Data descriptor: The openEar library of 3D models of the human temporal bone based on computed tomography and micro-slicing

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Standard

Data descriptor : The openEar library of 3D models of the human temporal bone based on computed tomography and micro-slicing. / Sieber, Daniel; Erfurt, Peter; John, Samuel; Dos Santos, Gabriel Ribeiro; Schurzig, Daniel; Sørensen, Mads Sølvsten; Lenarz, Thomas.

I: Scientific Data, Bind 6, 180297, 2019.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Sieber, D, Erfurt, P, John, S, Dos Santos, GR, Schurzig, D, Sørensen, MS & Lenarz, T 2019, 'Data descriptor: The openEar library of 3D models of the human temporal bone based on computed tomography and micro-slicing', Scientific Data, bind 6, 180297. https://doi.org/10.1038/sdata.2018.297

APA

Sieber, D., Erfurt, P., John, S., Dos Santos, G. R., Schurzig, D., Sørensen, M. S., & Lenarz, T. (2019). Data descriptor: The openEar library of 3D models of the human temporal bone based on computed tomography and micro-slicing. Scientific Data, 6, [180297]. https://doi.org/10.1038/sdata.2018.297

Vancouver

Sieber D, Erfurt P, John S, Dos Santos GR, Schurzig D, Sørensen MS o.a. Data descriptor: The openEar library of 3D models of the human temporal bone based on computed tomography and micro-slicing. Scientific Data. 2019;6. 180297. https://doi.org/10.1038/sdata.2018.297

Author

Sieber, Daniel ; Erfurt, Peter ; John, Samuel ; Dos Santos, Gabriel Ribeiro ; Schurzig, Daniel ; Sørensen, Mads Sølvsten ; Lenarz, Thomas. / Data descriptor : The openEar library of 3D models of the human temporal bone based on computed tomography and micro-slicing. I: Scientific Data. 2019 ; Bind 6.

Bibtex

@article{0519dd9781a8456f83f3f9a3e42a29a5,
title = "Data descriptor: The openEar library of 3D models of the human temporal bone based on computed tomography and micro-slicing",
abstract = "Virtual reality surgical simulation of temporal bone surgery requires digitized models of the full anatomical region in high quality and colour information to allow realistic texturization. Existing datasets which are usually based on microCT imaging are unable to fulfil these requirements as per the limited specimen size, and lack of colour information. The OpenEar Dataset provides a library consisting of eight three-dimensional models of the human temporal bone to enable surgical training including colour data. Each dataset is based on a combination of multimodal imaging including Cone Beam Computed Tomography (CBCT) and micro-slicing. 3D reconstruction of micro-slicing images and subsequent registration to CBCT images allowed for relatively efficient multimodal segmentation of inner ear compartments, middle ear bones, tympanic membrane, relevant nerve structures, blood vessels and the temporal bone. Raw data from the experiment as well as voxel data and triangulated models from the segmentation are provided in full for use in surgical simulators or any other application which relies on high quality models of the human temporal bone.",
author = "Daniel Sieber and Peter Erfurt and Samuel John and {Dos Santos}, {Gabriel Ribeiro} and Daniel Schurzig and S{\o}rensen, {Mads S{\o}lvsten} and Thomas Lenarz",
year = "2019",
doi = "10.1038/sdata.2018.297",
language = "English",
volume = "6",
journal = "Scientific data",
issn = "2052-4463",
publisher = "nature publishing group",

}

RIS

TY - JOUR

T1 - Data descriptor

T2 - The openEar library of 3D models of the human temporal bone based on computed tomography and micro-slicing

AU - Sieber, Daniel

AU - Erfurt, Peter

AU - John, Samuel

AU - Dos Santos, Gabriel Ribeiro

AU - Schurzig, Daniel

AU - Sørensen, Mads Sølvsten

AU - Lenarz, Thomas

PY - 2019

Y1 - 2019

N2 - Virtual reality surgical simulation of temporal bone surgery requires digitized models of the full anatomical region in high quality and colour information to allow realistic texturization. Existing datasets which are usually based on microCT imaging are unable to fulfil these requirements as per the limited specimen size, and lack of colour information. The OpenEar Dataset provides a library consisting of eight three-dimensional models of the human temporal bone to enable surgical training including colour data. Each dataset is based on a combination of multimodal imaging including Cone Beam Computed Tomography (CBCT) and micro-slicing. 3D reconstruction of micro-slicing images and subsequent registration to CBCT images allowed for relatively efficient multimodal segmentation of inner ear compartments, middle ear bones, tympanic membrane, relevant nerve structures, blood vessels and the temporal bone. Raw data from the experiment as well as voxel data and triangulated models from the segmentation are provided in full for use in surgical simulators or any other application which relies on high quality models of the human temporal bone.

AB - Virtual reality surgical simulation of temporal bone surgery requires digitized models of the full anatomical region in high quality and colour information to allow realistic texturization. Existing datasets which are usually based on microCT imaging are unable to fulfil these requirements as per the limited specimen size, and lack of colour information. The OpenEar Dataset provides a library consisting of eight three-dimensional models of the human temporal bone to enable surgical training including colour data. Each dataset is based on a combination of multimodal imaging including Cone Beam Computed Tomography (CBCT) and micro-slicing. 3D reconstruction of micro-slicing images and subsequent registration to CBCT images allowed for relatively efficient multimodal segmentation of inner ear compartments, middle ear bones, tympanic membrane, relevant nerve structures, blood vessels and the temporal bone. Raw data from the experiment as well as voxel data and triangulated models from the segmentation are provided in full for use in surgical simulators or any other application which relies on high quality models of the human temporal bone.

U2 - 10.1038/sdata.2018.297

DO - 10.1038/sdata.2018.297

M3 - Journal article

C2 - 30620342

AN - SCOPUS:85059809991

VL - 6

JO - Scientific data

JF - Scientific data

SN - 2052-4463

M1 - 180297

ER -

ID: 240635988