Regressing Heatmaps for Multiple Landmark Localization Using CNNs

Christian Payer; Darko Stern; Horst Bischof; Martin Urschler

doi:10.1007/978-3-319-46723-8_27

Regressing Heatmaps for Multiple Landmark Localization Using CNNs

Christian Payer, Darko Stern, Horst Bischof, Martin Urschler

Institut für Maschinelles Sehen und Darstellen (7100)

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in Buch/Bericht › Begutachtung

Abstract

We explore the applicability of deep convolutional neural networks (CNNs) for multiple landmark localization in medical image data. Exploiting the idea of regressing heatmaps for individual landmark locations, we investigate several fully convolutional 2D and 3D CNN architectures by training them in an end-to-end manner. We further propose a novel SpatialConfiguration-Net architecture that effectively combines accurate local appearance responses with spatial landmark configurations that model anatomical variation. Evaluation of our different architectures on 2D and 3D hand image datasets show that heatmap regression based on CNNs achieves state-of-the-art landmark localization performance, with SpatialConfiguration-Net being robust even in case of limited amounts of training data.

Originalsprache	englisch
Titel	Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016
Untertitel	19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II
Redakteure/-innen	Sebastien Ourselin, Leo Joskowicz, Mert R. Sabuncu, Gozde Unal, William Wells
Herausgeber (Verlag)	Springer International Publishing AG
Seiten	230-238
Seitenumfang	9
Band	9901
ISBN (elektronisch)	978-3-319-46723-8
ISBN (Print)	978-3-319-46722-1
DOIs	https://doi.org/10.1007/978-3-319-46723-8_27
Publikationsstatus	Veröffentlicht - 21 Okt. 2016
Veranstaltung	19th International Conference on Medical Image Computing & Computer Assisted Intervention: MICCAI 2016 - Intercontinental Athenaeum, Athens, Griechenland Dauer: 17 Okt. 2016 → 21 Okt. 2016 http://www.miccai2016.org

Publikationsreihe

Name	Lecture Notes in Computer Science
Herausgeber (Verlag)	Springer

Konferenz

Konferenz	19th International Conference on Medical Image Computing & Computer Assisted Intervention
Kurztitel	MICCAI
Land/Gebiet	Griechenland
Ort	Athens
Zeitraum	17/10/16 → 21/10/16
Internetadresse	http://www.miccai2016.org

Fields of Expertise

Information, Communication & Computing

Kooperationen

BioTechMed-Graz

Zugriff auf Dokument

10.1007/978-3-319-46723-8_27

FWF - FAME - Vollautomatische MRT-basierte Altersschätzung von Jugendlichen
Bischof, H. & Urschler, M.
1/07/15 → 31/12/18
Projekt: Foschungsprojekt
Klinisch-Forensische Bildgebung- Standardisierung zur computerunterstützten Auswertung von medizinischen MRT-Daten - Standard_MRT
Urschler, M., Bornik, A. & Bischof, H.
1/12/08 → 31/05/15
Projekt: Foschungsprojekt

Student Travel Award
Payer, Christian (Empfänger/-in), 2016
Auszeichnung: Preise / Medaillen / Ehrungen

Dieses zitieren

Payer, C., Stern, D., Bischof, H., & Urschler, M. (2016). Regressing Heatmaps for Multiple Landmark Localization Using CNNs. in S. Ourselin, L. Joskowicz, M. R. Sabuncu, G. Unal, & W. Wells (Hrsg.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II (Band 9901, S. 230-238). (Lecture Notes in Computer Science). Springer International Publishing AG . https://doi.org/10.1007/978-3-319-46723-8_27

Regressing Heatmaps for Multiple Landmark Localization Using CNNs. / Payer, Christian; Stern, Darko; Bischof, Horst et al.

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II. Hrsg. / Sebastien Ourselin; Leo Joskowicz; Mert R. Sabuncu; Gozde Unal; William Wells. Band 9901 Springer International Publishing AG , 2016. S. 230-238 (Lecture Notes in Computer Science).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in Buch/Bericht › Begutachtung

Payer, C, Stern, D, Bischof, H & Urschler, M 2016, Regressing Heatmaps for Multiple Landmark Localization Using CNNs. in S Ourselin, L Joskowicz, MR Sabuncu, G Unal & W Wells (Hrsg.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II. Bd. 9901, Lecture Notes in Computer Science, Springer International Publishing AG , S. 230-238, 19th International Conference on Medical Image Computing & Computer Assisted Intervention, Athens, Griechenland, 17/10/16. https://doi.org/10.1007/978-3-319-46723-8_27

Payer C, Stern D, Bischof H, Urschler M. Regressing Heatmaps for Multiple Landmark Localization Using CNNs. in Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W, Hrsg., Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II. Band 9901. Springer International Publishing AG . 2016. S. 230-238. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-46723-8_27

Payer, Christian ; Stern, Darko ; Bischof, Horst et al. / Regressing Heatmaps for Multiple Landmark Localization Using CNNs. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II. Hrsg. / Sebastien Ourselin ; Leo Joskowicz ; Mert R. Sabuncu ; Gozde Unal ; William Wells. Band 9901 Springer International Publishing AG , 2016. S. 230-238 (Lecture Notes in Computer Science).

@inbook{6bc99be5c4004c8b90832556bf865de2,

title = "Regressing Heatmaps for Multiple Landmark Localization Using CNNs",

abstract = "We explore the applicability of deep convolutional neural networks (CNNs) for multiple landmark localization in medical image data. Exploiting the idea of regressing heatmaps for individual landmark locations, we investigate several fully convolutional 2D and 3D CNN architectures by training them in an end-to-end manner. We further propose a novel SpatialConfiguration-Net architecture that effectively combines accurate local appearance responses with spatial landmark configurations that model anatomical variation. Evaluation of our different architectures on 2D and 3D hand image datasets show that heatmap regression based on CNNs achieves state-of-the-art landmark localization performance, with SpatialConfiguration-Net being robust even in case of limited amounts of training data.",

author = "Christian Payer and Darko Stern and Horst Bischof and Martin Urschler",

year = "2016",

month = oct,

day = "21",

doi = "10.1007/978-3-319-46723-8_27",

language = "English",

isbn = "978-3-319-46722-1",

volume = "9901",

series = "Lecture Notes in Computer Science",

publisher = "Springer International Publishing AG ",

pages = "230--238",

editor = "Sebastien Ourselin and Leo Joskowicz and Sabuncu, {Mert R.} and Gozde Unal and William Wells",

booktitle = "Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016",

address = "Switzerland",

note = "19th International Conference on Medical Image Computing & Computer Assisted Intervention : MICCAI 2016, MICCAI ; Conference date: 17-10-2016 Through 21-10-2016",

url = "http://www.miccai2016.org",

}

TY - CHAP

T1 - Regressing Heatmaps for Multiple Landmark Localization Using CNNs

AU - Payer, Christian

AU - Stern, Darko

AU - Bischof, Horst

AU - Urschler, Martin

PY - 2016/10/21

Y1 - 2016/10/21

N2 - We explore the applicability of deep convolutional neural networks (CNNs) for multiple landmark localization in medical image data. Exploiting the idea of regressing heatmaps for individual landmark locations, we investigate several fully convolutional 2D and 3D CNN architectures by training them in an end-to-end manner. We further propose a novel SpatialConfiguration-Net architecture that effectively combines accurate local appearance responses with spatial landmark configurations that model anatomical variation. Evaluation of our different architectures on 2D and 3D hand image datasets show that heatmap regression based on CNNs achieves state-of-the-art landmark localization performance, with SpatialConfiguration-Net being robust even in case of limited amounts of training data.

AB - We explore the applicability of deep convolutional neural networks (CNNs) for multiple landmark localization in medical image data. Exploiting the idea of regressing heatmaps for individual landmark locations, we investigate several fully convolutional 2D and 3D CNN architectures by training them in an end-to-end manner. We further propose a novel SpatialConfiguration-Net architecture that effectively combines accurate local appearance responses with spatial landmark configurations that model anatomical variation. Evaluation of our different architectures on 2D and 3D hand image datasets show that heatmap regression based on CNNs achieves state-of-the-art landmark localization performance, with SpatialConfiguration-Net being robust even in case of limited amounts of training data.

U2 - 10.1007/978-3-319-46723-8_27

DO - 10.1007/978-3-319-46723-8_27

M3 - Chapter

SN - 978-3-319-46722-1

VL - 9901

T3 - Lecture Notes in Computer Science

SP - 230

EP - 238

BT - Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

A2 - Ourselin, Sebastien

A2 - Joskowicz, Leo

A2 - Sabuncu, Mert R.

A2 - Unal, Gozde

A2 - Wells, William

PB - Springer International Publishing AG

T2 - 19th International Conference on Medical Image Computing & Computer Assisted Intervention

Y2 - 17 October 2016 through 21 October 2016

ER -

Regressing Heatmaps for Multiple Landmark Localization Using CNNs

Abstract

Publikationsreihe

Konferenz

Fields of Expertise

Kooperationen

Zugriff auf Dokument

Fingerprint

Projekte

FWF - FAME - Vollautomatische MRT-basierte Altersschätzung von Jugendlichen

Klinisch-Forensische Bildgebung- Standardisierung zur computerunterstützten Auswertung von medizinischen MRT-Daten - Standard_MRT

Auszeichnungen

Student Travel Award

Dieses zitieren