@inproceedings{ba177b84930d4cae976acc631d23dbaf,
title = "Where do experts look while doing 3D image segmentation",
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 pre-Uminary 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.",
keywords = "3D image segmentation, Coding scheme, Perception",
author = "Anahita Sanandaji and Cindy Grimm and Ruth West and Max Parola and Meghan Kajihara and Jeremy Deutsch and Anne Carlew and Deniece Yates",
note = "Publisher Copyright: {\textcopyright} 2016 Copyright held by the owner/author(s). Publication rights licensed to ACM. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.; 9th Biennial ACM Symposium on Eye Tracking Research and Applications, ETRA 2016 ; Conference date: 14-03-2016 Through 17-03-2016",
year = "2016",
month = mar,
day = "14",
doi = "10.1145/2857491.2857538",
language = "English (US)",
series = "Eye Tracking Research and Applications Symposium (ETRA)",
publisher = "Association for Computing Machinery",
pages = "171--174",
editor = "Spencer, {Stephen N.}",
booktitle = "Proceedings - ETRA 2016",
}