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BoneSplit – A 3D painting tool for interactive bone segmentation in CT images
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.ORCID iD: 0000-0001-9739-0364
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Oral and Maxillofacial Surgery.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
2017 (English)In: Pattern Recognition and Information Processing: PRIP 2016, Springer, 2017, p. 3-13Conference paper, Published paper (Refereed)
Abstract [en]

We present an efficient interactive tool for segmenting individual bones and bone fragments in 3D computed tomography (CT) images. The tool, which is primarily intended for virtual craniomaxillofacial (CMF) surgery planning, combines direct volume rendering with interactive 3D texture painting to enable quick identification and marking of bone structures. The user can paint markers (seeds) directly on the rendered bone surfaces as well as on individual CT slices. Separation of the marked bones is then achieved through the random walks algorithm, which is applied on a graph constructed from the thresholded bones. The segmentation runs on the GPU and can achieve close to real-time update rates for volumes as large as 512x512x512 voxels. The user can perform segmentation editing to correct the result. An evaluation reports segmentation results comparable with manual segmentations, but obtained within a few minutes. In the invited PRIP talk, BoneSplit is presented and how the tool fits into our haptics-assisted surgery-planning system.

Place, publisher, year, edition, pages
Springer, 2017. p. 3-13
Series
Communications in Computer and Information Science ; 673
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-317762DOI: 10.1007/978-3-319-54220-1_1ISI: 000416103800001ISBN: 978-3-319-54219-5 (print)ISBN: 978-3-319-54220-1 (electronic)OAI: oai:DiVA.org:uu-317762DiVA, id: diva2:1082775
Conference
13th International Conference on Pattern Recognition and Information Processing (PRIP), Minsk, Belarus, October 3-5, 2016
Available from: 2017-02-17 Created: 2017-03-17 Last updated: 2019-02-27Bibliographically approved

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Nyström, IngelaNysjö, JohanThor, AndreasMalmberg, Filip

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Nyström, IngelaNysjö, JohanThor, AndreasMalmberg, Filip
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