A Soft STAPLE Algorithm Combined with Anatomical Knowledge

Eytan Kats; Jacob Goldberger; Hayit Greenspan

doi:https://doi.org/10.1007/978-3-030-32248-9_57

A Soft STAPLE Algorithm Combined with Anatomical Knowledge

Eytan Kats, Jacob Goldberger, Hayit Greenspan

Tel Aviv University, Bar-Ilan University

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

Abstract

Supervised machine learning algorithms, especially in the medical domain, are affected by considerable ambiguity in expert markings. In this study we address the case where the experts’ opinion is obtained as a distribution over the possible values. We propose a soft version of the STAPLE algorithm for experts’ markings fusion that can handle soft values. The algorithm was applied to obtain consensus from soft Multiple Sclerosis (MS) segmentation masks. Soft MS segmentations are constructed from manual binary delineations by including lesion surrounding voxels in the segmentation mask with a reduced confidence weight. We suggest that these voxels contain additional anatomical information about the lesion structure. The fused masks are utilized as ground truth mask to train a Fully Convolutional Neural Network (FCNN). The proposed method was evaluated on the MICCAI 2016 challenge dataset, and yields improved precision-recall tradeoff and a higher average Dice similarity coefficient.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
Editors	Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	510-517
Number of pages	8
ISBN (Print)	9783030322472
DOIs	https://doi.org/10.1007/978-3-030-32248-9_57
State	Published - 2019
Event	22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 13 Oct 2019 → 17 Oct 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11766 LNCS

Conference

Conference	22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Country/Territory	China
City	Shenzhen
Period	13/10/19 → 17/10/19

Keywords

MS lesion segmentation
STAPLE algorithm
Soft labels

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

https://doi.org/10.1007/978-3-030-32248-9_57

Cite this

Kats, E., Goldberger, J., & Greenspan, H. (2019). A Soft STAPLE Algorithm Combined with Anatomical Knowledge. In D. Shen, P-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 510-517). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11766 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32248-9_57

Kats, Eytan ; Goldberger, Jacob ; Greenspan, Hayit. / A Soft STAPLE Algorithm Combined with Anatomical Knowledge. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. editor / Dinggang Shen ; Pew-Thian Yap ; Tianming Liu ; Terry M. Peters ; Ali Khan ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou. Springer Science and Business Media Deutschland GmbH, 2019. pp. 510-517 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A Soft STAPLE Algorithm Combined with Anatomical Knowledge",

abstract = "Supervised machine learning algorithms, especially in the medical domain, are affected by considerable ambiguity in expert markings. In this study we address the case where the experts{\textquoteright} opinion is obtained as a distribution over the possible values. We propose a soft version of the STAPLE algorithm for experts{\textquoteright} markings fusion that can handle soft values. The algorithm was applied to obtain consensus from soft Multiple Sclerosis (MS) segmentation masks. Soft MS segmentations are constructed from manual binary delineations by including lesion surrounding voxels in the segmentation mask with a reduced confidence weight. We suggest that these voxels contain additional anatomical information about the lesion structure. The fused masks are utilized as ground truth mask to train a Fully Convolutional Neural Network (FCNN). The proposed method was evaluated on the MICCAI 2016 challenge dataset, and yields improved precision-recall tradeoff and a higher average Dice similarity coefficient.",

keywords = "MS lesion segmentation, STAPLE algorithm, Soft labels",

author = "Eytan Kats and Jacob Goldberger and Hayit Greenspan",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 17-10-2019",

year = "2019",

doi = "https://doi.org/10.1007/978-3-030-32248-9_57",

language = "الإنجليزيّة",

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

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editor = "Dinggang Shen and Pew-Thian Yap and Tianming Liu and Peters, {Terry M.} and Ali Khan and Staib, {Lawrence H.} and Caroline Essert and Sean Zhou",

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address = "ألمانيا",

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Kats, E, Goldberger, J & Greenspan, H 2019, A Soft STAPLE Algorithm Combined with Anatomical Knowledge. in D Shen, P-T Yap, T Liu, TM Peters, A Khan, LH Staib, C Essert & S Zhou (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11766 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 510-517, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, China, 13/10/19. https://doi.org/10.1007/978-3-030-32248-9_57

A Soft STAPLE Algorithm Combined with Anatomical Knowledge. / Kats, Eytan; Goldberger, Jacob ; Greenspan, Hayit.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. ed. / Dinggang Shen; Pew-Thian Yap; Tianming Liu; Terry M. Peters; Ali Khan; Lawrence H. Staib; Caroline Essert; Sean Zhou. Springer Science and Business Media Deutschland GmbH, 2019. p. 510-517 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11766 LNCS).

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

TY - GEN

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AU - Goldberger, Jacob

AU - Greenspan, Hayit

PY - 2019

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N2 - Supervised machine learning algorithms, especially in the medical domain, are affected by considerable ambiguity in expert markings. In this study we address the case where the experts’ opinion is obtained as a distribution over the possible values. We propose a soft version of the STAPLE algorithm for experts’ markings fusion that can handle soft values. The algorithm was applied to obtain consensus from soft Multiple Sclerosis (MS) segmentation masks. Soft MS segmentations are constructed from manual binary delineations by including lesion surrounding voxels in the segmentation mask with a reduced confidence weight. We suggest that these voxels contain additional anatomical information about the lesion structure. The fused masks are utilized as ground truth mask to train a Fully Convolutional Neural Network (FCNN). The proposed method was evaluated on the MICCAI 2016 challenge dataset, and yields improved precision-recall tradeoff and a higher average Dice similarity coefficient.

AB - Supervised machine learning algorithms, especially in the medical domain, are affected by considerable ambiguity in expert markings. In this study we address the case where the experts’ opinion is obtained as a distribution over the possible values. We propose a soft version of the STAPLE algorithm for experts’ markings fusion that can handle soft values. The algorithm was applied to obtain consensus from soft Multiple Sclerosis (MS) segmentation masks. Soft MS segmentations are constructed from manual binary delineations by including lesion surrounding voxels in the segmentation mask with a reduced confidence weight. We suggest that these voxels contain additional anatomical information about the lesion structure. The fused masks are utilized as ground truth mask to train a Fully Convolutional Neural Network (FCNN). The proposed method was evaluated on the MICCAI 2016 challenge dataset, and yields improved precision-recall tradeoff and a higher average Dice similarity coefficient.

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DO - https://doi.org/10.1007/978-3-030-32248-9_57

M3 - منشور من مؤتمر

SN - 9783030322472

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

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EP - 517

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings

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A2 - Yap, Pew-Thian

A2 - Liu, Tianming

A2 - Peters, Terry M.

A2 - Khan, Ali

A2 - Staib, Lawrence H.

A2 - Essert, Caroline

A2 - Zhou, Sean

PB - Springer Science and Business Media Deutschland GmbH

T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

Y2 - 13 October 2019 through 17 October 2019

ER -

Kats E, Goldberger J , Greenspan H. A Soft STAPLE Algorithm Combined with Anatomical Knowledge. In Shen D, Yap P-T, Liu T, Peters TM, Khan A, Staib LH, Essert C, Zhou S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2019. p. 510-517. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: https://doi.org/10.1007/978-3-030-32248-9_57

A Soft STAPLE Algorithm Combined with Anatomical Knowledge

Abstract

Publication series

Conference

Keywords

All Science Journal Classification (ASJC) codes

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