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Related Experiment Video

Updated: Feb 11, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Latency Management in Scribble-Based Interactive Segmentation of Medical Images.

Houssem-Eddine Gueziri, Michael J McGuffin, Catherine Laporte

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    Summary
    This summary is machine-generated.

    User efficiency in interactive image segmentation depends on computation time and visual feedback refresh methods. Reducing latency alone does not guarantee better performance, as user perception and interpretation significantly impact task completion times.

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    Area of Science:

    • Computer Vision
    • Human-Computer Interaction
    • Cognitive Science

    Background:

    • Interactive image segmentation is crucial for many applications.
    • User performance is influenced by system responsiveness and feedback mechanisms.
    • Understanding human factors is key to optimizing interactive segmentation tools.

    Purpose of the Study:

    • To analyze user efficiency in scribble-based interactive image segmentation.
    • To investigate the impact of computation time (latency) and visual feedback refresh methods on user performance.
    • To characterize user efficiency as a function of computation time.

    Main Methods:

    • A controlled experiment was conducted with users performing interactive image segmentation.
    • Eight levels of simulated latency (100-2000 ms) were tested.
    • Two visual feedback refresh techniques were compared: automatic and user-initiated.

    Main Results:

    • For short latencies, automatic feedback slowed user performance due to attentional capture.
    • As latency increased, the difference between refresh techniques diminished.
    • Users spent significant time interpreting results, regardless of the refresh method.

    Conclusions:

    • Latency perception varies with the refresh method, suggesting strategies to mitigate its impact.
    • Optimizing interactive segmentation requires considering both system speed and user cognitive load.
    • This study provides insights into the cognitive mechanisms underlying interactive image segmentation.