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SoftCut: A Virtual Planning Tool for Soft Tissue Resection on CT Images
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
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 Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. 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 Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Oral and Maxillofacial Surgery. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Plastic Surgery.ORCID iD: 0000-0001-9590-2039
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2018 (English)In: Medical Image Understanding and Analysis / [ed] Mark Nixon, Sasan Mahmoodi, and Reyer Zwiggelaar, Cham: Springer, 2018, Vol. 894, p. 299-310Conference paper, Published paper (Refereed)
Abstract [en]

With the increasing use of three-dimensional (3D) models and Computer Aided Design (CAD) in the medical domain, virtual surgical planning is now frequently used. Most of the current solutions focus on bone surgical operations. However, for head and neck oncologic resection, soft tissue ablation and reconstruction are common operations. In this paper, we propose a method to provide a fast and efficient estimation of shape and dimensions of soft tissue resections. Our approach takes advantage of a simple sketch-based interface which allows the user to paint the contour of the resection on a patient specific 3D model reconstructed from a computed tomography (CT) scan. The volume is then virtually cut and carved following this pattern. From the outline of the resection defined on the skin surface as a closed curve, we can identify which areas of the skin are inside or outside this shape. We then use distance transforms to identify the soft tissue voxels which are closer from the inside of this shape. Thus, we can propagate the shape of the resection inside the soft tissue layers of the volume. We demonstrate the usefulness of the method on patient specific CT data.

Place, publisher, year, edition, pages
Cham: Springer, 2018. Vol. 894, p. 299-310
Series
Communications in Computer and Information Science
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-364351DOI: 10.1007/978-3-319-95921-4_28ISBN: 978-3-319-95920-7 (print)OAI: oai:DiVA.org:uu-364351DiVA, id: diva2:1258709
Conference
22nd Medical Image Understanding and Analysis (MIUA) 2018
Available from: 2018-10-25 Created: 2018-10-25 Last updated: 2018-10-25

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Nysjö, FredrikMalmberg, FilipThor, AndreasRodriguez-Lorenzo, AndresNyström, Ingela

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Nysjö, FredrikMalmberg, FilipThor, AndreasRodriguez-Lorenzo, AndresNyström, Ingela
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Department of Information TechnologyDivision of Visual Information and InteractionComputerized Image Analysis and Human-Computer InteractionOral and Maxillofacial SurgeryPlastic Surgery
Medical Image Processing

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