Image Annotation by Eye Tracking: Accuracy and Precision of Centerlines of Obstructed Small-Bowel Segments Placed Using Eye Trackers.
Small-bowel obstruction (SBO) is a common and important disease, for which machine learning tools have yet to be developed. Image annotation is a critical first step for development of such tools. This study assesses whether image annotation by eye tracking is sufficiently accurate and precise to se...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 5; pp. 855 - 865 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=138543062&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138543062 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2019 vid: 32 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138543062 138543062 144036225 138543062 10.1007/s10278-018-0169-5 138543062 ppf: 855 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Image Annotation by Eye Tracking: Accuracy and Precision of Centerlines of Obstructed Small-Bowel Segments Placed Using Eye Trackers. aug: au: Lucas, Alfredo Wang, Kang Santillan, Cynthia Hsiao, Albert Sirlin, Claude B. Murphy, Paul M. affil: Department of Bioengineering, University of California, San Diego, CA, USA sug: subj: Machine Learning Eye Movements Intestinal Diseases Diagnosis Precision Tomography, X-Ray Computed Human Clinical Assessment Tools Validity Intestines Pathology Intrarater Reliability Artificial Intelligence ab: Small-bowel obstruction (SBO) is a common and important disease, for which machine learning tools have yet to be developed. Image annotation is a critical first step for development of such tools. This study assesses whether image annotation by eye tracking is sufficiently accurate and precise to serve as a first step in the development of machine learning tools for detection of SBO on CT. Seven subjects diagnosed with SBO by CT were included in the study. For each subject, an obstructed segment of bowel was chosen. Three observers annotated the centerline of the segment by manual fiducial placement and by visual fiducial placement using a Tobii 4c eye tracker. Each annotation was repeated three times. The distance between centerlines was calculated after alignment using dynamic time warping (DTW) and statistically compared to clinical thresholds for diagnosis of SBO. Intra-observer DTW distance between manual and visual centerlines was calculated as a measure of accuracy. These distances were 1.1 ± 0.2, 1.3 ± 0.4, and 1.8 ± 0.2 cm for the three observers and were less than 1.5 cm for two of three observers (P < 0.01). Intra- and inter-observer DTW distances between centerlines placed with each method were calculated as measures of precision. These distances were 0.6 ± 0.1 and 0.8 ± 0.2 cm for manual centerlines, 1.1 ± 0.4 and 1.9 ± 0.6 cm for visual centerlines, and were less than 3.0 cm in all cases (P < 0.01). Results suggest that eye tracking–based annotation is sufficiently accurate and precise for small-bowel centerline annotation for use in machine learning–based applications. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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