Automatic liver tumor segmentation in follow-up CT scans: Preliminary method and results

Refael Vivanti; Ariel Ephrat; Leo Joskowicz; Naama Lev-Cohain; Onur A. Karaaslan; Jacob Sosna

doi:10.1007/978-3-319-28194-0_7

Automatic liver tumor segmentation in follow-up CT scans: Preliminary method and results

Refael Vivanti^*, Ariel Ephrat, Leo Joskowicz, Naama Lev-Cohain, Onur A. Karaaslan, Jacob Sosna

^*Corresponding author for this work

The Rachel and Selim Benin School of Engineering and Computer Science

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

21 Scopus citations

Abstract

We present a new, fully automatic algorithm for liver tumors segmentation in follow-up CT studies. The inputs are a baseline CT scan and a delineation of the tumors in it and a follow-up scan; the outputs are the tumors delineations in the follow-up CT scan. The algorithm starts by defining a region of interest using a deformable registration of the baseline scan and tumors delineations to the follow-up CT scan and automatic liver segmentation. Then, it constructs a voxel classifier by training a Convolutional Neural Network (CNN). Finally, it segments the tumor in the follow-up study with the learned classifier. The main novelty of our method is the combination of follow-up based detection with CNN-based segmentation. Our experimental results on 67 tumors from 21 patients with ground-truth segmentations approved by a radiologist yield a success rate of 95.4% and an average overlap error of 16.3% (std = 10.3).

Original language	English
Title of host publication	Patch-Based Techniques in Medical Imaging - First st International Workshop, Patch-MI 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers
Editors	Pierrick Coupé, Brent Munsell, Guorong Wu, Yiqiang Zhan, Daniel Rueckert
Publisher	Springer Verlag
Pages	54-61
Number of pages	8
ISBN (Print)	9783319281933
DOIs	https://doi.org/10.1007/978-3-319-28194-0_7
State	Published - 2015
Event	1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015 - Munich, Germany Duration: 9 Oct 2015 → 9 Oct 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9467
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015
Country/Territory	Germany
City	Munich
Period	9/10/15 → 9/10/15

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-319-28194-0_7

Cite this

Vivanti, R., Ephrat, A., Joskowicz, L., Lev-Cohain, N., Karaaslan, O. A., & Sosna, J. (2015). Automatic liver tumor segmentation in follow-up CT scans: Preliminary method and results. In P. Coupé, B. Munsell, G. Wu, Y. Zhan, & D. Rueckert (Eds.), Patch-Based Techniques in Medical Imaging - First st International Workshop, Patch-MI 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers (pp. 54-61). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9467). Springer Verlag. https://doi.org/10.1007/978-3-319-28194-0_7

Vivanti, Refael ; Ephrat, Ariel ; Joskowicz, Leo et al. / Automatic liver tumor segmentation in follow-up CT scans : Preliminary method and results. Patch-Based Techniques in Medical Imaging - First st International Workshop, Patch-MI 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers. editor / Pierrick Coupé ; Brent Munsell ; Guorong Wu ; Yiqiang Zhan ; Daniel Rueckert. Springer Verlag, 2015. pp. 54-61 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We present a new, fully automatic algorithm for liver tumors segmentation in follow-up CT studies. The inputs are a baseline CT scan and a delineation of the tumors in it and a follow-up scan; the outputs are the tumors delineations in the follow-up CT scan. The algorithm starts by defining a region of interest using a deformable registration of the baseline scan and tumors delineations to the follow-up CT scan and automatic liver segmentation. Then, it constructs a voxel classifier by training a Convolutional Neural Network (CNN). Finally, it segments the tumor in the follow-up study with the learned classifier. The main novelty of our method is the combination of follow-up based detection with CNN-based segmentation. Our experimental results on 67 tumors from 21 patients with ground-truth segmentations approved by a radiologist yield a success rate of 95.4% and an average overlap error of 16.3% (std = 10.3).",

author = "Refael Vivanti and Ariel Ephrat and Leo Joskowicz and Naama Lev-Cohain and Karaaslan, {Onur A.} and Jacob Sosna",

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Vivanti, R, Ephrat, A, Joskowicz, L, Lev-Cohain, N, Karaaslan, OA & Sosna, J 2015, Automatic liver tumor segmentation in follow-up CT scans: Preliminary method and results. in P Coupé, B Munsell, G Wu, Y Zhan & D Rueckert (eds), Patch-Based Techniques in Medical Imaging - First st International Workshop, Patch-MI 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9467, Springer Verlag, pp. 54-61, 1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015, Munich, Germany, 9/10/15. https://doi.org/10.1007/978-3-319-28194-0_7

Automatic liver tumor segmentation in follow-up CT scans: Preliminary method and results. / Vivanti, Refael; Ephrat, Ariel; Joskowicz, Leo et al.
Patch-Based Techniques in Medical Imaging - First st International Workshop, Patch-MI 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers. ed. / Pierrick Coupé; Brent Munsell; Guorong Wu; Yiqiang Zhan; Daniel Rueckert. Springer Verlag, 2015. p. 54-61 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9467).

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

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T1 - Automatic liver tumor segmentation in follow-up CT scans

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AU - Vivanti, Refael

AU - Ephrat, Ariel

AU - Joskowicz, Leo

AU - Lev-Cohain, Naama

AU - Karaaslan, Onur A.

AU - Sosna, Jacob

PY - 2015

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N2 - We present a new, fully automatic algorithm for liver tumors segmentation in follow-up CT studies. The inputs are a baseline CT scan and a delineation of the tumors in it and a follow-up scan; the outputs are the tumors delineations in the follow-up CT scan. The algorithm starts by defining a region of interest using a deformable registration of the baseline scan and tumors delineations to the follow-up CT scan and automatic liver segmentation. Then, it constructs a voxel classifier by training a Convolutional Neural Network (CNN). Finally, it segments the tumor in the follow-up study with the learned classifier. The main novelty of our method is the combination of follow-up based detection with CNN-based segmentation. Our experimental results on 67 tumors from 21 patients with ground-truth segmentations approved by a radiologist yield a success rate of 95.4% and an average overlap error of 16.3% (std = 10.3).

AB - We present a new, fully automatic algorithm for liver tumors segmentation in follow-up CT studies. The inputs are a baseline CT scan and a delineation of the tumors in it and a follow-up scan; the outputs are the tumors delineations in the follow-up CT scan. The algorithm starts by defining a region of interest using a deformable registration of the baseline scan and tumors delineations to the follow-up CT scan and automatic liver segmentation. Then, it constructs a voxel classifier by training a Convolutional Neural Network (CNN). Finally, it segments the tumor in the follow-up study with the learned classifier. The main novelty of our method is the combination of follow-up based detection with CNN-based segmentation. Our experimental results on 67 tumors from 21 patients with ground-truth segmentations approved by a radiologist yield a success rate of 95.4% and an average overlap error of 16.3% (std = 10.3).

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A2 - Zhan, Yiqiang

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Vivanti R, Ephrat A, Joskowicz L, Lev-Cohain N, Karaaslan OA, Sosna J. Automatic liver tumor segmentation in follow-up CT scans: Preliminary method and results. In Coupé P, Munsell B, Wu G, Zhan Y, Rueckert D, editors, Patch-Based Techniques in Medical Imaging - First st International Workshop, Patch-MI 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers. Springer Verlag. 2015. p. 54-61. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-28194-0_7

Automatic liver tumor segmentation in follow-up CT scans: Preliminary method and results

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