{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T19:21:13Z","timestamp":1767986473310,"version":"3.49.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T00:00:00Z","timestamp":1556668800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2019,8]]},"DOI":"10.1007\/s10278-019-00220-4","type":"journal-article","created":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T16:13:05Z","timestamp":1556727185000},"page":"597-604","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3169-9590","authenticated-orcid":false,"given":"J. N.","family":"Stember","sequence":"first","affiliation":[]},{"given":"H.","family":"Celik","sequence":"additional","affiliation":[]},{"given":"E.","family":"Krupinski","sequence":"additional","affiliation":[]},{"given":"P. D.","family":"Chang","sequence":"additional","affiliation":[]},{"given":"S.","family":"Mutasa","sequence":"additional","affiliation":[]},{"given":"B. J.","family":"Wood","sequence":"additional","affiliation":[]},{"given":"A.","family":"Lignelli","sequence":"additional","affiliation":[]},{"given":"G.","family":"Moonis","sequence":"additional","affiliation":[]},{"given":"L. H.","family":"Schwartz","sequence":"additional","affiliation":[]},{"given":"S.","family":"Jambawalikar","sequence":"additional","affiliation":[]},{"given":"U.","family":"Bagci","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,5,1]]},"reference":[{"issue":"3","key":"220_CR1","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1097\/00004424-197805000-00001","volume":"13","author":"HL Kundel","year":"1978","unstructured":"Kundel HL, Nodine CF, Carmody D: Visual scanning, pattern recognition and decision-making in pulmonary nodule detection. Investig Radiol 13(3):175\u2013181, 1978","journal-title":"Investig Radiol"},{"issue":"6","key":"220_CR2","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1097\/00004424-198906000-00012","volume":"24","author":"HL Kundel","year":"1989","unstructured":"Kundel HL, Nodine CF, Krupinski EA: Searching for lung nodules. Visual dwell indicates locations of false-positive and false-negative decisions. Investig Radiol 24(6):472\u2013478, 1989","journal-title":"Investig Radiol"},{"issue":"12","key":"220_CR3","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1016\/S1076-6332(96)80032-8","volume":"3","author":"CF Nodine","year":"1996","unstructured":"Nodine CF, Kundel HL, Lauver SC, Toto LC: Nature of expertise in searching mammograms for breast masses. Acad Radiol 3(12):1000\u20131006, 1996","journal-title":"Acad Radiol"},{"issue":"9","key":"220_CR4","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/S1076-6332(98)80295-X","volume":"5","author":"CF Nodine","year":"1998","unstructured":"Nodine CF, Krupinski EA: Perceptual skill, radiology expertise, and visual test performance with NINA and WALDO. Acad Radiol 5(9):603\u2013612, 1998","journal-title":"Acad Radiol"},{"issue":"12","key":"220_CR5","doi-asserted-by":"publisher","first-page":"1543","DOI":"10.1016\/j.humpath.2006.08.024","volume":"37","author":"EA Krupinski","year":"2006","unstructured":"Krupinski EA, Tillack AA, Richter L, Henderson JT, Bhattacharyya AK, Scott KM, Graham AR, Descour MR, Davis JR, Weinstein RS: Eye-movement study and human performance using telepathology virtual slides: implications for medical education and differences with experience. Hum Pathol 37(12):1543\u20131556, 2006","journal-title":"Hum Pathol"},{"issue":"6","key":"220_CR6","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1136\/amiajnl-2012-001503","volume":"20","author":"G Tourassi","year":"2013","unstructured":"Tourassi G, Voisin S, Paquit V, Krupinski E: Investigating the link between radiologists\u2019 gaze, diagnostic decision, and image content. J Am Med Inform Assoc 20(6):1067\u20131075, 2013","journal-title":"J Am Med Inform Assoc"},{"issue":"3","key":"220_CR7","volume":"5","author":"WF Auffermann","year":"2018","unstructured":"Auffermann WF, Krupinski EA, Tridandapani S: Search pattern training for evaluation of central venous catheter positioning on chest radiographs. J Med Imaging (Bellingham, Wash) 5(3):031407, 2018","journal-title":"J Med Imaging (Bellingham, Wash)"},{"issue":"03","key":"220_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.JMI.5.3.035502","volume":"5","author":"S Mall","year":"2018","unstructured":"Mall S, Brennan PC, Mello-Thoms C: Modeling visual search behavior of breast radiologists using a deep convolution neural network. J Med Imaging 5(03):1, 2018","journal-title":"J Med Imaging"},{"key":"220_CR9","doi-asserted-by":"crossref","unstructured":"Helbren E, Halligan S, Phillips P et al.: Towards a framework for analysis of eye-tracking studies in the three dimensional environment: A study of visual search by experienced readers of endoluminal CT colonography. J Radiol 87, 2014","DOI":"10.1259\/bjr.20130614"},{"issue":"5","key":"220_CR10","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1016\/j.joen.2017.12.021","volume":"44","author":"BP Hermanson","year":"2018","unstructured":"Hermanson BP, Burgdorf GC, Hatton JF, Speegle DM, Woodmansey KF: Visual fixation and scan patterns of dentists viewing dental periapical radiographs: an eye tracking pilot study. J Endod 44(5):722\u2013727, 2018","journal-title":"J Endod"},{"issue":"1","key":"220_CR11","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/S1076-6332(05)80780-9","volume":"1","author":"CH Hu","year":"1994","unstructured":"Hu CH, Kundel HL, Nodine CF, Krupinski EA, Toto LC: Searching for bone fractures: A comparison with pulmonary nodule search. Acad Radiol 1(1):25\u201332, 1994","journal-title":"Acad Radiol"},{"key":"220_CR12","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.ijmedinf.2017.03.001","volume":"105","author":"L McLaughlin","year":"2017","unstructured":"McLaughlin L, Bond R, Hughes C, McConnell J, McFadden S: Computing eye gaze metrics for the automatic assessment of radiographer performance during X-ray image interpretation. Int J Med Inform 105:11\u201321, 2017","journal-title":"Int J Med Inform"},{"issue":"4","key":"220_CR13","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1007\/s13244-018-0620-7","volume":"9","author":"A Iannessi","year":"2018","unstructured":"Iannessi A, Marcy P-Y, Clatz O, Bertrand A-S, Sugimoto M: A review of existing and potential computer user interfaces for modern radiology. Insights Imaging 9(4):599\u2013609, 2018","journal-title":"Insights Imaging"},{"issue":"03","key":"220_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.JMI.5.3.031406","volume":"5","author":"T Drew","year":"2018","unstructured":"Drew T, Williams LH, Aldred B, Heilbrun ME, Minoshima S: Quantifying the costs of interruption during diagnostic radiology interpretation using mobile eye-tracking glasses. J Med Imaging 5(03):1, 2018","journal-title":"J Med Imaging"},{"issue":"10","key":"220_CR15","doi-asserted-by":"publisher","first-page":"1260","DOI":"10.1016\/j.acra.2012.05.013","volume":"19","author":"T Drew","year":"2012","unstructured":"Drew T, Cunningham C, Wolfe JM: When and why might a computer-aided detection (CAD) system interfere with visual search? An eye-tracking study. Acad Radiol 19(10):1260\u20131267, 2012","journal-title":"Acad Radiol"},{"key":"220_CR16","unstructured":"Hanna TN, Zygmont ME, Peterson R et al.: The effects of fatigue from overnight shifts on radiology search patterns and diagnostic performance. J Am Coll Radiol, 2017"},{"issue":"2","key":"220_CR17","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.jacr.2016.10.009","volume":"14","author":"S Waite","year":"2017","unstructured":"Waite S, Kolla S, Jeudy J, Legasto A, Macknik SL, Martinez-Conde S, Krupinski EA, Reede DL: Tired in the reading room: The influence of fatigue in radiology. J Am Coll Radiol 14(2):191\u2013197, 2017","journal-title":"J Am Coll Radiol"},{"key":"220_CR18","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.media.2018.10.010","volume":"51","author":"N Khosravan","year":"2019","unstructured":"Khosravan N, Celik H, Turkbey B, Jones EC, Wood B, Bagci U: A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning. Med Image Anal 51:101\u2013115, 2019","journal-title":"Med Image Anal"},{"key":"220_CR19","doi-asserted-by":"crossref","unstructured":"Khosravan N, Celik H, Turkbey B, et al: Gaze2Segment: a pilot study for integrating eye-tracking technology into medical image segmentation. In: Bayesian and graphical Models for Biomedical Imaging International MICCAI Workshop on Medical Computer Vision 2016: Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging, pp 94\u2013104","DOI":"10.1007\/978-3-319-61188-4_9"},{"issue":"1","key":"220_CR20","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1002\/mp.13264","volume":"46","author":"B Sahiner","year":"2019","unstructured":"Sahiner B, Pezeshk A, Hadjiiski LM, et al: Deep learning in medical imaging and radiation therapy. Med Phys 46(1):e1\u2013e36, 2019","journal-title":"Med Phys"},{"key":"220_CR21","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, S\u00e1nchez CI: A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388, 2017","journal-title":"Med Image Anal"},{"issue":"3","key":"220_CR22","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/s12194-017-0406-5","volume":"10","author":"K Suzuki","year":"2017","unstructured":"Suzuki K: Overview of deep learning in medical imaging. Radiol Phys Technol 10(3):257\u2013273, 2017","journal-title":"Radiol Phys Technol"},{"issue":"3","key":"220_CR23","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1148\/radiol.2017162664","volume":"285","author":"LM Prevedello","year":"2017","unstructured":"Prevedello LM, Erdal BS, Ryu JL, Little KJ, Demirer M, Qian S, White RD: Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology 285(3):923\u2013931, 2017","journal-title":"Radiology"},{"issue":"2","key":"220_CR24","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1148\/rg.2017170047","volume":"38","author":"LR Folio","year":"2018","unstructured":"Folio LR, Machado LB, Dwyer AJ: Multimedia-enhanced radiology reports: Concept, components, and challenges. RadioGraphics 38(2):462\u2013482, 2018","journal-title":"RadioGraphics"},{"key":"220_CR25","unstructured":"Google Images. https:\/\/images.google.com\/ . Accessed December 6, 2018."},{"key":"220_CR26","unstructured":"Home - PMC - NCBI. https:\/\/www.ncbi.nlm.nih.gov\/pmc\/ . Accessed December 6, 2018."},{"key":"220_CR27","unstructured":"LONI image data archive (IDA). https:\/\/ida.loni.usc.edu\/login.jsp . Accessed November 19, 2018."},{"key":"220_CR28","unstructured":"Ronneberger O, Fischer P, Brox T: U-Net: Convolutional networks for biomedical image segmentation. arXiv:1505.04597v1 [cs.CV]"},{"issue":"2","key":"220_CR29","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1002\/mp.12079","volume":"44","author":"MU Dalm\u0131\u015f","year":"2017","unstructured":"Dalm\u0131\u015f MU, Litjens G, Holland K, Setio A, Mann R, Karssemeijer N, Gubern-M\u00e9rida A: Using deep learning to segment breast and fibroglandular tissue in MRI volumes. Med Phys 44(2):533\u2013546, 2017","journal-title":"Med Phys"},{"key":"220_CR30","unstructured":"Sadegh S, Salehi M, Erdogmus D, Gholipour A: Auto-context convolutional neural network (auto-net) for brain extraction in magnetic resonance imaging. arXiv:1703.02083v2 [cs.CV]"},{"key":"220_CR31","first-page":"171","volume":"95","author":"FG Venhuizen","year":"2011","unstructured":"Venhuizen FG, Van Ginneken B, Liefers B et al.: Optical coherence tomography; (100.4996) Pattern recognition, neural networks; (100.2960) Image analysis; (170.4470) Clinical applications; (170.4470) Ophthalmology. J Ophthalmol 95:171\u2013177, 2011","journal-title":"J Ophthalmol"},{"key":"220_CR32","doi-asserted-by":"crossref","unstructured":"Stember JN, Chang P, Stember DM et al.: Convolutional neural networks for the detection and measurement of cerebral aneurysms on magnetic resonance angiography. J Digit Imaging:1\u20138, 2018","DOI":"10.1007\/s10278-018-0162-z"},{"issue":"6","key":"220_CR33","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/BF01068419","volume":"15","author":"DJ Schuirmann","year":"1987","unstructured":"Schuirmann DJ: A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. J Pharmacokinet Biopharm 15(6):657\u2013680, 1987","journal-title":"J Pharmacokinet Biopharm"},{"issue":"4","key":"220_CR34","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1177\/1948550617697177","volume":"8","author":"D Lakens","year":"2017","unstructured":"Lakens D: Equivalence tests: a practical primer for t tests, correlations, and meta-analyses. Soc Psychol Personal Sci 8(4):355\u2013362, 2017","journal-title":"Soc Psychol Personal Sci"},{"key":"220_CR35","unstructured":"Dodge S, Karam L: Understanding how image quality affects deep neural networks. arXiv:1604.04004v2 [cs.CV]"},{"key":"220_CR36","unstructured":"Paranhos Da Costa GB, Contato WA, Nazare TS, Neto JESB, Ponti M: An empirical study on the effects of different types of noise in image classification tasks. arXiv:1609.02781v1 [cs.CV]"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-019-00220-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10278-019-00220-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-019-00220-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,17]],"date-time":"2022-09-17T07:06:21Z","timestamp":1663398381000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10278-019-00220-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,1]]},"references-count":36,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,8]]}},"alternative-id":["220"],"URL":"https:\/\/doi.org\/10.1007\/s10278-019-00220-4","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,1]]},"assertion":[{"value":"1 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}