通过低层次感知线索、高层次认知任务和决策过程分析3D体分割
Analyzing 3D Volume Segmentation by Low-level Perceptual Cues, High-level Cognitive Tasks, and Decision-making Processes
摘要：
3D体积分割是许多科学和医学应用中的基本任务。有效地产生准确的分割是具有挑战性的，部分地由于低成像数据质量（例如，噪声和低图像分辨率）和数据中的模糊性，这些模糊性只能用结构的更高级别的知识来解决。自动算法确实存在，但有许多用例它们失败了。黄金标准仍然是手动分割或审查。不幸的是，即使对于专家来说，手动分割也是费力、耗时且容易出错的。现有的3D分割工具通常是基于底层算法设计的，并且没有考虑到人类的心理模型，他们的低层次感知能力和更高层次的认知任务。我们的目标是使用关键决策方法（CDM）来分析手动分割，以便更好地了解分割器使用的低级（感知和标记）动作和高级决策过程。我们面临的一个关键挑战是，决策由一系列相互关联且难以用语言表达的低级视觉空间决策组成。为了解决这个问题，我们开发了一种新的混合协议，它集成了CDM与眼动跟踪，观察和有针对性的问题。在本文中，我们开发和验证的数据编码方案，这个混合数据集，识别段的低层次的行动，更高层次的认知任务，整体任务结构和决策过程。我们成功地检测视觉处理的变化，任务序列和微观决策反映在眼睛凝视数据的基础上，并确定不同的分割决策策略所使用的分割器。
英文摘要：
3D volume segmentation is a fundamental task in many scientific and medical applications. Producing accurate segmentations efficiently is challenging, in part due to low imaging data quality (e.g., noise and low image resolution) and ambiguity in the data that can only be resolved with higher-level knowledge of the structure. Automatic algorithms do exist, but there are many use cases where they fail. The gold standard is still manual segmentation or review. Unfortunately, even for an expert, manual segmentation is laborious, time consuming, and prone to errors. Existing 3D segmentation tools are often designed based on the underlying algorithm, and do not take into account human mental models, their lower-level perception abilities, and higher-level cognitive tasks. Our goal is to analyze manual segmentation using the critical decision method (CDM) in order to gain a better understanding of the low-level (perceptual and marking) actions and higher-level decision-making processes that segmenters use. A key challenge we faced is that decision-making consists of an accumulated set of low-level visual-spatial decisions that are inter-related and difficult to articulate verbally. To address this, we developed a novel hybrid protocol which integrates CDM with eye-tracking, observation, and targeted questions. In this paper, we develop and validate data coding schemes for this hybrid data set that discern segmenters' low-level actions, higher-level cognitive tasks, overall task structures, and decision-making processes. We successfully detect the visual processing changes based on tasks sequences and micro decisions reflected in the eye-gaze data and identified different segmentation decision strategies utilized by the segmenters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
