Share Email Print

Proceedings Paper

Comparative analysis of semantic localization accuracies between adult and pediatric DICOM CT images
Author(s): Duncan Robertson; Sayan D. Pathak; Antonio Criminisi; Steve White; David Haynor; Oliver Chen; Khan Siddiqui
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Existing literature describes a variety of techniques for semantic annotation of DICOM CT images, i.e. the automatic detection and localization of anatomical structures. Semantic annotation facilitates enhanced image navigation, linkage of DICOM image content and non-image clinical data, content-based image retrieval, and image registration. A key challenge for semantic annotation algorithms is inter-patient variability. However, while the algorithms described in published literature have been shown to cope adequately with the variability in test sets comprising adult CT scans, the problem presented by the even greater variability in pediatric anatomy has received very little attention. Most existing semantic annotation algorithms can only be extended to work on scans of both adult and pediatric patients by adapting parameters heuristically in light of patient size. In contrast, our approach, which uses random regression forests ('RRF'), learns an implicit model of scale variation automatically using training data. In consequence, anatomical structures can be localized accurately in both adult and pediatric CT studies without the need for parameter adaptation or additional information about patient scale. We show how the RRF algorithm is able to learn scale invariance from a combined training set containing a mixture of pediatric and adult scans. Resulting localization accuracy for both adult and pediatric data remains comparable with that obtained using RRFs trained and tested using only adult data.

Paper Details

Date Published: 16 February 2012
PDF: 11 pages
Proc. SPIE 8319, Medical Imaging 2012: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 83190N (16 February 2012); doi: 10.1117/12.912428
Show Author Affiliations
Duncan Robertson, Microsoft Research Cambridge (United Kingdom)
Sayan D. Pathak, Microsoft Corp. (United States)
Antonio Criminisi, Microsoft Research Cambridge (United Kingdom)
Steve White, Microsoft Corp. (United States)
David Haynor, Univ. of Washington (United States)
Oliver Chen, Microsoft Corp. (United States)
Khan Siddiqui, Microsoft Corp. (United States)

Published in SPIE Proceedings Vol. 8319:
Medical Imaging 2012: Advanced PACS-based Imaging Informatics and Therapeutic Applications
William W. Boonn; Brent J. Liu, Editor(s)

© SPIE. Terms of Use
Back to Top