Eliciting Tacit Expertise in 3D Volume Segmentation
Eliciting Tacit Expertise in 3D Volume Segmentation
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DOI：
10.1145/2968220.2968235
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发表时间：
2016-09
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通讯作者：
Ruth West;Meghan Kajihara;M. Parola;Kathryn Hays;L. Hillard;Anne Carlew;Jeremey Deutsch;Brandon Lane;M. Holloway;Brendan David-John;Anahita Sanandaji;C. Grimm
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作者：
Ruth West;Meghan Kajihara;M. Parola;Kathryn Hays;L. Hillard;Anne Carlew;Jeremey Deutsch;Brandon Lane;M. Holloway;Brendan David-John;Anahita Sanandaji;C. Grimm
三维体分割的输出对很多领域都是至关重要的。产生准确的分割通常被证明是低效和具有挑战性的，部分原因是由于缺乏成像数据质量（对比度和分辨率），以及由于数据中的模糊性，只能通过更高层次的结构知识和它所在的上下文来解决。自动和半自动方法正在改进，但在许多情况下仍然失败或需要大量的人工清理或干预。因此，专家手动分割和审查仍然是许多应用程序的黄金标准。不幸的是，现有的工具（包括定制的和商业的）通常是基于底层算法设计的，而不是表达高级意图的最佳方法。我们的目标是分析手动（或半自动）分割，以更好地理解低级（感知任务和动作）和高级决策制定。这可以用来产生更准确、更有效、更容易使用的分割工具。仅靠提问或观察不足以获取这些信息，因此我们采用混合捕获协议，将观察、调查和眼动追踪混合在一起。然后，我们开发并验证了能够识别低级动作和整体任务结构的数据编码方案。
The output of 3D volume segmentation is crucial to a wide range of endeavors. Producing accurate segmentations often proves to be both inefficient and challenging, in part due to lack of imaging data quality (contrast and resolution), and because of ambiguity in the data that can only be resolved with higher-level knowledge of the structure and the context wherein it resides. Automatic and semi-automatic approaches are improving, but in many cases still fail or require substantial manual clean-up or intervention. Expert manual segmentation and review is therefore still the gold standard for many applications. Unfortunately, existing tools (both custom-made and commercial) are often designed based on the underlying algorithm, not the best method for expressing higher-level intention. Our goal is to analyze manual (or semi-automatic) segmentation to gain a better understanding of both low-level (perceptual tasks and actions) and high-level decision making. This can be used to produce segmentation tools that are more accurate, efficient, and easier to use. Questioning or observation alone is insufficient to capture this information, so we utilize a hybrid capture protocol that blends observation, surveys, and eye tracking. We then developed, and validated, data coding schemes capable of discerning low-level actions and overall task structures.