Open and reusable deep learning for pathology with WSInfer and QuPath.

Digital pathology has seen a proliferation of deep learning models in recent years, but many models are not readily reusable. To address this challenge, we developed WSInfer: an open-source software ecosystem designed to streamline the sharing and reuse of deep learning models for digital pathology. The increased access to trained models can augment research on the diagnostic, prognostic, and predictive capabilities of digital pathology.

ImageBox 2 - Efficient and Rapid Access of Image Tiles from Whole-Slide Images Using Serverless HTTP Range Requests.

Background: Whole-slide images (WSI) are produced by a high-resolution scanning of pathology glass slides. There are a large number of whole-slide imaging scanners, and the resulting images are frequently larger than 100,000 × 100,000 pixels which typically image 100,000 to one million cells, ranging from several hundred megabytes to many gigabytes in size.

Aims And Objectives: Provide HTTP access over the web to Whole Slide Image tiles that do not have localized tiling servers but only basic HTTP access.

PRISM: A Platform for Imaging in Precision Medicine.

Purpose: Precision medicine requires an understanding of individual variability, which can only be acquired from large data collections such as those supported by the Cancer Imaging Archive (TCIA). We have undertaken a program to extend the types of data TCIA can support. This, in turn, will enable TCIA to play a key role in precision medicine research by collecting and disseminating high-quality, state-of-the-art, quantitative imaging data that meet the evolving needs of the cancer research community.

Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer.

Quantitative assessment of spatial relations between tumor and tumor-infiltrating lymphocytes (TIL) is increasingly important in both basic science and clinical aspects of breast cancer research. We have developed and evaluated convolutional neural network analysis pipelines to generate combined maps of cancer regions and TILs in routine diagnostic breast cancer whole slide tissue images. The combined maps provide insight about the structural patterns and spatial distribution of lymphocytic infiltrates and facilitate improved quantification of TILs.

Multi-objective Parameter Auto-tuning for Tissue Image Segmentation Workflows.

We propose a software platform that integrates methods and tools for multi-objective parameter auto-tuning in tissue image segmentation workflows. The goal of our work is to provide an approach for improving the accuracy of nucleus/cell segmentation pipelines by tuning their input parameters. The shape, size, and texture features of nuclei in tissue are important biomarkers for disease prognosis, and accurate computation of these features depends on accurate delineation of boundaries of nuclei.

A Containerized Software System for Generation, Management, and Exploration of Features from Whole Slide Tissue Images.

Well-curated sets of pathology image features will be critical to clinical studies that aim to evaluate and predict treatment responses. Researchers require information synthesized across multiple biological scales, from the patient to the molecular scale, to more effectively study cancer. This article describes a suite of services and web applications that allow users to select regions of interest in whole slide tissue images, run a segmentation pipeline on the selected regions to extract nuclei and compute shape, size, intensity, and texture features, store and index images and analysis results, and visualize and explore images and computed features.

Towards Generation, Management, and Exploration of Combined Radiomics and Pathomics Datasets for Cancer Research.

Cancer is a complex multifactorial disease state and the ability to anticipate and steer treatment results will require information synthesis across multiple scales from the host to the molecular level. Radiomics and Pathomics, where image features are extracted from routine diagnostic Radiology and Pathology studies, are also evolving as valuable diagnostic and prognostic indicators in cancer. This information explosion provides new opportunities for integrated, multi-scale investigation of cancer, but also mandates a need to build systematic and integrated approaches to manage, query and mine combined Radiomics and Pathomics data.

Safe "cloudification" of large images through picker APIs.

The "Box model" allows users with no particular training in informatics, or access to specialized infrastructure, operate generic cloud computing resources through a temporary URI dereferencing mechanism known as "drop-file-picker API" ("picker API" for sort). This application programming interface (API) was popularized in the web app development community by DropBox, and is now a consumer-facing feature of all major cloud computing platforms such as Box.com, Google Drive and Amazon S3.

From EHRs to Linked Data: representing and mining encounter data for clinical expertise evaluation.

Translational science, today, involves multidisciplinary teams of scientists rather than single scientists. Teams facilitate biologically meaningful and clinically consequential breakthroughs. There are a myriad of sources of data about investigators, physicians, research resources, clinical encounters, and expertise to promote team interaction; however, much of this information is not connected and is left siloed.

MiGenes: a searchable interspecies database of mitochondrial proteins curated using gene ontology annotation.

Motivation: There has been an explosion of interest in the role of mitochondria in programmed cell death and other fundamental pathological processes underlying the development of human diseases. Nevertheless, the inventory of mitochondrial proteins encoded in the nuclear genome remains incomplete, providing an impediment to mitochondrial research at the interface with systems biology. We created the MiGenes database to further define the scope of the mitochondrial proteome in humans and model organisms including mice, rats, flies and worms as well as budding and fission yeasts.