Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

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Implementing the DICOM Standard for Digital Pathology

1MGH and BWH Center for Clinical Data Science, Boston, MA, USA.
2Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
3PixelMed Publishing, LLC, Bangor, PA, USA.
4Isomics, Inc., Cambridge, MA, USA.
5Harvard Medical School, Boston, MA, USA.
6Enterprise Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
7Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Publication Date:
J Pathol Inform.
Volume Number:
Issue Number:
J Pathol Inform. 2018 Nov 2;9:37.
PubMed ID:
Computational pathology, DICOMweb, image compression, slide scanning, whole slide imaging
Appears in Collections:
R01 CA225655/CA/NCI NIH HHS/United States
U24 CA180918/CA/NCI NIH HHS/United States
P41 EB015898/EB/NIBIB NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
U24 CA199460/CA/NCI NIH HHS/United States
Generated Citation:
Herrmann M., Clunie D.A., Fedorov A., Doyle S.W., Pieper S., Klepeis V., Le L.P., Mutter G.L., Milstone D.S., Schultz T.J., Kikinis R., Kotecha G.K., Hwang D.H., Andriole K.P., Iafrate A.J., Brink J.A., Boland G.W., Dreyer K.J., Michalski M., Golden J.A., Louis D.N., Lennerz J.K. Implementing the DICOM Standard for Digital Pathology. J Pathol Inform. 2018 Nov 2;9:37. PMID: 30533276. PMCID: PMC6236926.
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Digital Imaging and Communications in Medicine (DICOM®) is the standard for the representation, storage, and communication of medical images and related information. A DICOM file format and communication protocol for pathology have been defined; however, adoption by vendors and in the field is pending. Here, we implemented the essential aspects of the standard and assessed its capabilities and limitations in a multisite, multivendor healthcare network. Methods: We selected relevant DICOM attributes, developed a program that extracts pixel data and pixel-related metadata, integrated patient and specimen-related metadata, populated and encoded DICOM attributes, and stored DICOM files. We generated the files using image data from four vendor‑specific image file formats and clinical metadata from two departments with different laboratory information systems. We validated the generated DICOM files using recognized DICOM validation tools and measured encoding, storage, and access efficiency for three image compression methods. Finally, we evaluated storing, querying, and retrieving data over the web using existing DICOM archive software. Results: Whole slide image data can be encoded together with relevant patient and specimen‑related metadata as DICOM objects. These objects can be accessed efficiently from files or through RESTful web services using existing software implementations. Performance measurements show that the choice of image compression method has a major impact on data access efficiency. For lossy compression, JPEG achieves the fastest compression/decompression rates. For lossless compression, JPEG‑LS significantly outperforms JPEG 2000 with respect to data encoding and decoding speed. Conclusion: Implementation of DICOM allows efficient access to image data as well as associated metadata. By leveraging a wealth of existing infrastructure solutions, the use of DICOM facilitates enterprise integration and data exchange for digital pathology.