The Digital Slide Archive: A Software Platform for Management, Integration, and Analysis of Histology for Cancer Research

Abstract

Tissue based cancer studies can generate large amounts of histology data in the form of glass slides. These slides contain important diagnostic, prognostic, and biological information, and can be digitized into expansive and high-resolution whole-slide images (WSI) using slide-scanning devices. Effectively utilizing digital pathology data in cancer research requires the ability to manage, visualize, share and perform quantitative analysis on these large amounts of image data, tasks that are often complex and difficult for investigators with the current state of commercial digital pathology software. In this paper we describe the Digital Slide Archive (DSA), an open source web-based platform for digital pathology. DSA allows investigators to manage large collections of histologic images and integrate them with clinical and genomic metadata. The open-source model enables DSA to be extended to provide additional capabilities.

Online narrative guides for illuminating tissue atlas data and digital pathology images

“…These biopsies are obtained to advance scientific knowledge rather than inform the treatment of a specific patient and there is an ethical obligation for the resulting data (appropriately anonymized) to be made available in the most open and useful form possible for scientific discovery 29 . This principle is widely recognized in genomics 30,31 but has only recently been addressed in the area of tissue imaging 32 . More generally, digital pathology and tissue imaging are disciplines in which the goal of making research Findable, Accessible, Interoperable, and Reusable (FAIR) 33 is highly relevant, but the necessary computational infrastructure is deficient.…”

The Emergence of Pathomics

“…Gutman et al [50] implemented a software system for management and analysis of pathology images by using Girder [51] as the backend database management system. Martel et al [52] developed a desktop application, PIIP (Pathology Image Informatics Platform), which extends a freely available tool, Sedeen, for the management, annotation, and viewing of pathology image data.…”

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

“…Bottom row: mammography, and contrast-enhanced X-ray CT, ultrasound, digital radiography, magnetic resonance imaging (MRI), and PET plus single-photon emission computed tomography (SPECT). (All images from the The Cancer Imaging Archive (TCIA) [2] except the microscopy [3] and ultrasound [4]. )…”

“…The first is C N , which is the space of finite-length lists of N complex numbers. 3 Generally, we use elements of this space to represent measurements or coefficients. The use of complex-valued vectors is mathematically convenient, but can be a source of frustration during implementation-on one hand, some programming languages do not natively handle complex values, one the other, some papers specify algorithms only for real values.…”

“…Throughout the text, we use bold font for these finite-length vectors and matrices to emphasize that they are concrete objects that we can store and use in computations.2 but not all, as we will discuss in Section 53 In an engineering context, the term vector usually refers to an element of C N or R N ; in the following, we have always tried to specify vector spaces explicitly to avoid ambiguity.…”