As outlined in the recent blog article by Dr. Ralf Huss, CMO of Definiens, we believe in big data and machine learning to significantly influence decision making in medicine. Already in digital histopathology, machine learning is a key component, for example, for the detection of regions of interest (e.g., tumor metastasis regions, stroma regions) or for the detection, segmentation and classification of objects of interest (e.g., nuclei, cells, mitosis, glomeruli and glands). The use of machine learning in digital pathology is motivated by the complexity and variety of the problems, and it has been enabled only recently by the availability of large amounts of raw data (e.g., the public TCGA database from the NIH) and of efficient algorithms or increased computing power. However, while big data is getting even bigger by the minute, we cannot accept algorithms without a medical plausibility check and robust clinical validation.Read More
Machine Learning in Digital Pathology: A Journey from Handcrafted Feature Descriptors to Deep Learning Approaches
Path to personalised medicine
Pathology is one of the main driving forces behind personalised or precision medicine. In fact it has always striven towards the accurate diagnosis and prognosis of a patient’s disease through the observation of tissue architecture under the microscope. Through the application of international staging guidelines, such as the Tissue, Node, Metastasis (TNM) system in the majority of cancers, pathologists are very good at predicting prognosis at the population scale but not so good at predicting a prognosis for the individual patient. For example, if a patient presents with stage II colorectal cancer (CRC) they are predicted to have 20-30% chance of succumbing to their disease. However, it is currently difficult to accurately identify if an individual patient will be within that 20-30% group.Read More
Pathology has established itself as a decisive diagnostic discipline in precision medicine and in particular in oncology. This brings new expectations for the accuracy and reproducibility of tissue-based diagnosis, in particular, when quantification of clinically meaningful histomorphological features (“tissue phenes”) is essential to better predict patient outcome and response to treatment. Until recently traditional pathologic diagnosis has been regarded as the ground truth, a concept which is no longer sustainable in contemporary tissue-based biomarker research and clinical utility, particular with the emergence of innovative immunotherapies and their combinations.Read More
This year’s Pathology Visions Conference was held at the Westin Waterfront in Boston, MA, USA from October 11 to 13. In three parallel tracks, a broad range of topics related to digital pathology was covered. Approximately 270 attendees had the choice between cutting-edge presentations and workshops focusing on clinical applications, education and research, and image analysis.Read More
With the new releases of Tissue Studio® 4.0, Developer XD 2.3, and Image Miner® 2.3, Definiens is providing you with an updated suite of leading image analysis software. The new updates contain improvements to help address your changing tissue analysis performance needs through customizable configurations and add-ons for interactive data review, quality control, and data mining.
Tissue Studio® 4.0
If you are a scientist performing research and discovery and preclinical studies, Tissue Studio® opens up new dimensions in your tissue quantification. By providing morphological fingerprints and biomarker expression profiles on a cell-by-cell basis, Tissue Studio® enables you to achieve comprehensive and consistent data from any tissue-based assay. This latest version of Tissue Studio® also enables you to... use any chromogen for single or dual stain immunohistochemistry (IHC). If you have a need for tissue analysis where various cell types and cell subtypes need to be identified with respect to one another, you can easily utilize any two IHC stains of your choice to detect and quantify different cell populations.