Automatic thoracic body region localization.

Proceedings of SPIE
PeiRui BaiDrew A Torigian

Abstract

Radiological imaging and image interpretation for clinical decision making are mostly specific to each body region such as head & neck, thorax, abdomen, pelvis, and extremities. For automating image analysis and consistency of results, standardizing definitions of body regions and the various anatomic objects, tissue regions, and zones in them becomes essential. Assuming that a standardized definition of body regions is available, a fundamental early step needed in automated image and object analytics is to automatically trim the given image stack into image volumes exactly satisfying the body region definition. This paper presents a solution to this problem based on the concept of virtual landmarks and evaluates it on whole-body positron emission tomography/computed tomography (PET/CT) scans. The method first selects a (set of) reference object(s), segments it (them) roughly, and identifies virtual landmarks for the object(s). The geometric relationship between these landmarks and the boundary locations of body regions in the cranio-caudal direction is then learned through a neural network regressor, and the locations are predicted. Based on low-dose unenhanced CT images of 180 near whole-body PET/CT scans (which includes 34 w...Continue Reading

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