User Interaction in Semi-Automatic Segmentation of Organs at Risk: a Case Study in Radiotherapy.
Accurate segmentation of organs at risk is an important step in radiotherapy planning. Manual segmentation being a tedious procedure and prone to inter- and intra-observer variability, there is a growing interest in automated segmentation methods. However, automatic methods frequently fail to provid...
| Publicado en: | Journal of Digital Imaging Vol. 29; no. 2; pp. 264 - 278 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | case study diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2016
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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=113705632&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 113705632 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2016 vid: 29 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 113705632 113705632 113705632 10.1007/s10278-015-9839-8 113705632 ppf: 264 ppct: 14 formats: fmt: @attributes: type: P tig: atl: User Interaction in Semi-Automatic Segmentation of Organs at Risk: a Case Study in Radiotherapy. aug: au: Ramkumar, Anjana Dolz, Jose Kirisli, Hortense Adebahr, Sonja Schimek-Jasch, Tanja Nestle, Ursula Massoptier, Laurent Varga, Edit Stappers, Pieter Niessen, Wiro Song, Yu affil: Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15 2628CE Delft The Netherlands sug: subj: Neoplasms Radiotherapy User-Computer Interface Radiographic Image Interpretation, Computer-Assisted Evaluation Research Case Studies Human ab: Accurate segmentation of organs at risk is an important step in radiotherapy planning. Manual segmentation being a tedious procedure and prone to inter- and intra-observer variability, there is a growing interest in automated segmentation methods. However, automatic methods frequently fail to provide satisfactory result, and post-processing corrections are often needed. Semi-automatic segmentation methods are designed to overcome these problems by combining physicians' expertise and computers' potential. This study evaluates two semi-automatic segmentation methods with different types of user interactions, named the 'strokes' and the 'contour', to provide insights into the role and impact of human-computer interaction. Two physicians participated in the experiment. In total, 42 case studies were carried out on five different types of organs at risk. For each case study, both the human-computer interaction process and quality of the segmentation results were measured subjectively and objectively. Furthermore, different measures of the process and the results were correlated. A total of 36 quantifiable and ten non-quantifiable correlations were identified for each type of interaction. Among those pairs of measures, 20 of the contour method and 22 of the strokes method were strongly or moderately correlated, either directly or inversely. Based on those correlated measures, it is concluded that: (1) in the design of semi-automatic segmentation methods, user interactions need to be less cognitively challenging; (2) based on the observed workflows and preferences of physicians, there is a need for flexibility in the interface design; (3) the correlated measures provide insights that can be used in improving user interaction design. pubtype: Academic Journal doctype: case study diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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