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. 2020 Feb:11316:113160U.
doi: 10.1117/12.2549119. Epub 2020 Mar 16.

Human observer templates for lesion discrimination tasks

Craig K Abbey  1 , Frank W Samuelson  2 , Rongping Zeng  2 , John M Boone  3 , Miguel P Eckstein  1 , Kyle J Myers  2
Affiliations

Human observer templates for lesion discrimination tasks

Craig K Abbey et al. Proc SPIE Int Soc Opt Eng. 2020 Feb.

Abstract

We investigate a series of two-alternative forced-choice (2AFC) discrimination tasks based on malignant features of abnormalities in low-dose lung CT scans. A total of 3 tasks are evaluated, and these consist of a size-discrimination task, a boundary-sharpness task, and an irregular-interior task. Target and alternative signal profiles for these tasks are modulated by one of two system transfer functions and embedded in ramp-spectrum noise that has been apodized for noise control in one of 4 different ways. This gives the resulting images statistical properties that are related to weak ground-glass lesions in axial slices of low-dose lung CT images. We investigate observer performance in these tasks using a combination of statistical efficiency and classification images. We report results of 24 2AFC experiments involving the three tasks. A staircase procedure is used to find the approximate 80% correct discrimination threshold in each task, with a subsequent set of 2,000 trials at this threshold. These data are used to estimate statistical efficiency with respect to the ideal observer for each task, and to estimate the observer template using the classification-image methodology. We find efficiency varies between the different tasks with lowest efficiency in the boundary-sharpness task, and highest efficiency in the non-uniform interior task. All three tasks produce clearly visible patterns of positive and negative weighting in the classification images. The spatial frequency plots of classification images show how apodization results in larger weights at higher spatial frequencies.

Keywords: Classification Images; discrimination tasks; low-dose CT; observer performance.

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Figures

Figure 1.
Figure 1.. System properties.
The simulated MTFs for System 1 (A) and System 2 (B) are shown, along with plots of the noise power spectrum (C and D). The Legend applies to all plots.
Figure 2.
Figure 2.. Noise Textures.
The different levels of simulated apodization lead to different noise textures in the simulated imaging systems. Higher levels of apodization lead to a smoother and less grainy texture.
Figure 3.
Figure 3.. Task Profiles.
Radial plots of the “Malignant” and “Benign” profiles are shown for each of the three tasks considered. In Task 1 (A), the feature of interest is the lesion size. In Task 2 (B), the feature of interest is an indistinct or unsharp boundary. In Task 3 (C), the feature of interest is a nonuniform lesion interior.
Figure 4.
Figure 4.. Task object spectra.
Radial plots of the difference signal spectrum for each task are shown. The Nyquist frequency of the final image is shown for reference.
Figure 5.
Figure 5.. Stimulus Profiles and Images.
The (noiseless) signal and alternative profiles for each of the three tasks are shown, along with the difference signal and sample image patches from each class (System 1, Apodization Level 3). Each task parameter has been exaggerated for the purpose of display in this figure. The image patches represent 21.2mm of a 350mm simulated field of view. All the images have a window of 1500 HU and level of −650 HU except for the difference images which are scaled to the max difference value.
Figure 6.
Figure 6.. Characterization of performance.
The plots show performance for each of the three tasks, both imaging systems, and the four levels of apodization (A1 – A4). The PC plot (top) shows that the training process does not necessarily result in an observed PC of 80%. The thresholds and efficiency plots show variability between the tasks and some evidence of better performance with increasing apodization.
Figure 7.
Figure 7.. Classification Images.
The average classification image across readers is shown for each of the 24 experimental conditions (3 Tasks, 2 Systems, and 4 Apodizations). These display patches (21.2mm) have been spatially windowed to radius of 10mm (HWHM), and frequency windowed to 0.4cyc/mm.
Figure 8.
Figure 8.. Classification Image Spectra.
Average spatial-frequency weights of the classification images are plotted as a function of the average radial frequency. Each plot shows the average radial weights associated with the 4 levels of apodization (A1-A4) in the simulated imaging systems. The legend in the upper left applies to all plots.
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