Literature DB >> 25325324

Characterizing search, recognition, and decision in the detection of lung nodules on CT scans: elucidation with eye tracking.

Geoffrey D Rubin1, Justus E Roos, Martin Tall, Brian Harrawood, Sukantadev Bag, Donald L Ly, Danielle M Seaman, Lynne M Hurwitz, Sandy Napel, Kingshuk Roy Choudhury.   

Abstract

PURPOSE: To determine the effectiveness of radiologists' search, recognition, and acceptance of lung nodules on computed tomographic (CT) images by using eye tracking.
MATERIALS AND METHODS: This study was performed with a protocol approved by the institutional review board. All study subjects provided informed consent, and all private health information was protected in accordance with HIPAA. A remote eye tracker was used to record time-varying gaze paths while 13 radiologists interpreted 40 lung CT images with an average of 3.9 synthetic nodules (5-mm diameter) embedded randomly in the lung parenchyma. The radiologists' gaze volumes ( GV gaze volume s) were defined as the portion of the lung parenchyma within 50 pixels (approximately 3 cm) of all gaze points. The fraction of the total lung volume encompassed within the GV gaze volume s, the fraction of lung nodules encompassed within each GV gaze volume (search effectiveness), the fraction of lung nodules within the GV gaze volume detected by the reader (recognition-acceptance effectiveness), and overall sensitivity of lung nodule detection were measured.
RESULTS: Detected nodules were within 50 pixels of the nearest gaze point for 990 of 992 correct detections. On average, radiologists searched 26.7% of the lung parenchyma in 3 minutes and 16 seconds and encompassed between 86 and 143 of 157 nodules within their GV gaze volume s. Once encompassed within their GV gaze volume , the average sensitivity of nodule recognition and acceptance ranged from 47 of 100 nodules to 103 of 124 nodules (sensitivity, 0.47-0.82). Overall sensitivity ranged from 47 to 114 of 157 nodules (sensitivity, 0.30-0.73) and showed moderate correlation (r = 0.62, P = .02) with the fraction of lung volume searched.
CONCLUSION: Relationships between reader search, recognition and acceptance, and overall lung nodule detection rate can be studied with eye tracking. Radiologists appear to actively search less than half of the lung parenchyma, with substantial interreader variation in volume searched, fraction of nodules included within the search volume, sensitivity for nodules within the search volume, and overall detection rate.

Mesh:

Year:  2014        PMID: 25325324     DOI: 10.1148/radiol.14132918

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  22 in total

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Authors:  Kevin K John; Jakob D Jensen; Andy J King; Manusheela Pokharel; Douglas Grossman
Journal:  J Dermatol Sci       Date:  2018-04-06       Impact factor: 4.563

2.  Radiology resident MR and CT image analysis skill assessment using an interactive volumetric simulation tool - the RadioLOG project.

Authors:  Pedro Augusto Gondim Teixeira; Romain Cendre; Gabriela Hossu; Christophe Leplat; Jacques Felblinger; Alain Blum; Marc Braun
Journal:  Eur Radiol       Date:  2016-05-10       Impact factor: 5.315

3.  Local complexity metrics to quantify the effect of anatomical noise on detectability of lung nodules in chest CT imaging.

Authors:  Taylor Brunton Smith; Geoffrey D Rubin; Justin Solomon; Brian Harrawood; Kingshuk Roy Choudhury; Ehsan Samei
Journal:  J Med Imaging (Bellingham)       Date:  2018-11-22

4.  Image Annotation by Eye Tracking: Accuracy and Precision of Centerlines of Obstructed Small-Bowel Segments Placed Using Eye Trackers.

Authors:  Alfredo Lucas; Kang Wang; Cynthia Santillan; Albert Hsiao; Claude B Sirlin; Paul M Murphy
Journal:  J Digit Imaging       Date:  2019-10       Impact factor: 4.056

5.  The Radiologist's Gaze: Mapping Three-Dimensional Visual Search in Computed Tomography of the Abdomen and Pelvis.

Authors:  Linda C Kelahan; Allan Fong; Joseph Blumenthal; Swaminathan Kandaswamy; Raj M Ratwani; Ross W Filice
Journal:  J Digit Imaging       Date:  2019-04       Impact factor: 4.056

6.  Interchangeability between real and three-dimensional simulated lung tumors in computed tomography: an interalgorithm volumetry study.

Authors:  Marthony Robins; Justin Solomon; Jocelyn Hoye; Taylor Smith; Yuese Zheng; Lukas Ebner; Kingshuk Roy Choudhury; Ehsan Samei
Journal:  J Med Imaging (Bellingham)       Date:  2018-09-24

7.  Techniques for virtual lung nodule insertion: volumetric and morphometric comparison of projection-based and image-based methods for quantitative CT.

Authors:  Marthony Robins; Justin Solomon; Pooyan Sahbaee; Martin Sedlmair; Kingshuk Roy Choudhury; Aria Pezeshk; Berkman Sahiner; Ehsan Samei
Journal:  Phys Med Biol       Date:  2017-08-22       Impact factor: 3.609

8.  Measurement of the useful field of view for single slices of different imaging modalities and targets.

Authors:  Miguel A Lago; Ioannis Sechopoulos; François O Bochud; Miguel P Eckstein
Journal:  J Med Imaging (Bellingham)       Date:  2020-02-08

9.  Influence of background lung characteristics on nodule detection with computed tomography.

Authors:  Boning Li; Taylor B Smith; Kingshuk R Choudhury; Brian Harrawood; Lukas Ebner; Justus E Roos; Geoffrey D Rubin
Journal:  J Med Imaging (Bellingham)       Date:  2020-01-25

10.  The wisdom of crowds for visual search.

Authors:  Mordechai Z Juni; Miguel P Eckstein
Journal:  Proc Natl Acad Sci U S A       Date:  2017-05-10       Impact factor: 11.205

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