MR-to-US Registration Using Multiclass Segmentation of Hepatic Vasculature with a Reduced 3D U-Net

Bart R. Thomson; Jasper N. Smit; Oleksandra V. Ivashchenko; Niels F.M. Kok; Koert F.D. Kuhlmann; Theo J.M. Ruers; Matteo Fusaglia

doi:10.1007/978-3-030-59716-0_27

MR-to-US Registration Using Multiclass Segmentation of Hepatic Vasculature with a Reduced 3D U-Net

Bart R. Thomson^*, Jasper N. Smit, Oleksandra V. Ivashchenko, Niels F.M. Kok, Koert F.D. Kuhlmann, Theo J.M. Ruers, Matteo Fusaglia

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

20 Citations (Scopus)

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Abstract

Accurate hepatic vessel segmentation and registration using ultrasound (US) can contribute to beneficial navigation during hepatic surgery. However, it is challenging due to noise and speckle in US imaging and liver deformation. Therefore, a workflow is developed using a reduced 3D U-Net for segmentation, followed by non-rigid coherent point drift (CPD) registration. By means of electromagnetically tracked US, 61 3D volumes were acquired during surgery. Dice scores of 0.77, 0.65 and 0.66 were achieved for segmentation of all vasculature, hepatic vein and portal vein respectively. This compares to inter-observer variabilities of 0.85, 0.88 and 0.74 respectively. Target registration error at a tumor lesion of interest was lower (7.1 mm) when registration is performed either on the hepatic or the portal vein, compared to using all vasculature (8.9 mm). Using clinical data, we developed a workflow consisting of multi-class segmentation combined with selective non-rigid registration that leads to sufficient accuracy for integration in computer assisted liver surgery.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
Editors	Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
Publisher	Springer
Pages	275-284
Number of pages	10
ISBN (Print)	9783030597153
DOIs	https://doi.org/10.1007/978-3-030-59716-0_27
Publication status	Published - 2020
Event	23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Online Conference, Lima, Peru Duration: 4 Oct 2020 → 8 Oct 2020 Conference number: 23

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12263 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Abbreviated title	MICCAI 2020
Country/Territory	Peru
City	Lima
Period	4/10/20 → 8/10/20

Keywords

Computer assisted intervention
Liver surgery
Non-rigid registration
22/2 OA procedure

Access to Document

10.1007/978-3-030-59716-0_27

Thomson-2020-MR-to-USFinal published version, 1.91 MBLicence: Taverne

Cite this

Thomson, B. R., Smit, J. N., Ivashchenko, O. V., Kok, N. F. M., Kuhlmann, K. F. D., Ruers, T. J. M., & Fusaglia, M. (2020). MR-to-US Registration Using Multiclass Segmentation of Hepatic Vasculature with a Reduced 3D U-Net. In A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu, & L. Joskowicz (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings (pp. 275-284). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12263 LNCS). Springer. https://doi.org/10.1007/978-3-030-59716-0_27

Thomson, Bart R. ; Smit, Jasper N. ; Ivashchenko, Oleksandra V. et al. / MR-to-US Registration Using Multiclass Segmentation of Hepatic Vasculature with a Reduced 3D U-Net. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. editor / Anne L. Martel ; Purang Abolmaesumi ; Danail Stoyanov ; Diana Mateus ; Maria A. Zuluaga ; S. Kevin Zhou ; Daniel Racoceanu ; Leo Joskowicz. Springer, 2020. pp. 275-284 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "MR-to-US Registration Using Multiclass Segmentation of Hepatic Vasculature with a Reduced 3D U-Net",

abstract = "Accurate hepatic vessel segmentation and registration using ultrasound (US) can contribute to beneficial navigation during hepatic surgery. However, it is challenging due to noise and speckle in US imaging and liver deformation. Therefore, a workflow is developed using a reduced 3D U-Net for segmentation, followed by non-rigid coherent point drift (CPD) registration. By means of electromagnetically tracked US, 61 3D volumes were acquired during surgery. Dice scores of 0.77, 0.65 and 0.66 were achieved for segmentation of all vasculature, hepatic vein and portal vein respectively. This compares to inter-observer variabilities of 0.85, 0.88 and 0.74 respectively. Target registration error at a tumor lesion of interest was lower (7.1 mm) when registration is performed either on the hepatic or the portal vein, compared to using all vasculature (8.9 mm). Using clinical data, we developed a workflow consisting of multi-class segmentation combined with selective non-rigid registration that leads to sufficient accuracy for integration in computer assisted liver surgery.",

keywords = "Computer assisted intervention, Liver surgery, Non-rigid registration, 22/2 OA procedure",

author = "Thomson, {Bart R.} and Smit, {Jasper N.} and Ivashchenko, {Oleksandra V.} and Kok, {Niels F.M.} and Kuhlmann, {Koert F.D.} and Ruers, {Theo J.M.} and Matteo Fusaglia",

year = "2020",

doi = "10.1007/978-3-030-59716-0_27",

language = "English",

isbn = "9783030597153",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "275--284",

editor = "Martel, {Anne L.} and Purang Abolmaesumi and Danail Stoyanov and Diana Mateus and Zuluaga, {Maria A.} and Zhou, {S. Kevin} and Daniel Racoceanu and Leo Joskowicz",

booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings",

address = "Germany",

note = "23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, MICCAI 2020 ; Conference date: 04-10-2020 Through 08-10-2020",

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Thomson, BR, Smit, JN, Ivashchenko, OV, Kok, NFM, Kuhlmann, KFD, Ruers, TJM & Fusaglia, M 2020, MR-to-US Registration Using Multiclass Segmentation of Hepatic Vasculature with a Reduced 3D U-Net. in AL Martel, P Abolmaesumi, D Stoyanov, D Mateus, MA Zuluaga, SK Zhou, D Racoceanu & L Joskowicz (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12263 LNCS, Springer, pp. 275-284, 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, Peru, 4/10/20. https://doi.org/10.1007/978-3-030-59716-0_27

MR-to-US Registration Using Multiclass Segmentation of Hepatic Vasculature with a Reduced 3D U-Net. / Thomson, Bart R.; Smit, Jasper N.; Ivashchenko, Oleksandra V. et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. ed. / Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov; Diana Mateus; Maria A. Zuluaga; S. Kevin Zhou; Daniel Racoceanu; Leo Joskowicz. Springer, 2020. p. 275-284 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12263 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - MR-to-US Registration Using Multiclass Segmentation of Hepatic Vasculature with a Reduced 3D U-Net

AU - Thomson, Bart R.

AU - Smit, Jasper N.

AU - Ivashchenko, Oleksandra V.

AU - Kok, Niels F.M.

AU - Kuhlmann, Koert F.D.

AU - Ruers, Theo J.M.

AU - Fusaglia, Matteo

N1 - Conference code: 23

PY - 2020

Y1 - 2020

N2 - Accurate hepatic vessel segmentation and registration using ultrasound (US) can contribute to beneficial navigation during hepatic surgery. However, it is challenging due to noise and speckle in US imaging and liver deformation. Therefore, a workflow is developed using a reduced 3D U-Net for segmentation, followed by non-rigid coherent point drift (CPD) registration. By means of electromagnetically tracked US, 61 3D volumes were acquired during surgery. Dice scores of 0.77, 0.65 and 0.66 were achieved for segmentation of all vasculature, hepatic vein and portal vein respectively. This compares to inter-observer variabilities of 0.85, 0.88 and 0.74 respectively. Target registration error at a tumor lesion of interest was lower (7.1 mm) when registration is performed either on the hepatic or the portal vein, compared to using all vasculature (8.9 mm). Using clinical data, we developed a workflow consisting of multi-class segmentation combined with selective non-rigid registration that leads to sufficient accuracy for integration in computer assisted liver surgery.

AB - Accurate hepatic vessel segmentation and registration using ultrasound (US) can contribute to beneficial navigation during hepatic surgery. However, it is challenging due to noise and speckle in US imaging and liver deformation. Therefore, a workflow is developed using a reduced 3D U-Net for segmentation, followed by non-rigid coherent point drift (CPD) registration. By means of electromagnetically tracked US, 61 3D volumes were acquired during surgery. Dice scores of 0.77, 0.65 and 0.66 were achieved for segmentation of all vasculature, hepatic vein and portal vein respectively. This compares to inter-observer variabilities of 0.85, 0.88 and 0.74 respectively. Target registration error at a tumor lesion of interest was lower (7.1 mm) when registration is performed either on the hepatic or the portal vein, compared to using all vasculature (8.9 mm). Using clinical data, we developed a workflow consisting of multi-class segmentation combined with selective non-rigid registration that leads to sufficient accuracy for integration in computer assisted liver surgery.

KW - Computer assisted intervention

KW - Liver surgery

KW - Non-rigid registration

KW - 22/2 OA procedure

UR - http://www.scopus.com/inward/record.url?scp=85092724636&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-59716-0_27

DO - 10.1007/978-3-030-59716-0_27

M3 - Conference contribution

AN - SCOPUS:85092724636

SN - 9783030597153

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 275

EP - 284

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings

A2 - Martel, Anne L.

A2 - Abolmaesumi, Purang

A2 - Stoyanov, Danail

A2 - Mateus, Diana

A2 - Zuluaga, Maria A.

A2 - Zhou, S. Kevin

A2 - Racoceanu, Daniel

A2 - Joskowicz, Leo

PB - Springer

T2 - 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

Y2 - 4 October 2020 through 8 October 2020

ER -

Thomson BR, Smit JN, Ivashchenko OV, Kok NFM, Kuhlmann KFD, Ruers TJM et al. MR-to-US Registration Using Multiclass Segmentation of Hepatic Vasculature with a Reduced 3D U-Net. In Martel AL, Abolmaesumi P, Stoyanov D, Mateus D, Zuluaga MA, Zhou SK, Racoceanu D, Joskowicz L, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. Springer. 2020. p. 275-284. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-59716-0_27

MR-to-US Registration Using Multiclass Segmentation of Hepatic Vasculature with a Reduced 3D U-Net

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