Latency Management in Scribble-Based Interactive Segmentation of Medical Images

Gueziri, Houssem-Eddine; McGuffin, Michael; Laporte, Catherine

doi:10.1109/tbme.2017.2777742

Cited by 4 publications

(3 citation statements)

References 36 publications

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“…The performance issue was addressed with approximations of F-measure (Csurka, Larlus, and Perronnin 2013;Perazzi et al 2016b The robustness of the conventional segmentation approaches was already explored (Kamann and Rother 2020); yet, as user inputs are not involved, the robustness could only be measured w.r.t image perturbations. A recent series of works (Guan et al 2023;Zhang et al 2023b;Qiao et al 2023;Wang, Zhao, and Petzold 2023) measuring the robustness of SAM also focused on perturbing images rather than user prompts. In contrast, we fix input images and investi-gate the robustness w.r.t user inputs.…”

Section: Related Workmentioning

confidence: 99%

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TETRIS: Towards Exploring the Robustness of Interactive Segmentation

Moskalenko,

Shakhuro,

Vorontsova

et al. 2024

AAAI

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Interactive segmentation methods rely on user inputs to iteratively update the selection mask. A click specifying the object of interest is arguably the most simple and intuitive interaction type, and thereby the most common choice for interactive segmentation. However, user clicking patterns in the interactive segmentation context remain unexplored. Accordingly, interactive segmentation evaluation strategies rely more on intuition and common sense rather than empirical studies (e.g., assuming that users tend to click in the center of the area with the largest error). In this work, we conduct a real-user study to investigate real user clicking patterns. This study reveals that the intuitive assumption made in the common evaluation strategy may not hold. As a result, interactive segmentation models may show high scores in the standard benchmarks, but it does not imply that they would perform well in a real world scenario. To assess the applicability of interactive segmentation methods, we propose a novel evaluation strategy providing a more comprehensive analysis of a model's performance. To this end, we propose a methodology for finding extreme user inputs by a direct optimization in a white-box adversarial attack on the interactive segmentation model. Based on the performance with such adversarial user inputs, we assess the robustness of interactive segmentation models w.r.t click positions. Besides, we introduce a novel benchmark for measuring the robustness of interactive segmentation, and report the results of an extensive evaluation of dozens of models.

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Section: Related Workmentioning

confidence: 99%

“…(Xu et al 2016;Rother, Kolmogorov, and Blake 2004). In(Gueziri, McGuffin, and Laporte 2017), object selection was guided with manual strokes. In(Agustsson, Uijlings, and Ferrari 2019), an initial selection was made using bounding boxes obtained via extreme clicking(Papadopoulos et al 2017), and then refined with strokes (Cheng et al 2021b…”

mentioning

confidence: 99%

TETRIS: Towards Exploring the Robustness of Interactive Segmentation

Moskalenko,

Shakhuro,

Vorontsova

et al. 2024

AAAI

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“…Figure adopted from [17]. [19], [27], [32]. Bostock, Ogievetsky, and Heer write that "a sufficient frame rate is necessary for fluent interaction and animation", and that "results also indicate that browser vendors still have some distance to cover in improving SVG rendering performance" [7].…”

Section: Javascriptmentioning

confidence: 99%

Scalable Scalable Vector Graphics: Automatic Translation of Interactive SVGs to a Multithread VDOM for Fast Rendering

Schwab

Saffo

Bond

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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The dominant markup language for Web visualizations-Scalable Vector Graphics (SVG)-is comparatively easy to learn, and is open, accessible, customizable via CSS, and searchable via the DOM, with easy interaction handling and debugging. Because these attributes allow visualization creators to focus on design on implementation details, tools built on top of SVG, such as D3.js, are essential to the visualization community. However, slow SVG rendering can limit designs by effectively capping the number of on-screen data points, and this can force visualization creators to switch to Canvas or WebGL. These are less flexible (e.g., no search or styling via CSS), and harder to learn. We introduce Scalable Scalable Vector Graphics (SSVG) to reduce these limitations and allow complex and smooth visualizations to be created with SVG. SSVG automatically translates interactive SVG visualizations into a dynamic virtual DOM (VDOM) to bypass the browser's slow 'to specification' rendering by intercepting JavaScript function calls. De-coupling the SVG visualization specification from SVG rendering, and obtaining a dynamic VDOM, creates flexibility and opportunity for visualization system research. SSVG uses this flexibility to free up the main thread for more interactivity and renders the visualization with Canvas or WebGL on a web worker. Together, these concepts create a drop-in JavaScript library which can improve rendering performance by 3-9× with only one line of code added. To demonstrate applicability, we describe the use of SSVG on multiple example visualizations including published visualization research. A free copy of this paper, collected data, and source code are available as open science at osf.io/ge8wp.

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3D Block Matching Algorithm in Concealed Image Recognition and E-Commerce Customer Segmentation

Liu

2020

IEEE Sensors J.

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Latency Management in Scribble-Based Interactive Segmentation of Medical Images

Abstract: 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.

Cited by 4 publications

References 36 publications

TETRIS: Towards Exploring the Robustness of Interactive Segmentation

TETRIS: Towards Exploring the Robustness of Interactive Segmentation

Scalable Scalable Vector Graphics: Automatic Translation of Interactive SVGs to a Multithread VDOM for Fast Rendering

3D Block Matching Algorithm in Concealed Image Recognition and E-Commerce Customer Segmentation

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