GridIMAGE: a novel use of grid computing to support interactive human and computer-assisted detection decision support

Abstract

This paper describes a Grid-aware image reviewing system (GridIMAGE) that allows practitioners to (a) select images from multiple geographically distributed digital imaging and communication in medicine (DICOM) servers, (b) send those images to a specified group of human readers and computer-assisted detection (CAD) algorithms, and (c) obtain and compare interpretations from human readers and CAD algorithms. The currently implemented system was developed using the National Cancer Institute caGrid infrastructure and is designed to support the identification of lung nodules on thoracic computed tomography. However, the infrastructure is general and can support any type of distributed review. caGrid data and analytical services are used to link DICOM image databases and CAD systems and to interact with human readers. Moreover, the service-oriented and distributed structure of the GridIMAGE framework enables a flexible system, which can be deployed in an institution (linking multiple DICOM servers and CAD algorithms) and in a Grid environment (linking the resources of collaborating research groups). GridIMAGE provides a framework that allows practitioners to obtain interpretations from one or more human readers or CAD algorithms. It also provides a mechanism to allow cooperative imaging groups to systematically perform image interpretation tasks associated with research protocols.

Fig 1

Schematic representation of GridIMAGE showing the sequence of interaction among different subsystems. The data management subsystem stores and manages image data sets and analysis results. The CAD subsystem enables access to distributed CAD applications. The human mark-up subsystem allows review and analysis of image data by humans. A suite of GUIs enables clients to log on to the environment, interact with services in each subsystem, submit requests for data query, apply CAD processing, provide human mark-up, and review the analysis results. Steps 1 through 5 show the sequence of interactions when submitting images for CAD processing and reviewing results. Steps 2′ through 5′ show the sequence of interactions when marking the images.

Fig 2.

The main structure of the schema for the DICOM data object (additional attributes are not shown).

Fig 3.

Work-order schema.

Fig 4.

Schema for nodule-detection results.

Fig 5.

The user interface for human mark-up.

Fig 6.

The browser user interface.