There are a few websites online that talk about different aspects of digital pathology. An important one being Pathology News.
Pathology News is delighted to announce that Micropix, experts in Optical Microscopy and Digital Pathology, are now included in our Technology Buyers' Guide with a detailed company showcase and product page
Rare diseases are often difficult to diagnose and predicting the best course of treatment can be challenging for clinicians. Modern electronic databases can store an immense amount of digital records and reference images, particularly in pathology through whole slide images (WSIs). However, the gigapixel size of each individual WSI and the ever-increasing number of images in large repositories, means that search and retrieval of WSIs can be slow and complicated. As a result, scalability remains a pertinent roadblock for efficient use. To solve this issue, researchers have now developed a deep learning algorithm that can teach itself to learn features which can then be used to find similar cases in large pathology image repositories. Known as SISH (Self-Supervised Image search for Histology), the new tool developed by investigators at Brigham and Women’s Hospital (Boston, MA, USA) acts like a search engine for pathology images and has many potential applications, including identifying rare diseases and helping clinicians determine which patients are likely to respond to similar therapies. The algorithm teaches itself to learn feature representations which can be used to find cases with analogous features in pathology at a constant speed regardless of the size of the database. In their study, the researchers tested the speed and ability of SISH to retrieve interpretable disease subtype information for common and rare cancers. The algorithm successfully retrieved images with speed and accuracy from a database of tens of thousands of whole slide images from over 22,000 patient cases, with over 50 different disease types and over a dozen anatomical sites. The speed of retrieval outperformed other methods in many scenarios, including disease subtype retrieval, particularly as the image database size scaled into the thousands of images. Even while the repositories expanded in size, SISH was still able to maintain a constant search speed. The self-teaching algorithm, however, has some limitations including a large memory requirement, limited context awareness within large tissue slides and the fact that it is limited to a single imaging modality. Overall, the algorithm demonstrated the ability to efficiently retrieve images independent of repository size and in diverse datasets. It also demonstrated proficiency in diagnosis of rare disease types and the ability to serve as a search engine to recognize certain regions of images that may be relevant for diagnosis. This work may greatly inform future disease diagnosis, prognosis, and analysis. “We show that our system can assist with the diagnosis of rare diseases and find cases with similar morphologic patterns without the need for manual annotations, and large datasets for supervised training,” said senior author Faisal Mahmood, PhD, in the Brigham’s Department of Pathology. “This system has the potential to improve pathology training, disease subtyping, tumor identification, and rare morphology identification.” “As the sizes of image databases continue to grow, we hope that SISH will be useful in making identification of diseases easier,” added Mahmood. “We believe one important future direction in this area is multimodal case retrieval which involves jointly using pathology, radiology, genomic and electronic medical record data to find similar patient cases.” Related Links:Brigham and Women’s Hospital
MSKCC has been using digital pathology since 2006, with over 5.3 million slides in its archive. However, digitizing glass slides from external institutions is challenging due to inconsistent staining and preparation methods, requiring manual review and extra processing time. To address this, MSKCC evaluated Pramana’s two-head scanning system to assess its efficiency in handling challenging slides. [...]
This webinar will demonstrate HALO’s ease-of-use and scalability, powerful analytic capabilities, and will provide a tour of select HALO modules for research areas as diverse as immuno-oncology, neuroscience, metabolism, and infectious disease. Dr. Anne Hellebust, Director of Product will also present an overview of how easily HALO AI™ integrates with HALO to perform tasks such as tissue classification, nuclear segmentation, and cell phenotyping.
Often those pathologists and scientists who are thinking of starting their journey with digital pathology are intimidated by the workflow changes and investment they will have to make in order to get started. The cost of the equipment and the challenges related to workflow re-design are often a significant barrier for adoption.
Traditional approach to peer review for non-clinical studies involving a second person review of the slide, data, and interpretation of the slide conducted by a peer has its own downsides that are eliminated by organizations that have long embraced digital pathology. For instance, a sponsor pharma company working with a CRO to conduct peer reviews using glass slides meant that either slides had to be shipped to the pathologist conducting peer review or the pathologists had to travel to the site to complete this evaluation. A digital peer review using digital pathology solution on the other hand enables organizations to streamline this process and improve efficiency.
by Christos Evangelou, MSc, PhD – Medical Writer and Editor. Breast cancer is the most common cancer in women, causing more than half a million deaths every year worldwide. The expression levels of biomarkers can help clinicians to determine the appropriate treatment method for each patient. Immunohistochemistry (IHC) alone or in combination with in situ hybridization (ISH) [...]
Ultivue, Inc. an industry leader in multiplexing tools and novel image analysis solutions for tissue biomarker studies and, the Translational Research Unit (TRU) platform of University of Bern, that develops novel workflows for tissue microarray (ngTMA®) construction, announce a collaborative agreement to promote multiplexed immunofluorescence (mIF) assays and the use of next generation TMAs to unlock spatial analysis in scientific research and improve the quality of TMAs integrated in clinical and translational research.Ultivue develops unique solutions for use in mIF applications, imaging, and spatial phenomics. Its proprietary InSituPlex® technology enabling improved signal to noise data is designed for fast and comprehensive exploration of biologically relevant targets, up to 12-plex, with same slide-H&E analysis in tissue samples combines the power of computational pathology & spatial biology to guide translational science in immuno-oncology.ngTMA® supports research collaborators with an interdisciplinary approach to studying tumor biology and disease states. Services include scientific consulting and TMA design, digital slide annotation, automated tissue punching and documentation for quality control. The ngTMA® platform is embedded in an innovative research framework and as part of the Translational Research Unit (TRU), Institute of Pathology, University of Bern, with expertise in tissue-related technologies.“We are thrilled to be partnering with ngTMA® to jointly empower biopharma customers with tissue multiplex services. In addition, our scientific collaboration will investigate the relevance of the tumor microenvironment by characterizing clinically well-annotated cohorts using Ultivue reagents at ngTMA,” said Florian Leiss, Ph.D. Vice President Product Strategy & Corporate Development at Ultivue.“We are very excited to join forces with the Ultivue team and have their reagents in our pipeline in order to support translational research of our collaborators,” Said Paulina Brönnimann, PhD, Head of Translational Research Unit.About the Translational Research Unit and ngTMA®TRU is a core facility at the Institute of Pathology at the University of Bern, providing human and animal tissue-related services with expertise in histology, tissue visualization, digital pathology, image analysis and construction of next-generation Tissue Microarrays (learn more at https://www.ngtma.com/).About UltivueUltivue provides researchers and scientists in translational medicine with multiplex biomarker assays for tissue phenotyping and digital pathology. Its proprietary InSituPlex® technology enables advanced exploration and interrogation of tissue samples for precision medicine research. These highly customizable solutions coupled with our scientific consultative approach strengthen and accelerate biomarker discovery and drug development programs. Learn more at Ultivue.com.
Chaith Kondragunta, started working on neural network applications as an engineer back in the days when the computing power to fully utilize them was not available yet. This research field had to wait for the technology to catch up with the theoretical concepts. When this was achieved, Chaith harnessed deep learning for data analytics in the financial sector, but always knew, that to make a real difference it should be implemented in health care and medical sciences. This opportunity came in 2018 when he became the CEO of Aira Matrix – an image analysis company applying deep learning to pathology images.
