Scanners and drillers: Characterizing expert visual search through volumetric images

Abstract

Modern imaging methods like computed tomography (CT) generate 3-D volumes of image data. How do radiologists search through such images? Are certain strategies more efficient? Although there is a large literature devoted to understanding search in 2-D, relatively little is known about search in volumetric space. In recent years, with the ever-increasing popularity of volumetric medical imaging, this question has taken on increased importance as we try to understand, and ultimately reduce, errors in diagnostic radiology. In the current study, we asked 24 radiologists to search chest CTs for lung nodules that could indicate lung cancer. To search, radiologists scrolled up and down through a "stack" of 2-D chest CT "slices." At each moment, we tracked eye movements in the 2-D image plane and coregistered eye position with the current slice. We used these data to create a 3-D representation of the eye movements through the image volume. Radiologists tended to follow one of two dominant search strategies: "drilling" and "scanning." Drillers restrict eye movements to a small region of the lung while quickly scrolling through depth. Scanners move more slowly through depth and search an entire level of the lung before moving on to the next level in depth. Driller performance was superior to the scanners on a variety of metrics, including lung nodule detection rate, percentage of the lung covered, and the percentage of search errors where a nodule was never fixated.

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