Digital Pathology: Data-Intensive Frontier in Medical Imaging

• Published in: Proceedings of the IEEE Vol. 100; no. 4; pp. 991 - 1003
• Main Authors: Cooper, Lee A. D., Sharma, Ashish, Kurc, Tahsin M., Brat, Daniel J., Saltz, Joel H., Carter, Alexis B., Farris, Alton B., Wang, Fusheng, Kong, Jun, Gutman, David A., Widener, Patrick, Pan, Tony C., Cholleti, Sharath R.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2012; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bioinformatics; Biomedical imaging; biomedical informatics; Diagnostic systems; Digital; digital pathology; Diseases; Image analysis; Image segmentation; Medical; Medical image processing; Microscopy; Pathology; Patients; Streamlining; Virtual environments; virtual microscopy
• ISSN: 0018-9219; 1558-2256
• DOI: 10.1109/JPROC.2011.2182074

Pathology is a medical subspecialty that practices the diagnosis of disease. Microscopic examination of tissue reveals information enabling the pathologist to render accurate diagnoses and to guide therapy. The basic process by which anatomic pathologists render diagnoses has remained relatively unchanged over the last century, yet advances in information technology now offer significant opportunities in image-based diagnostic and research applications. Pathology has lagged behind other healthcare practices such as radiology where digital adoption is widespread. As devices that generate whole slide images become more practical and affordable, practices will increasingly adopt this technology and eventually produce an explosion of data that will quickly eclipse the already vast quantities of radiology imaging data. These advances are accompanied by significant challenges for data management and storage, but they also introduce new opportunities to improve patient care by streamlining and standardizing diagnostic approaches and uncovering disease mechanisms. Computer-based image analysis is already available in commercial diagnostic systems, but further advances in image analysis algorithms are warranted in order to fully realize the benefits of digital pathology in medical discovery and patient care. In coming decades, pathology image analysis will extend beyond the streamlining of diagnostic workflows and minimizing interobserver variability and will begin to provide diagnostic assistance, identify therapeutic targets, and predict patient outcomes and therapeutic responses.