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3D volume segmentation is a fundamental task in many scientific and medicalapplications. Producing accurate segmentations efficiently is challenging, inpart due to low imaging data quality (e.g., noise and low image resolution) andambiguity in the data that can only be resolved with higher-level knowledge ofthe structure. Automatic algorithms do exist, but there are many use caseswhere they fail. The gold standard is still manual segmentation or review.Unfortunately, even for an expert, manual segmentation is laborious, timeconsuming, and prone to errors. Existing 3D segmentation tools are oftendesigned based on the underlying algorithm, and do not take into account humanmental models, their lower-level perception abilities, and higher-levelcognitive tasks. Our goal is to analyze manual segmentation using the criticaldecision method (CDM) in order to gain a better understanding of the low-level(perceptual and marking) actions and higher-level decision-making processesthat segmenters use. A key challenge we faced is that decision-making consistsof an accumulated set of low-level visual-spatial decisions that areinter-related and difficult to articulate verbally. To address this, wedeveloped a novel hybrid protocol which integrates CDM with eye-tracking,observation, and targeted questions. In this paper, we develop and validatedata coding schemes for this hybrid data set that discern segmenters' low-levelactions, higher-level cognitive tasks, overall task structures, anddecision-making processes. We successfully detect the visual processing changesbased on tasks sequences and micro decisions reflected in the eye-gaze data andidentified different segmentation decision strategies utilized by thesegmenters.

Analyzing 3D Volume Segmentation by Low-level Perceptual Cues, High-level Cognitive Tasks, and Decision-making Processes