short-paper

Where do experts look while doing 3D image segmentation

Authors:

Anahita Sanandaji,

Cindy Grimm,

Ruth West,

Max ParolaAuthors Info & Claims

ETRA '16: Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications

Pages 171 - 174

https://doi.org/10.1145/2857491.2857538

Published: 14 March 2016 Publication History

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Abstract

3D image segmentation is a fundamental process in many scientific and medical applications. Automatic algorithms do exist, but there are many use cases where these algorithms fail. The gold standard is still manual segmentation or review. Unfortunately, even for an expert this is laborious, time consuming, and prone to errors. Existing 3D segmentation tools do not currently take into account human mental models and low-level perception tasks. Our goal is to improve the quality and efficiency of manual segmentation and review by analyzing how experts perform segmentation. As a preliminary step we conducted a field study with 8 segmentation experts, recording video and eye tracking data. We developed a novel coding scheme to analyze this data and verified that it successfully covers and quantifies the low-level actions, tasks and behaviors of experts during 3D image segmentation.

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Cited By

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Zorzal ESousa MMendes Ddos Anjos RMedeiros DPaulo SRodrigues PMendes JDelmas VUhl JMogorrón JJorge JLopes D(2022)Anatomy StudioComputers and Graphics10.1016/j.cag.2019.09.00685:C(74-84)Online publication date: 21-Apr-2022
https://dl.acm.org/doi/10.1016/j.cag.2019.09.006
Sanandaji AGrimm CWest RSanchez C(2021)Developing and Validating a Computer-Based Training Tool for Inferring 2D Cross-Sections of Complex 3D StructuresHuman Factors: The Journal of the Human Factors and Ergonomics Society10.1177/0018720821101811065:3(508-528)Online publication date: 18-May-2021
https://doi.org/10.1177/00187208211018110
Roberto Zorzal ESousa MMendes DFigueiredo Paulo SRodrigues PJorge JLopes D(2021)A Tool for Collaborative Anatomical DissectionDigital Anatomy10.1007/978-3-030-61905-3_3(41-58)Online publication date: 15-May-2021
https://doi.org/10.1007/978-3-030-61905-3_3
Show More Cited By

Index Terms

Where do experts look while doing 3D image segmentation
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Perception

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Published In

ETRA '16: Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications

March 2016

378 pages

ISBN:9781450341257

DOI:10.1145/2857491

Conference Chairs:
Pernilla Qvarfordt
FX Palo Alto Laboratory
,
Dan Witzner Hansen
IT University of Copenhagen, Denmark

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 14 March 2016

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ETRA '16: 2016 Symposium on Eye Tracking Research and Applications

March 14 - 17, 2016

South Carolina, Charleston

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Overall Acceptance Rate 69 of 137 submissions, 50%

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2025 Symposium on Eye Tracking Research and Applications

May 26 - 29, 2025

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Zorzal ESousa MMendes Ddos Anjos RMedeiros DPaulo SRodrigues PMendes JDelmas VUhl JMogorrón JJorge JLopes D(2022)Anatomy StudioComputers and Graphics10.1016/j.cag.2019.09.00685:C(74-84)Online publication date: 21-Apr-2022
https://dl.acm.org/doi/10.1016/j.cag.2019.09.006
Sanandaji AGrimm CWest RSanchez C(2021)Developing and Validating a Computer-Based Training Tool for Inferring 2D Cross-Sections of Complex 3D StructuresHuman Factors: The Journal of the Human Factors and Ergonomics Society10.1177/0018720821101811065:3(508-528)Online publication date: 18-May-2021
https://doi.org/10.1177/00187208211018110
Roberto Zorzal ESousa MMendes DFigueiredo Paulo SRodrigues PJorge JLopes D(2021)A Tool for Collaborative Anatomical DissectionDigital Anatomy10.1007/978-3-030-61905-3_3(41-58)Online publication date: 15-May-2021
https://doi.org/10.1007/978-3-030-61905-3_3
Sanandaji AGrimm CWest RCunningham DBreuß MJoerg SInterrante V(2017)Inferring cross-sections of 3D objectsProceedings of the ACM Symposium on Applied Perception10.1145/3119881.3119888(1-4)Online publication date: 16-Sep-2017
https://dl.acm.org/doi/10.1145/3119881.3119888
West RKajihara MParola MHays KHillard LCarlew ADeutsch JLane BHolloway MJohn BSanandaji AGrimm CZhang KKerren A(2016)Eliciting Tacit Expertise in 3D Volume SegmentationProceedings of the 9th International Symposium on Visual Information Communication and Interaction10.1145/2968220.2968235(59-66)Online publication date: 24-Sep-2016
https://dl.acm.org/doi/10.1145/2968220.2968235
Sanandaji AGrimm CWest RJain EJoerg S(2016)How experts' mental model affects 3D image segmentationProceedings of the ACM Symposium on Applied Perception10.1145/2931002.2948718(135-135)Online publication date: 22-Jul-2016
https://dl.acm.org/doi/10.1145/2931002.2948718

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How experts' mental model affects 3D image segmentation

An unbiased and intervoxel watershed algorithm for 3d image segmentation

3D unsupervised modified spatial fuzzy c-means method for segmentation of 3D brain MR image

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