Validating automatic semantic annotation of anatomy in DICOM CT images

Sayan D. Pathak; Antonio Criminisi; Jamie Shotton; Steve White; Duncan Robertson; Bobbi Sparks; Indeera Munasinghe; Khan Siddiqui

doi:10.1117/12.878355

25 February 2011 Validating automatic semantic annotation of anatomy in DICOM CT images

Sayan D. Pathak, Antonio Criminisi, Jamie Shotton, Steve White, Duncan Robertson, Bobbi Sparks, Indeera Munasinghe, Khan Siddiqui

Author Affiliations +

Proceedings Volume 7967, Medical Imaging 2011: Advanced PACS-based Imaging Informatics and Therapeutic Applications; 796704 (2011) https://doi.org/10.1117/12.878355
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

In the current health-care environment, the time available for physicians to browse patients' scans is shrinking due to the rapid increase in the sheer number of images. This is further aggravated by mounting pressure to become more productive in the face of decreasing reimbursement. Hence, there is an urgent need to deliver technology which enables faster and effortless navigation through sub-volume image visualizations. Annotating image regions with semantic labels such as those derived from the RADLEX ontology can vastly enhance image navigation and sub-volume visualization. This paper uses random regression forests for efficient, automatic detection and localization of anatomical structures within DICOM 3D CT scans. A regression forest is a collection of decision trees which are trained to achieve direct mapping from voxels to organ location and size in a single pass. This paper focuses on comparing automated labeling with expert-annotated ground-truth results on a database of 50 highly variable CT scans. Initial investigations show that regression forest derived localization errors are smaller and more robust than those achieved by state-of-the-art global registration approaches. The simplicity of the algorithm's context-rich visual features yield typical runtimes of less than 10 seconds for a 512³ voxel DICOM CT series on a single-threaded, single-core machine running multiple trees; each tree taking less than a second. Furthermore, qualitative evaluation demonstrates that using the detected organs' locations as index into the image volume improves the efficiency of the navigational workflow in all the CT studies.

Citation Download Citation

Sayan D. Pathak, Antonio Criminisi, Jamie Shotton, Steve White, Duncan Robertson, Bobbi Sparks, Indeera Munasinghe, and Khan Siddiqui "Validating automatic semantic annotation of anatomy in DICOM CT images", Proc. SPIE 7967, Medical Imaging 2011: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 796704 (25 February 2011); https://doi.org/10.1117/12.878355

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