Characterization of Radiologists’ Search Strategies for Lung Nodule Detection: Slice-Based Versus Volumetric Displays

Abstract

The goal of this study was to assess whether radiologists' search paths for lung nodule detection in chest computed tomography (CT) between different rendering and display schemes have reliable properties that can be exploited as an indicator of ergonomic efficiency for the purpose of comparing different display paradigms. Eight radiologists retrospectively viewed 30 lung cancer screening CT exams, containing a total of 91 nodules, in each of three display modes [i.e., slice-by-slice, orthogonal maximum intensity projection (MIP) and stereoscopic] for the purpose of detecting and classifying lung nodules. Radiologists' search patterns in the axial direction were recorded and analyzed along with the location, size, and shape for each detected feature, and the likelihood that the feature is an actual nodule. Nodule detection performance was analyzed by employing free-response receiver operating characteristic methods. Search paths were clearly different between sliceby-slice displays and volumetric displays but, aside from training and novelty effects, not between MIP and stereographic displays. Novelty and training effects were associated with the stereographic display mode, as evidenced by differences between the beginning and end of the study. The stereo display provided higher detection and classification performance with less interpretation time compared to other display modes tested in the study; however, the differences were not statistically significant. Our preliminary results indicate a potential role for the use of radiologists' search paths in evaluating the relative ergonomic efficiencies of different display paradigms, but systematic training and practice is necessary to eliminate training curve and novelty effects before search strategies can be meaningfully compared.

Evaluating Interaction Techniques for Stack Mode Viewing

“…In an earlier work 14 , we showed that radiologists and laypersons had similar interaction performance when expert knowledge was not required, so we hypothesized that we could use naïve laypersons to evaluate different image navigation techniques, with results that would generalize to radiologists performing clinical tasks. For example, we had observed a lot of forward and backward scrolling during a radiology look-alike task with naïve students as subjects 9 and backward and forward scrolling in stack mode by radiologists has been similarly noted by others [11][12][13] . As a final, tertiary, issue, we were interested whether a group of naïve students would have similar performance and preferences to radiologists for the three techniques.…”

“…Wang et al 11 compared three display techniques: a stereoscopic display, slice-by-slice, and maximum intensity projections (MIP), for detecting lung nodules from CT lung volumes. They found that the stereoscopic display provided higher detection and classification performance with less interpretation time but the differences were not statistically significant in their study of eight radiologists viewing a total of 91 anomalies.…”

Lung Nodule and Cancer Detection in Computed Tomography Screening

“…Viewing transverse sections on a cine display results in the detection of 31% more nodules 5 mm when compared to film-based review (42). With the advent of image review stations that allow real-time reformation and rendering of transverse sections, an innumerable range of approaches are possible for navigating and evaluating the rich volumetric lung CT data (43) which impact radiologists’ effectiveness and efficiency of search (44). …”

FluorescenceIn SituHybridization (FISH) Signal Analysis Using Automated Generated Projection Images

“…Then, the CAD scheme uses the (x, y) coordinate of the detected cell center and cell boundary in the ROI to define a volume of interest (VOI) in another image stack that records the FISH-probed chromosome signals. All FISH-probed signal spots recorded in the multiple image slices (in z direction) are mapped into cell ROI using a maximum intensity projection (MIP) method that enables to map a stack of 3-D image slices into one 2-D projection image [32]. Figure 4 illustrates an example of region of interests (ROIs) containing FISH signal spots captured in green spectrum in 9 focal planes (Fig.…”