Background: Whole slide images (WSIs, digitized histopathology glass slides) are large data files whose long-term storage remains a significant cost for pathology departments. Currently used WSI formats are based on lossy image compression alogrithms, either using JPEG or its more efficient successor JPEG 2000. While the advantages of the JPEG 2000 algorithm (JP2) are commonly recognized, its compression parameters have not been fully optimized for pathology WSIs. Methods: We defined an optimized parametrization for JPEG 2000 image compression, designated JP2-WSI, to be used specifically with histopathological WSIs. Our parametrization is based on allowing a very high degree of compression on the background part of the WSI while using a conventional amount of compression on the tissue-containing part of the image, resulting in high overall compression ratios. Results: When comparing the compression power of JP2-WSI to the commonly used fixed 35:1 compression ratio JPEG 2000 and the default image formats of proprietary Aperio, Hamamatsu, and 3DHISTECH scanners, JP2-WSI produced the smallest file sizes and highest overall compression ratios for all 17 slides tested. The image quality, as judged by visual inspection and peak signal-to-noise ratio (PSNR) measurements, was equal to or better than the compared image formats. The average file size by JP2-WSI amounted to 15, 9, and 16 percent, respectively, of the file sizes of the three commercial scanner vendors' proprietary file formats (3DHISTECH MRXS, Aperio SVS, and Hamamatsu NDPI). In comparison to the commonly used 35:1 compressed JPEG 2000, JP2-WSI was three times more efficient. Conclusions: JP2-WSI allows very efficient and cost-effective data compression for whole slide images without loss of image information required for histopathological diagnosis.

Background: Open source technologies and mobile innovations have radically changed the way people interact with technology. These innovations and advancements have been used across various disciplines and already have a significant impact. Microscopy, with focus on visually appealing contrasting colors for better appreciation of morphology, forms the core of the disciplines such as Pathology, microbiology, and anatomy. Here, learning happens with the aid of multi-head microscopes and digital camera systems for teaching larger groups and in organizing interactive sessions for students or faculty of other departments. Methods: The cost of the original equipment manufacturer (OEM) camera systems in bringing this useful technology at all the locations is a limiting factor. To avoid this, we have used the low-cost technologies like Raspberry Pi, Mobile high definition link and 3D printing for adapters to create portable camera systems. Results: Adopting these open source technologies enabled us to convert any binocular or trinocular microscope be connected to a projector or HD television at a fraction of the cost of the OEM camera systems with comparable quality. Conclusion: These systems, in addition to being cost-effective, have also provided the added advantage of portability, thus providing the much-needed flexibility at various teaching locations.

Background: The aim of this study is to derive and to validate a cohort of rectal cancer surgical patients within administrative datasets using text-search analysis of pathology reports. Materials and Methods: A text-search algorithm was developed and validated on pathology reports from 694 known rectal cancers, 1000 known colon cancers, and 1000 noncolorectal specimens. The algorithm was applied to all pathology reports available within the Ottawa Hospital Data Warehouse from 1996 to 2010. Identified pathology reports were validated as rectal cancer specimens through manual chart review. Sensitivity, specificity, and positive predictive value (PPV) of the text-search methodology were calculated. Results: In the derivation cohort of pathology reports (n = 2694), the text-search algorithm had a sensitivity and specificity of 100% and 98.6%, respectively. When this algorithm was applied to all pathology reports from 1996 to 2010 (n = 284,032), 5588 pathology reports were identified as consistent with rectal cancer. Medical record review determined that 4550 patients did not have rectal cancer, leaving a final cohort of 1038 rectal cancer patients. Sensitivity and specificity of the text-search algorithm were 100% and 98.4%, respectively. PPV of the algorithm was 18.6%. Conclusions: Text-search methodology is a feasible way to identify all rectal cancer surgery patients through administrative datasets with high sensitivity and specificity. However, in the presence of a low pretest probability, text-search methods must be combined with a validation method, such as manual chart review, to be a viable approach.

Background: Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visual microscopic assessment. In this study, we propose and evaluate a deep learning approach to classify high-resolution digital images of neuroblastoma histology into five different classes determined by the Shimada classification. Subjects and Methods: We apply a combination of convolutional deep belief network (CDBN) with feature encoding algorithm that automatically classifies digital images of neuroblastoma histology into five different classes. We design a three-layer CDBN to extract high-level features from neuroblastoma histological images and combine with a feature encoding model to extract features that are highly discriminative in the classification task. The extracted features are classified into five different classes using a support vector machine classifier. Data: We constructed a dataset of 1043 neuroblastoma histological images derived from Aperio scanner from 125 patients representing different classes of neuroblastoma tumors. Results: The weighted average F-measure of 86.01% was obtained from the selected high-level features, outperforming state-of-the-art methods. Conclusion: The proposed computer-aided classification system, which uses the combination of deep architecture and feature encoding to learn high-level features, is highly effective in the classification of neuroblastoma histological images.