Deep Learning from Label Proportions for Emphysema Quantification

Gerda Bortsova; Florian Dubost; Silas Ørting; Ioannis Katramados; Laurens Hogeweg; Laura Thomsen; Mathilde Wille; Marleen de Bruijne

doi:10.1007/978-3-030-00934-2_85

Deep Learning from Label Proportions for Emphysema Quantification

Gerda Bortsova^*, Florian Dubost, Silas Ørting, Ioannis Katramados, Laurens Hogeweg, Laura Thomsen, Mathilde Wille, Marleen de Bruijne

^*Corresponding author af dette arbejde

5 Citationer (Scopus)

Abstract

We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue. These proportions were visually estimated by experts using a standard grading system, in which grades correspond to intervals (label example: 1–5% of diseased tissue). The proposed architecture encodes the knowledge that the labels represent a volumetric proportion. A custom loss is designed to learn with intervals. Thus, during training, our network learns to segment the diseased tissue such that its proportions fit the ground truth intervals. Our architecture and loss combined improve the performance substantially (8% ICC) compared to a more conventional regression network. We outperform traditional lung densitometry and two recently published methods for emphysema quantification by a large margin (at least 7% AUC and 15% ICC), and achieve near-human-level performance. Moreover, our method generates emphysema segmentations that predict the spatial distribution of emphysema at human level.

Originalsprog	Engelsk
Titel	Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 : 21st International Conference, 2018, Proceedings Part II
Redaktører	Alejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger
Forlag	Springer
Publikationsdato	2018
Sider	768-776
ISBN (Trykt)	9783030009335
DOI	https://doi.org/10.1007/978-3-030-00934-2_85
Status	Udgivet - 2018
Begivenhed	21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spanien Varighed: 16 sep. 2018 → 20 sep. 2018

Konference

Konference	21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Land/Område	Spanien
By	Granada
Periode	16/09/2018 → 20/09/2018

Navn	Lecture notes in computer science
Vol/bind	11071
ISSN	0302-9743

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10.1007/978-3-030-00934-2_85

https://arxiv.org/pdf/1807.08601.pdfLicens: Andet

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Citationsformater

Bortsova, G., Dubost, F., Ørting, S., Katramados, I., Hogeweg, L., Thomsen, L., Wille, M., & de Bruijne, M. (2018). Deep Learning from Label Proportions for Emphysema Quantification. I A. F. Frangi, J. A. Schnabel, C. Davatzikos, C. Alberola-López, & G. Fichtinger (red.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, 2018, Proceedings Part II (s. 768-776). Springer. https://doi.org/10.1007/978-3-030-00934-2_85

Deep Learning from Label Proportions for Emphysema Quantification. / Bortsova, Gerda; Dubost, Florian; Ørting, Silas et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, 2018, Proceedings Part II. red. / Alejandro F. Frangi; Julia A. Schnabel; Christos Davatzikos; Carlos Alberola-López; Gabor Fichtinger. Springer, 2018. s. 768-776 (Lecture notes in computer science, Bind 11071).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

Bortsova, G, Dubost, F, Ørting, S, Katramados, I, Hogeweg, L, Thomsen, L, Wille, M & de Bruijne, M 2018, Deep Learning from Label Proportions for Emphysema Quantification. i AF Frangi, JA Schnabel, C Davatzikos, C Alberola-López & G Fichtinger (red), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, 2018, Proceedings Part II. Springer, Lecture notes in computer science, bind 11071, s. 768-776, 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, Spanien, 16/09/2018. https://doi.org/10.1007/978-3-030-00934-2_85

Bortsova G, Dubost F, Ørting S, Katramados I, Hogeweg L, Thomsen L et al. Deep Learning from Label Proportions for Emphysema Quantification. I Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G, red., Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, 2018, Proceedings Part II. Springer. 2018. s. 768-776. (Lecture notes in computer science, Bind 11071). doi: 10.1007/978-3-030-00934-2_85

Bortsova, Gerda ; Dubost, Florian ; Ørting, Silas et al. / Deep Learning from Label Proportions for Emphysema Quantification. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, 2018, Proceedings Part II. red. / Alejandro F. Frangi ; Julia A. Schnabel ; Christos Davatzikos ; Carlos Alberola-López ; Gabor Fichtinger. Springer, 2018. s. 768-776 (Lecture notes in computer science, Bind 11071).

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title = "Deep Learning from Label Proportions for Emphysema Quantification",

abstract = "We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue. These proportions were visually estimated by experts using a standard grading system, in which grades correspond to intervals (label example: 1–5% of diseased tissue). The proposed architecture encodes the knowledge that the labels represent a volumetric proportion. A custom loss is designed to learn with intervals. Thus, during training, our network learns to segment the diseased tissue such that its proportions fit the ground truth intervals. Our architecture and loss combined improve the performance substantially (8% ICC) compared to a more conventional regression network. We outperform traditional lung densitometry and two recently published methods for emphysema quantification by a large margin (at least 7% AUC and 15% ICC), and achieve near-human-level performance. Moreover, our method generates emphysema segmentations that predict the spatial distribution of emphysema at human level.",

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author = "Gerda Bortsova and Florian Dubost and Silas {\O}rting and Ioannis Katramados and Laurens Hogeweg and Laura Thomsen and Mathilde Wille and {de Bruijne}, Marleen",

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AU - Thomsen, Laura

AU - Wille, Mathilde

AU - de Bruijne, Marleen

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N2 - We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue. These proportions were visually estimated by experts using a standard grading system, in which grades correspond to intervals (label example: 1–5% of diseased tissue). The proposed architecture encodes the knowledge that the labels represent a volumetric proportion. A custom loss is designed to learn with intervals. Thus, during training, our network learns to segment the diseased tissue such that its proportions fit the ground truth intervals. Our architecture and loss combined improve the performance substantially (8% ICC) compared to a more conventional regression network. We outperform traditional lung densitometry and two recently published methods for emphysema quantification by a large margin (at least 7% AUC and 15% ICC), and achieve near-human-level performance. Moreover, our method generates emphysema segmentations that predict the spatial distribution of emphysema at human level.

AB - We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue. These proportions were visually estimated by experts using a standard grading system, in which grades correspond to intervals (label example: 1–5% of diseased tissue). The proposed architecture encodes the knowledge that the labels represent a volumetric proportion. A custom loss is designed to learn with intervals. Thus, during training, our network learns to segment the diseased tissue such that its proportions fit the ground truth intervals. Our architecture and loss combined improve the performance substantially (8% ICC) compared to a more conventional regression network. We outperform traditional lung densitometry and two recently published methods for emphysema quantification by a large margin (at least 7% AUC and 15% ICC), and achieve near-human-level performance. Moreover, our method generates emphysema segmentations that predict the spatial distribution of emphysema at human level.

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Deep Learning from Label Proportions for Emphysema Quantification

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