Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-Specific Atlas Maps

Samuel Budd; Matthew Sinclair; Thomas Day; Athanasios Vlontzos; Jeremy Tan; Tianrui Liu; Jacqueline Matthew; Emily Skelton; John Simpson; Reza Razavi; Ben Glocker; Daniel Rueckert; Emma C. Robinson; Bernhard Kainz

doi:10.1007/978-3-030-87234-2_20

Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-Specific Atlas Maps

Samuel Budd^*, Matthew Sinclair, Thomas Day, Athanasios Vlontzos, Jeremy Tan, Tianrui Liu, Jacqueline Matthew, Emily Skelton, John Simpson, Reza Razavi, Ben Glocker, Daniel Rueckert, Emma C. Robinson, Bernhard Kainz

^*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill required to diagnose such malformations from live ultrasound during examination is high and resources for screening are often limited. We present an interpretable, atlas-learning segmentation method for automatic diagnosis of Hypo-plastic Left Heart Syndrome (HLHS) from a single ‘4 Chamber Heart’ view image. We propose to extend the recently introduced Image-and-Spatial Transformer Networks (Atlas-ISTN) into a framework that enables sensitising atlas generation to disease. In this framework we can jointly learn image segmentation, registration, atlas construction and disease prediction while providing a maximum level of clinical interpretability compared to direct image classification methods. As a result our segmentation allows diagnoses competitive with expert-derived manual diagnosis and yields an AUC-ROC of 0.978 (1043 cases for training, 260 for validation and 325 for testing).

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
Editors	Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	207-217
Number of pages	11
ISBN (Print)	9783030872335
DOIs	https://doi.org/10.1007/978-3-030-87234-2_20
Publication status	Published - 2021
Externally published	Yes
Event	24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online Duration: 27 Sep 2021 → 1 Oct 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12907 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
City	Virtual, Online
Period	27/09/21 → 1/10/21

Bibliographical note

Funding Information:
Acknowledgements. We thank the volunteers and sonographers at St. Thomas’ Hospital London. The work of E.C.R. was supported by the Academy of Medical Sciences/the British Heart Foundation/the Government Department of Business, Energy and Industrial Strategy/the Wellcome Trust Springboard Award [SBF003/1116]. We also gratefully acknowledge financial support from the Wellcome Trust IEH 102431, EPSRC (EP/S022104/1, EP/S013687/1), EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z], the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London and supported by the NIHR Clinical Research Facility (CRF) at Guy’s and St Thomas’, and Nvidia GPU donations.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Atlas
Segmentation
Ultrasound

Access to Document

10.1007/978-3-030-87234-2_20

Cite this

Budd, S., Sinclair, M., Day, T., Vlontzos, A., Tan, J., Liu, T., Matthew, J., Skelton, E., Simpson, J., Razavi, R., Glocker, B., Rueckert, D., Robinson, E. C., & Kainz, B. (2021). Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-Specific Atlas Maps. In M. de Bruijne, P. C. Cattin, S. Cotin, N. Padoy, S. Speidel, Y. Zheng, & C. Essert (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings (pp. 207-217). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12907 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-87234-2_20

Budd, Samuel ; Sinclair, Matthew ; Day, Thomas ; Vlontzos, Athanasios ; Tan, Jeremy ; Liu, Tianrui ; Matthew, Jacqueline ; Skelton, Emily ; Simpson, John ; Razavi, Reza ; Glocker, Ben ; Rueckert, Daniel ; Robinson, Emma C. ; Kainz, Bernhard. / Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-Specific Atlas Maps. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings. editor / Marleen de Bruijne ; Philippe C. Cattin ; Stéphane Cotin ; Nicolas Padoy ; Stefanie Speidel ; Yefeng Zheng ; Caroline Essert. Springer Science and Business Media Deutschland GmbH, 2021. pp. 207-217 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill required to diagnose such malformations from live ultrasound during examination is high and resources for screening are often limited. We present an interpretable, atlas-learning segmentation method for automatic diagnosis of Hypo-plastic Left Heart Syndrome (HLHS) from a single {\textquoteleft}4 Chamber Heart{\textquoteright} view image. We propose to extend the recently introduced Image-and-Spatial Transformer Networks (Atlas-ISTN) into a framework that enables sensitising atlas generation to disease. In this framework we can jointly learn image segmentation, registration, atlas construction and disease prediction while providing a maximum level of clinical interpretability compared to direct image classification methods. As a result our segmentation allows diagnoses competitive with expert-derived manual diagnosis and yields an AUC-ROC of 0.978 (1043 cases for training, 260 for validation and 325 for testing).",

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author = "Samuel Budd and Matthew Sinclair and Thomas Day and Athanasios Vlontzos and Jeremy Tan and Tianrui Liu and Jacqueline Matthew and Emily Skelton and John Simpson and Reza Razavi and Ben Glocker and Daniel Rueckert and Robinson, {Emma C.} and Bernhard Kainz",

note = "Funding Information: Acknowledgements. We thank the volunteers and sonographers at St. Thomas{\textquoteright} Hospital London. The work of E.C.R. was supported by the Academy of Medical Sciences/the British Heart Foundation/the Government Department of Business, Energy and Industrial Strategy/the Wellcome Trust Springboard Award [SBF003/1116]. We also gratefully acknowledge financial support from the Wellcome Trust IEH 102431, EPSRC (EP/S022104/1, EP/S013687/1), EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z], the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) based at Guy{\textquoteright}s and St Thomas{\textquoteright} NHS Foundation Trust and King{\textquoteright}s College London and supported by the NIHR Clinical Research Facility (CRF) at Guy{\textquoteright}s and St Thomas{\textquoteright}, and Nvidia GPU donations. Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 ; Conference date: 27-09-2021 Through 01-10-2021",

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doi = "10.1007/978-3-030-87234-2_20",

language = "English",

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publisher = "Springer Science and Business Media Deutschland GmbH",

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editor = "{de Bruijne}, Marleen and Cattin, {Philippe C.} and St{\'e}phane Cotin and Nicolas Padoy and Stefanie Speidel and Yefeng Zheng and Caroline Essert",

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Budd, S, Sinclair, M, Day, T, Vlontzos, A, Tan, J, Liu, T, Matthew, J, Skelton, E, Simpson, J, Razavi, R, Glocker, B, Rueckert, D, Robinson, EC & Kainz, B 2021, Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-Specific Atlas Maps. in M de Bruijne, PC Cattin, S Cotin, N Padoy, S Speidel, Y Zheng & C Essert (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12907 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 207-217, 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online, 27/09/21. https://doi.org/10.1007/978-3-030-87234-2_20

Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-Specific Atlas Maps. / Budd, Samuel; Sinclair, Matthew; Day, Thomas; Vlontzos, Athanasios; Tan, Jeremy; Liu, Tianrui; Matthew, Jacqueline; Skelton, Emily; Simpson, John; Razavi, Reza; Glocker, Ben; Rueckert, Daniel; Robinson, Emma C.; Kainz, Bernhard.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings. ed. / Marleen de Bruijne; Philippe C. Cattin; Stéphane Cotin; Nicolas Padoy; Stefanie Speidel; Yefeng Zheng; Caroline Essert. Springer Science and Business Media Deutschland GmbH, 2021. p. 207-217 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12907 LNCS).

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

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AU - Sinclair, Matthew

AU - Day, Thomas

AU - Vlontzos, Athanasios

AU - Tan, Jeremy

AU - Liu, Tianrui

AU - Matthew, Jacqueline

AU - Skelton, Emily

AU - Simpson, John

AU - Razavi, Reza

AU - Glocker, Ben

AU - Rueckert, Daniel

AU - Robinson, Emma C.

AU - Kainz, Bernhard

N1 - Funding Information: Acknowledgements. We thank the volunteers and sonographers at St. Thomas’ Hospital London. The work of E.C.R. was supported by the Academy of Medical Sciences/the British Heart Foundation/the Government Department of Business, Energy and Industrial Strategy/the Wellcome Trust Springboard Award [SBF003/1116]. We also gratefully acknowledge financial support from the Wellcome Trust IEH 102431, EPSRC (EP/S022104/1, EP/S013687/1), EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z], the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London and supported by the NIHR Clinical Research Facility (CRF) at Guy’s and St Thomas’, and Nvidia GPU donations. Publisher Copyright: © 2021, Springer Nature Switzerland AG.

PY - 2021

Y1 - 2021

N2 - Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill required to diagnose such malformations from live ultrasound during examination is high and resources for screening are often limited. We present an interpretable, atlas-learning segmentation method for automatic diagnosis of Hypo-plastic Left Heart Syndrome (HLHS) from a single ‘4 Chamber Heart’ view image. We propose to extend the recently introduced Image-and-Spatial Transformer Networks (Atlas-ISTN) into a framework that enables sensitising atlas generation to disease. In this framework we can jointly learn image segmentation, registration, atlas construction and disease prediction while providing a maximum level of clinical interpretability compared to direct image classification methods. As a result our segmentation allows diagnoses competitive with expert-derived manual diagnosis and yields an AUC-ROC of 0.978 (1043 cases for training, 260 for validation and 325 for testing).

AB - Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill required to diagnose such malformations from live ultrasound during examination is high and resources for screening are often limited. We present an interpretable, atlas-learning segmentation method for automatic diagnosis of Hypo-plastic Left Heart Syndrome (HLHS) from a single ‘4 Chamber Heart’ view image. We propose to extend the recently introduced Image-and-Spatial Transformer Networks (Atlas-ISTN) into a framework that enables sensitising atlas generation to disease. In this framework we can jointly learn image segmentation, registration, atlas construction and disease prediction while providing a maximum level of clinical interpretability compared to direct image classification methods. As a result our segmentation allows diagnoses competitive with expert-derived manual diagnosis and yields an AUC-ROC of 0.978 (1043 cases for training, 260 for validation and 325 for testing).

KW - Atlas

KW - Segmentation

KW - Ultrasound

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U2 - 10.1007/978-3-030-87234-2_20

DO - 10.1007/978-3-030-87234-2_20

M3 - Conference contribution

AN - SCOPUS:85116499913

SN - 9783030872335

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

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EP - 217

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings

A2 - de Bruijne, Marleen

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A2 - Cotin, Stéphane

A2 - Padoy, Nicolas

A2 - Speidel, Stefanie

A2 - Zheng, Yefeng

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ER -

Budd S, Sinclair M, Day T, Vlontzos A, Tan J, Liu T et al. Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-Specific Atlas Maps. In de Bruijne M, Cattin PC, Cotin S, Padoy N, Speidel S, Zheng Y, Essert C, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 207-217. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-87234-2_20

Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-Specific Atlas Maps

Abstract

Publication series

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Bibliographical note

Keywords

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