Radiologist's vision check no. 2.
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Eye Movements↗
Biomedical subjects
Calvin F Nodine
Publications and source records attributed to Calvin F Nodine.
The perception of breast cancers--a spatial frequency analysis of what differentiates missed from reported cancers.
The primary detector of breast cancer is the human eye. Radiologists read mammograms by mapping exogenous and endogenous factors, which are based on the image and observer, respectively, into observer-based decisions. These decisions rely on an internal schema that contains a representation of possible malignant and benign findings. Thus, to understand the hits and misses made by the radiologists, it is important to model the interactions between the measurable image-based elements contained in the mammogram and the decisions made. The image-based elements can be of two types, i.e., areas that attracted the visual attention of the radiologist, but did not yield a report, and areas where the radiologist indicated the presence of an abnormal finding. In this way, overt and covert decisions are made when reading a mammogram. In order to model this decision-making process, we use a system that is based upon the processing done by the human visual system, which decomposes the areas under scrutiny in elements of different sizes and orientations. In our system, this decomposition is done using wavelet packets (WPs). Nonlinear features are then extracted from the WP coefficients, and an artificial neural network is trained to recognize the patterns of decisions made by each radiologist. Afterwards, the system is used to predict how the radiologist will respond to visually selected areas in new mammogram cases.
Artificial Intelligence↗
The perception of breast cancer: what differentiates missed from reported cancers in mammography?
RATIONALE AND OBJECTIVES: Mammographers map endogenous and exogenous factors into decisions whether to report the presence of a malignant finding in a mammogram case. Thus, to understand how image-based elements are translated into observer-based decisions, the authors used spatial frequency analysis to model the areas on mammograms that attracted visual attention, in addition to the areas localized as abnormal. MATERIALS AND METHODS: Four mammographers read 40 two-view mammogram cases, of which 30 contained at least one malignant lesion visible on one or two views. Their eye positions were recorded during visual search. Once the mammographer felt confident enough to provide an initial impression of the case ("normal" or "abnormal"), the eye position monitoring was turned off and the mammographer indicated, with a mouse-controlled cursor, the location and nature of any malignant findings. Regions that elicited an overt or a covert response by the mammographers were extracted for processing by means of wavelet packets and artificial neural networks. RESULTS: Different decision outcomes yielded different energy representations, in the spatial frequency domain. These energy representations were used by an artificial neural network to predict decision outcome in areas of interest, derived from eye position analysis, on mammograms from new cases. Individual trends were observed for each mammographer. CONCLUSION: Spatial frequency representation of regions that attracted a given mammographer's visual attention may be useful for characterizing how that mammographer will respond to the visually selected areas.
Analysis of Variance↗
Time course of perception and decision making during mammographic interpretation.
OBJECTIVE: This article describes the time course of lesion detection on digital mammograms using data about both eye position and decision time to compare performance between experienced mammographers and trainees. Research indicates that a longer decision time works against performance in the interpretation of chest radiographs because the likelihood of error is increased, particularly for trainees. Is this relation between decision time and performance also true for interpreting mammograms? Is there an optimal decision time-performance trade-off for detecting breast lesions? MATERIALS AND METHODS: Six radiology trainees (experience, 302-976 cases) and three mammographers (experience, 3000-5000 cases per year) reviewed 40 test cases. Each test case was represented by two mammograms that showed different views of the same breast. Twenty breasts contained suspicious lesions, and 20 were lesion-free. An interactive computer display system with an eye-head tracker measured the timing of decisions, where visual attention was directed, and how much time was spent fixating on a region of interest for each decision. Eye position was monitored during an initial-decision phase, and decision times were measured throughout a final-decision phase during which suspicious lesions recognized initially were interpreted and localized. Performance was analyzed using localization receiver operating characteristic curves. RESULTS: The time course of interpreting mammograms is similar to that for interpreting chest radiographs. Mammographers detected 71% of the true lesions within 25 sec, and trainees detected 46% within 40 sec. Both a fixation dwell time of 1000 msec and a high level of confidence in the decision were associated with the detection of true lesions for the mammographers but not for the trainees. CONCLUSION: Mammographers detected most breast lesions by global recognition within 25 sec, but trainees took more time. Prolonging one's search beyond the global recognition phase yielded few new lesions and increased the risk of error.
Breast Neoplasms↗