaining a second opinion (88 ). However, 78 of pathologists agreed that digital slides are too slow for routine clinical interpretation. Fifty-nine percent agreed that the benefits of WSI outweigh concerns. The respondents were equally split as to whether they would like to adopt WSI (51 ) or not (49 ). About half of pathologists reported experience with the WSI technology, largely for CME, licensure board exams, and teaching. Positive perceptions regarding WSI slightly outweigh negative perceptions. Understanding practice patterns with WSI as dissemination advances may facilitate concordance of perceptions with adoption of the technology. 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. 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. Introduction: In the framework of the Cognitive Microscope (MICO) project, we have set up a contest about mitosis detection in images of H and E stained slides of breast cancer for the conference ICPR 2012. Mitotic count is an important parameter for the prognosis of breast cancer. However, mitosis detection in digital histopathology is a challenging problem that needs a deeper study. Indeed, mitosis detection is difficult because mitosis are small objects with a large variety of shapes, and they can thus be easily confused with some other objects or artefacts present in the image. We added a further dimension to the contest by using two different slide scanners having different resolutions and producing red-green-blue (RGB) images, and a multi-spectral microscope producing images in 10 different spectral bands and 17 layers Z-stack. 17 teams participated in the study and the best team achieved a recall rate of 0.7 and precision of 0.89. Context: Several studies on automatic tools to process digitized slides have been reported focusing mainly on nuclei or tubule detection. Mitosis detection is a challenging problem that has not yet been addressed well in the literature. Aims: Mitotic count is an important parameter in breast cancer grading as it gives an evaluation of the aggressiveness of the tumor. However, consistency, reproducibility and agreement on mitotic count for the same slide can vary largely among pathologists. An automatic tool for this task may help for reaching a better consistency, and at the same time reducing the burden of this demanding task for the pathologists. Subjects and Methods: Professor Frederique Capron team of the pathology department at Pitie-Salpetriere Hospital in Paris, France, has selected a set of five slides of breast cancer. The slides are stained with H and E. They have been scanned by three different equipments: Aperio ScanScope XT slide scanner, Hamamatsu NanoZoomer 2.0‑HT slide scanner and 10 bands multispectral microscope. The data set is made up of 50 high power fields (HPF) coming from 5 different slides scanned at ×40 magnification. There are 10 HPFs slide. The pathologist has annotated all the mitotic cells manually. A HPF has a size of 512 µm × 512 µm (that is an area of 0.262 mm 2 , which is a surface equivalent to that of a microscope field diameter of 0.58 mm. These 50 HPFs contain a total of 326 mitotic cells on images of both scanners, and