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User-Centered Design and Evaluation of Interactive Segmentation Methods for Medical Images = = Conception et evaluation orientees utilisateur des methodes de segmentation interactives des images medicales.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	User-Centered Design and Evaluation of Interactive Segmentation Methods for Medical Images =/
其他題名:	Conception et evaluation orientees utilisateur des methodes de segmentation interactives des images medicales.
其他題名:	Conception et evaluation orientees utilisateur des methodes de segmentation interactives des images medicales.
作者:	Gueziri, Houssem-Eddine.
面頁冊數:	1 online resource (145 pages)
附註:	Source: Dissertations Abstracts International, Volume: 79-10, Section: B.
Contained By:	Dissertations Abstracts International79-10B.
標題:	Biomedical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10749841click for full text (PQDT)
ISBN:	9780355792867

User-Centered Design and Evaluation of Interactive Segmentation Methods for Medical Images = = Conception et evaluation orientees utilisateur des methodes de segmentation interactives des images medicales.
Gueziri, Houssem-Eddine.

User-Centered Design and Evaluation of Interactive Segmentation Methods for Medical Images =Conception et evaluation orientees utilisateur des methodes de segmentation interactives des images medicales.Conception et evaluation orientees utilisateur des methodes de segmentation interactives des images medicales. - 1 online resource (145 pages)

Source: Dissertations Abstracts International, Volume: 79-10, Section: B.

Thesis (D.Eng.)--Ecole de Technologie Superieure (Canada), 2017.

Includes bibliographical references

Segmentation of medical images is a challenging task that aims to identify a particular structure present on the image. Among the existing methods involving the user at different levels, from a fully-manual to a fully-automated task, interactive segmentation methods provide assistance to the user during the task to reduce the variability in the results and allow occasional corrections of segmentation failures. Therefore, they offer a compromise between the segmentation efficiency and the accuracy of the results. It is the user who judges whether the results are satisfactory and how to correct them during the segmentation, making the process subject to human factors. Despite the strong influence of the user on the outcomes of a segmentation task, the impact of such factors has received little attention, with the literature focusing the assessment of segmentation processes on computational performance. Yet, involving the user performance in the analysis is more representative of a realistic scenario. Our goal is to explore the user behaviour in order to improve the efficiency of interactive image segmentation processes. This is achieved through three contributions. First, we developed a method which is based on a new user interaction mechanism to provide hints as to where to concentrate the computations. This significantly improves the computation efficiency without sacrificing the quality of the segmentation. The benefits of using such hints are twofold: (i) because our contribution is based on user interaction, it generalizes to a wide range of segmentation methods, and (ii) it gives comprehensive indications about where to focus the segmentation search. The latter advantage is used to achieve the second contribution. We developed an automated method based on a multi-scale strategy to: (i) reduce the user's workload and, (ii) improve the computational time up to tenfold, allowing real-time segmentation feedback. Third, we have investigated the effects of such improvements in computations on the user's performance. We report an experiment that manipulates the delay induced by the computation time while performing an interactive segmentation task. Results reveal that the influence of this delay can be significantly reduced with an appropriate interaction mechanism design. In conclusion, this project provides an effective image segmentation solution that has been developed in compliance with user performance requirements. We validated our approach through multiple user studies that provided a step forward into understanding the user behaviour during interactive image segmentation.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9780355792867Subjects--Topical Terms:

535387
Biomedical engineering.
Subjects--Index Terms:

Human factorsIndex Terms--Genre/Form:

542853
Electronic books.

User-Centered Design and Evaluation of Interactive Segmentation Methods for Medical Images = = Conception et evaluation orientees utilisateur des methodes de segmentation interactives des images medicales.
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500 $a Advisor: Laporte, Catherine; McGuffin, Michael J.
502 $a Thesis (D.Eng.)--Ecole de Technologie Superieure (Canada), 2017.
504 $a Includes bibliographical references
520 $a Segmentation of medical images is a challenging task that aims to identify a particular structure present on the image. Among the existing methods involving the user at different levels, from a fully-manual to a fully-automated task, interactive segmentation methods provide assistance to the user during the task to reduce the variability in the results and allow occasional corrections of segmentation failures. Therefore, they offer a compromise between the segmentation efficiency and the accuracy of the results. It is the user who judges whether the results are satisfactory and how to correct them during the segmentation, making the process subject to human factors. Despite the strong influence of the user on the outcomes of a segmentation task, the impact of such factors has received little attention, with the literature focusing the assessment of segmentation processes on computational performance. Yet, involving the user performance in the analysis is more representative of a realistic scenario. Our goal is to explore the user behaviour in order to improve the efficiency of interactive image segmentation processes. This is achieved through three contributions. First, we developed a method which is based on a new user interaction mechanism to provide hints as to where to concentrate the computations. This significantly improves the computation efficiency without sacrificing the quality of the segmentation. The benefits of using such hints are twofold: (i) because our contribution is based on user interaction, it generalizes to a wide range of segmentation methods, and (ii) it gives comprehensive indications about where to focus the segmentation search. The latter advantage is used to achieve the second contribution. We developed an automated method based on a multi-scale strategy to: (i) reduce the user's workload and, (ii) improve the computational time up to tenfold, allowing real-time segmentation feedback. Third, we have investigated the effects of such improvements in computations on the user's performance. We report an experiment that manipulates the delay induced by the computation time while performing an interactive segmentation task. Results reveal that the influence of this delay can be significantly reduced with an appropriate interaction mechanism design. In conclusion, this project provides an effective image segmentation solution that has been developed in compliance with user performance requirements. We validated our approach through multiple user studies that provided a step forward into understanding the user behaviour during interactive image segmentation.
520 $a La segmentation d'images consiste a identifier une structure particuliere dans une image. Parmi les methodes existantes qui impliquent l'utilisateur a differents niveaux, les methodes de segmentation interactives fournissent un support logiciel pour assister l'utilisateur dans cette tache, ce qui aide a reduire la variabilite des resultats et permet de corriger les erreurs occasionnelles. Ces methodes offrent un compromis entre l'efficacite et la precision des resultats. En effet, durant la segmentation, l'utilisateur decide si les resultats sont satisfaisants et dans le cas contraire, comment les corriger, rendant le processus sujet aux facteurs humains. Malgre la forte influence qu'a l'utilisateur sur l'issue de la segmentation, l'impact de ces facteurs a recu peu d'attention de la part de la communaute scientifique, qui souvent, reduit l'evaluation des methodes de segmentation a leurs performances de calcul. Pourtant, inclure la performance de l'utilisateur lors de l'evaluation de la segmentation permet une representation plus fidele de la realite. Notre but est d'explorer le comportement de l'utilisateur afin d'ameliorer l'efficacite des methodes de segmentation interactives. Cette tache est realisee en trois contributions. Dans un premier temps, nous avons developpe un nouveau mecanisme d'interaction utilisateur qui oriente la methode de segmentation vers les endroits de l'image ou concentrer les calculs. Ceci augmente significativement l'efficacite des calculs sans attenuer la qualite de la segmentation. Il y a un double avantage a utiliser un tel mecanisme: (i) puisque notre contribution est basee sur l'interaction utilisateur, l'approche est generalisable a un grand nombre de methodes de segmentation, et (ii) ce mecanisme permet une meilleure comprehension des endroits de l'image ou l'on doit orienter la recherche du contour lors de la segmentation. Ce dernier point est exploite pour realiser la deuxieme contribution. En effet, nous avons remplace le mecanisme d'interaction par une methode automatique basee sur une strategie multi-echelle qui permet de: (i) reduire l'effort produit par l'utilisateur lors de la segmentation, et (ii) ameliorer jusqu'a dix fois le temps de calcul, permettant une segmentation en temps-reel. Dans la troisieme contribution, nous avons etudie l'effet d'une telle amelioration des performances de calculs sur l'utilisateur. Nous avons mene une experience qui manipule les delais des calculs lors de la segmentation interactive. Les resultats revelent qu'une conception appropriee du mecanisme d'interaction peut reduire l'effet de ces delais sur l'utilisateur. En conclusion, ce projet offre une solution interactive de segmentation d'images developpee en tenant compte de la performance de l'utilisateur. Nous avons valide notre approche a travers de multiples etudes utilisateurs qui nous ont permis une meilleure comprehension du comportement utilisateur durant la segmentation interactive des images.
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538 $a Mode of access: World Wide Web
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650 4 $a Medical imaging. $3 3172799
650 4 $a Computer engineering. $3 621879
653 $a Human factors
653 $a Interactive graph-based segmentation
653 $a Latency
653 $a Medical image segmentation
653 $a Segmentation assessment
653 $a User interaction
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710 2 $a Ecole de Technologie Superieure (Canada). $b Departement de genie electrique. $3 3169175
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