Digital Synthesis of Lung Nodules

Rh, Sherrier; Johnson, G. Allan; Sa, Suddarth; Chiles, Caroline; Hulka, Carol A.; Ravin, CE

doi:10.1097/00004424-198512000-00008

Cited by 13 publications

(5 citation statements)

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“…An example is the work of Sherrier et al [171]. who construct simulated nodules images by identifying isolated nodules, without any rib structures nearby, and adding rotated and scaled versions of these nodule templates to new radiographs, at various locations.…”

Section: B Nodule Detectionmentioning

confidence: 99%

“…Physically there is not much wrong with this procedure since radiographs are projection images, although it could be doubted whether the distribution of size and structure of nodules does not depend on the location in the lung field, and the scatter contributions of nodules of different size and orientation would be different. Observer studies in [171] showed that radiologists could not discern between simulated and true nodule cases.…”

Section: B Nodule Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Computer-aided diagnosis in chest radiography: a survey

Ginneken

Romeny

Viergever

2001

IEEE Trans. Med. Imaging

439

214

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Section: B Nodule Detectionmentioning

confidence: 99%

Section: B Nodule Detectionmentioning

confidence: 99%

Computer-aided diagnosis in chest radiography: a survey

Ginneken

Romeny

Viergever

2001

IEEE Trans. Med. Imaging

439

214

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“…Not mentioned in Tables 2.4-2.5 are a number of observer studies that test the application of CAD schemes for nodule detection [80,175,135,167] or the detection of simulated modules [145], and a paper on synthesis of lung nodules by Sherrier et al [219]. The conclusion of the latter work is that by simply adding nodules that are not obscured by other structures to radiographs at new locations (physically there is nothing wrong with this since radiographs are projection images) it is possible to mimic nodules that cannot be differentiated from real ones by radiologists.…”

Section: Annmentioning

confidence: 99%

Computer aided diagnosis in chest radiography

Mouton¹,

Douglas

2003

Academic Radiology

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“…Studies have shown that 30% of all lung nodules can be missed on the first reading of a chest radiogram, even though the same nodules were clearly present on prior radiograms of the same patient [3], [33]. Reasons proposed for these false negatives include factors relating to the image such as the location of nodules within the chest [29], the size and number of nodules present [35], and the diagnostic quality of the images [27], [36]; as well as factors relating to the diagnostician, such as level of training [4], search versus nonsearch protocols [38], premature termination of the search [8]- [10], [31], and conservative decision criteria [26].…”

Section: B Computer-aided Diagnosismentioning

confidence: 99%

Modeling human eye behavior during mammographic scanning: preliminary results

White

Hutson²,

Hutchinson³

1997

IEEE Trans. Syst., Man, Cybern. A

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Understanding how people acquire information from pictures-radiographs, maps, charts, photographs, drawings, and other static images-can be an important component in understanding, aiding, and eventually automating a wide range of diagnostic tasks. In the experiment reported here, we investigate the use of an inexpensive and unobtrusive eye-tracking system to explore relationships between visual scanning patterns, pupillary response, and the clinical diagnoses of mammographic experts. One radiologist and three radiological technicians each examined a series of 14 mammograms for indications of abnormalities associated with breast cancer. The status of each mammogram was verified by biopsy. The eye-tracking system was used to measure and record eye position and pupil diameter as a function of time as the subjects scanned the mammograms. Three treatments were applied to the scan data to model the experts' eye behaviors. These included quantification of dwell time and pupil diameter as a function diagnostic accuracy in regions of the mammogram where abnormalities existed or were perceived; independent clustering of lookpoints without respect to abnormalities; and analysis of scan transitions between lookpoint clusters. Results of the analysis were consistent with extensive prior studies of eyescan measures recorded during the diagnosis of abnormalities on chest radiograms. This preliminary investigation provides a proof of concept for use of the eye-tracking technology, experimental protocols, and analysis methodologies as the basis for expanded mammographic studies, with the promise of eventual adaptation as a source of diagnostic information in clinical practice.

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Digital Synthesis of Lung Nodules

Cited by 13 publications

References 0 publications

Computer-aided diagnosis in chest radiography: a survey

Computer-aided diagnosis in chest radiography: a survey

Computer aided diagnosis in chest radiography

Modeling human eye behavior during mammographic scanning: preliminary results

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