Latency management in scribble-based interactive segmentation of medical images [r-libre/3121]

Gueziri, Houssem-Eddine; McGuffin, Michael J et Laporte, Catherine (2017). Latency management in scribble-based interactive segmentation of medical images. IEEE Transactions on Biomedical Engineering, 65 (5), 1140-1150. https://doi.org/10.1109/TBME.2017.2777742

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Catégorie de document :	Articles de revues
Évaluation par un comité de lecture :	Oui
Étape de publication :	Publié
Résumé :	Objective: During an interactive image segmentation task, the outcome is strongly influenced by human factors. In particular, a reduction in computation time does not guarantee an improvement in the overall segmentation time. This paper characterizes user efficiency during scribble-based interactive segmentation as a function of computation time. Methods: We report a controlled experiment with users who experienced eight different levels of simulated latency (ranging from 100 to 2000 ms) with two techniques for refreshing visual feedback (either automatic, where the segmentation was recomputed and displayed continuously during label drawing, or user initiated, which was only computed and displayed each time the user hits a defined button). Results: For short latencies, the user's attention is focused on the automatic visual feedback, slowing down his/her labeling performance. This effect is attenuated as the latency grows larger, and the two refresh techniques yield similar user performance at the largest latencies. Moreover, during the segmentation task, participants spent in average 72.67% ± 2.42% for automatic refresh and 96.23% ± 0.06% for user-initiated refresh of the overall segmentation time interpreting the results. Conclusion: The latency is perceived differently according to the refresh method used during the segmentation task. Therefore, it is possible to reduce its impact on the user performance. Significance: This is the first time a study investigates the effects of latency in an interactive segmentation task. The analysis and recommendations provided in this paper help understanding the cognitive mechanisms in interactive image segmentation.
Adresse de la version officielle :	https://doi.org/10.1109/TBME.2017.2777742
Déposant:	GUEZIRI, Houssem
Responsable :	Houssem GUEZIRI
Dépôt :	14 déc. 2023 21:30
Dernière modification :	14 déc. 2023 21:30

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