Guided Structure-Aligned Segmentation of Volumetric Data

Holloway, Michelle; Sanandaji, Anahita; Yates, Deniece; Krigger, Amali; Sowell, Ross; West, Ruth; Grimm, Cindy

doi:10.1007/978-3-319-27857-5_28

Cited by 3 publications

(10 citation statements)

References 12 publications

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“…While contours of nontrivial models are available through previous works such as [2], our method is designed to work with contours of (deformed) variants of a common template structure. We expect to collect more of these data with the availability of tools such as [3].…”

Section: Discussionmentioning

confidence: 99%

“…The same experts also chose the position and orientation of a sparse set of source planes. Given a test subject, a novice was asked to pick a set of target planes in correspondence with the source planes (we use the interface in [3]). In addition, we followed the same approach for creating the templates to create a ground truth mesh for each test subject.…”

Section: Real-world Examplesmentioning

confidence: 99%

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Template-based surface reconstruction from cross-sections

Holloway

Grimm

2016

Computers & Graphics

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In this work we explore a new approach to solving the problem of surface reconstruction from cross-section curves. Existing reconstruction methods focus on producing smooth interpolations and may require a large number of crosssections to recreate feature-rich objects. In the context of medical image segmentation, we make the observation that anatomical structures usually share a common overall shape across subjects. This observation motivates taking a template-based approach that can better capture geometric features, instead of solving for the surface from the crosssectional curves alone. We deform an existing template, whose shape represents the structure of interest, to fit a set of target curves. We describe our algorithm with the main focus on finding a correspondence between a mesh and a cross-section curve network. We show our results with real medical data and compare to current reconstruction methods.

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Section: Discussionmentioning

confidence: 99%

Section: Real-world Examplesmentioning

confidence: 99%

Template-based surface reconstruction from cross-sections

Holloway

Grimm

2016

Computers & Graphics

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“…Viewpoint selection is an important spatial skill for understanding 2D cross-sections, both for finding object features and for determining the correct alignment of the plane with the feature. However, previous research shows that full viewpoint interactivity can confuse participants [28,30,31]. For our tasks, participants do not need full 3D viewpoint control; we only allow them to move the camera up and down or left and right using curved buttons.…”

Section: Training Tool Application Designmentioning

confidence: 99%

“…Also, previous studies show it is difficult to infer cross-sections of complex 3D structures without user control over the viewpoints and objects [28,47,62]. To our knowledge, there is no training tool--let alone a computer-based one--to support user control and interaction in this context.…”

Section: Introductionmentioning

confidence: 96%

“…The ability to understand 2D cross-sections of 3D structures plays an important role in many scientific domains including medical imaging, biology, geology, architecture, and engineering [11,21,24,28,29,47]. Identifying and orienting the correct 2D cross-section of a 3D object requires a complex set of spatial/visualization processes, including mental rotation, spatial structure understanding, and viewpoint projection.…”

Section: Introductionmentioning

confidence: 99%

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Developing and Validating a Computer-Based Training Tool for Inferring 2D Cross-Sections of Complex 3D Structures

et al. 2021

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Objective Developing and validating a novel domain-agnostic, computer-based training tool for enhancing 2D cross-section understanding of complex 3D structures. Background Understanding 2D cross-sections of 3D structures is a crucial skill in many disciplines, from geology to medical imaging . It requires a complex set of spatial/visualization skills including mental rotation, spatial structure understanding, and viewpoint projection. Prior studies show that experts differ from novices in these skills. Method We have developed a novel training tool for inferring 2D cross-sections of 3D structures using a participatory design methodology. We used a between-subject study design, with 60 participants, to evaluate the training tool. Our primary effectiveness evaluation was based on pre- and postspatial tests that measured both cross-section abilities and specific spatial skills: viewpoint, mental rotation, and card rotation. Results Results showed significant performance gains on inferring 2D cross-sections for participants of the training group. Our tool improves two other spatial skills as well: mental rotation and viewpoint visualization. Conclusion Our training tool was effective not only in enhancing 2D cross-section understanding of complex 3D structures, but also in improving mental rotation and viewpoint visualization skills. Application Our tool can be beneficial in different fields such as medical imaging, biology, geology, and engineering. For example, an application of our tool is in medical/research labs to train novice segmenters in ongoing manual 3D segmentation tasks. It can also be adapted in other contexts, such as training children, older adults, and individuals with very low spatial skills.

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A Summer Research Experience in Robotics

Grimm

Lyman-Holt²,

Smart

2017

AAAI

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The Robotics Program at Oregon State University has beenrunning an NSF-funded summer Research Experiences forUndergraduates (REU) site since 2014. Over twenty studentsper year (on average) have participated in the site, spendingten weeks embedded in the OSU Robotics Program. Our mainfocus with this REU Site is to give the participants a com-plete research experience, from problem definition to the fi-nal presentation of results, "in miniature". Our secondary ed-ucational objectives are: 1) Teach basic non-technical skillsneeded for graduate work, such as time management and lit-erature review, 2) Provide details on how to apply to gradu-ate school and for funding, 3) Clarify what we look for in agraduate student, and 4) Detail what to expect from the grad-uate student experience. In this paper, we describe the over-all structure of the participants’ summer experience, outlinesome of the training materials that we use, describe the moti-vations for our approach, and discuss the lessons that we havelearned after running the program for a number of years.

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Guided Structure-Aligned Segmentation of Volumetric Data

Cited by 3 publications

References 12 publications

Template-based surface reconstruction from cross-sections

Template-based surface reconstruction from cross-sections

Developing and Validating a Computer-Based Training Tool for Inferring 2D Cross-Sections of Complex 3D Structures

A Summer Research Experience in Robotics

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