Deep Shape Analysis on Abdominal Organs for Diabetes Prediction

Benjamín Gutiérrez-Becker; Sergios Gatidis; Daniel Gutmann; Annette Peters; Christopher Schlett; Fabian Bamberg; Christian Wachinger

doi:10.1007/978-3-030-04747-4_21

Deep Shape Analysis on Abdominal Organs for Diabetes Prediction

Benjamín Gutiérrez-Becker
, Sergios Gatidis
, Daniel Gutmann
, Annette Peters
, Christopher Schlett
, Fabian Bamberg
, Christian Wachinger

Ludwig-Maximilians-Universität München
University of Tübingen
Helmholtz Zentrum München German Research Center for Environmental Health
Universitätsklinikum Heidelberg

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

1 Scopus citations

Abstract

Morphological analysis of organs based on images is a key task in medical imaging computing. Several approaches have been proposed for the quantitative assessment of morphological changes, and they have been widely used for the analysis of the effects of aging, disease and other factors in organ morphology. In this work, we propose a deep neural network for predicting diabetes on abdominal shapes. The network directly operates on raw point clouds without requiring mesh processing or shape alignment. Instead of relying on hand-crafted shape descriptors, an optimal representation is learned in the end-to-end training stage of the network. For comparison, we extend the state-of-the-art shape descriptor BrainPrint to the AbdomenPrint. Our results demonstrate that the network learns shape representations that better separates healthy and diabetic individuals than traditional representations.

Original language	English
Title of host publication	Shape in Medical Imaging - International Workshop, ShapeMI 2018, Held in Conjunction with MICCAI 2018, Proceedings
Editors	Hervé Lombaert, Beatriz Paniagua, Bernhard Egger, Marcel Lüthi, Martin Reuter, Christian Wachinger
Publisher	Springer Verlag
Pages	223-231
Number of pages	9
ISBN (Print)	9783030047467
DOIs	https://doi.org/10.1007/978-3-030-04747-4_21
State	Published - 2018
Externally published	Yes
Event	International Workshop on Shape in Medical Imaging, ShapeMI 2018 held in conjunction with 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 20 Sep 2018 → 20 Sep 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11167 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Workshop on Shape in Medical Imaging, ShapeMI 2018 held in conjunction with 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Country/Territory	Spain
City	Granada
Period	20/09/18 → 20/09/18

UN SDGs

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

SDG 3 Good Health and Well-being

Access to Document

10.1007/978-3-030-04747-4_21

Cite this

Gutiérrez-Becker, B., Gatidis, S., Gutmann, D., Peters, A., Schlett, C., Bamberg, F., & Wachinger, C. (2018). Deep Shape Analysis on Abdominal Organs for Diabetes Prediction. In H. Lombaert, B. Paniagua, B. Egger, M. Lüthi, M. Reuter, & C. Wachinger (Eds.), Shape in Medical Imaging - International Workshop, ShapeMI 2018, Held in Conjunction with MICCAI 2018, Proceedings (pp. 223-231). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11167 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-04747-4_21

Gutiérrez-Becker, Benjamín ; Gatidis, Sergios ; Gutmann, Daniel et al. / Deep Shape Analysis on Abdominal Organs for Diabetes Prediction. Shape in Medical Imaging - International Workshop, ShapeMI 2018, Held in Conjunction with MICCAI 2018, Proceedings. editor / Hervé Lombaert ; Beatriz Paniagua ; Bernhard Egger ; Marcel Lüthi ; Martin Reuter ; Christian Wachinger. Springer Verlag, 2018. pp. 223-231 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{c888ee22935a4a3893cbd95d7701aa92,

title = "Deep Shape Analysis on Abdominal Organs for Diabetes Prediction",

abstract = "Morphological analysis of organs based on images is a key task in medical imaging computing. Several approaches have been proposed for the quantitative assessment of morphological changes, and they have been widely used for the analysis of the effects of aging, disease and other factors in organ morphology. In this work, we propose a deep neural network for predicting diabetes on abdominal shapes. The network directly operates on raw point clouds without requiring mesh processing or shape alignment. Instead of relying on hand-crafted shape descriptors, an optimal representation is learned in the end-to-end training stage of the network. For comparison, we extend the state-of-the-art shape descriptor BrainPrint to the AbdomenPrint. Our results demonstrate that the network learns shape representations that better separates healthy and diabetic individuals than traditional representations.",

author = "Benjam{\'i}n Guti{\'e}rrez-Becker and Sergios Gatidis and Daniel Gutmann and Annette Peters and Christopher Schlett and Fabian Bamberg and Christian Wachinger",

note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; International Workshop on Shape in Medical Imaging, ShapeMI 2018 held in conjunction with 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 ; Conference date: 20-09-2018 Through 20-09-2018",

year = "2018",

doi = "10.1007/978-3-030-04747-4\_21",

language = "English",

isbn = "9783030047467",

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

publisher = "Springer Verlag",

pages = "223--231",

editor = "Herv{\'e} Lombaert and Beatriz Paniagua and Bernhard Egger and Marcel L{\"u}thi and Martin Reuter and Christian Wachinger",

booktitle = "Shape in Medical Imaging - International Workshop, ShapeMI 2018, Held in Conjunction with MICCAI 2018, Proceedings",

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Gutiérrez-Becker, B, Gatidis, S, Gutmann, D, Peters, A, Schlett, C, Bamberg, F & Wachinger, C 2018, Deep Shape Analysis on Abdominal Organs for Diabetes Prediction. in H Lombaert, B Paniagua, B Egger, M Lüthi, M Reuter & C Wachinger (eds), Shape in Medical Imaging - International Workshop, ShapeMI 2018, Held in Conjunction with MICCAI 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11167 LNCS, Springer Verlag, pp. 223-231, International Workshop on Shape in Medical Imaging, ShapeMI 2018 held in conjunction with 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, Spain, 20/09/18. https://doi.org/10.1007/978-3-030-04747-4_21

Deep Shape Analysis on Abdominal Organs for Diabetes Prediction. / Gutiérrez-Becker, Benjamín; Gatidis, Sergios; Gutmann, Daniel et al.
Shape in Medical Imaging - International Workshop, ShapeMI 2018, Held in Conjunction with MICCAI 2018, Proceedings. ed. / Hervé Lombaert; Beatriz Paniagua; Bernhard Egger; Marcel Lüthi; Martin Reuter; Christian Wachinger. Springer Verlag, 2018. p. 223-231 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11167 LNCS).

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

TY - GEN

T1 - Deep Shape Analysis on Abdominal Organs for Diabetes Prediction

AU - Gutiérrez-Becker, Benjamín

AU - Gatidis, Sergios

AU - Gutmann, Daniel

AU - Peters, Annette

AU - Schlett, Christopher

AU - Bamberg, Fabian

AU - Wachinger, Christian

PY - 2018

Y1 - 2018

N2 - Morphological analysis of organs based on images is a key task in medical imaging computing. Several approaches have been proposed for the quantitative assessment of morphological changes, and they have been widely used for the analysis of the effects of aging, disease and other factors in organ morphology. In this work, we propose a deep neural network for predicting diabetes on abdominal shapes. The network directly operates on raw point clouds without requiring mesh processing or shape alignment. Instead of relying on hand-crafted shape descriptors, an optimal representation is learned in the end-to-end training stage of the network. For comparison, we extend the state-of-the-art shape descriptor BrainPrint to the AbdomenPrint. Our results demonstrate that the network learns shape representations that better separates healthy and diabetic individuals than traditional representations.

AB - Morphological analysis of organs based on images is a key task in medical imaging computing. Several approaches have been proposed for the quantitative assessment of morphological changes, and they have been widely used for the analysis of the effects of aging, disease and other factors in organ morphology. In this work, we propose a deep neural network for predicting diabetes on abdominal shapes. The network directly operates on raw point clouds without requiring mesh processing or shape alignment. Instead of relying on hand-crafted shape descriptors, an optimal representation is learned in the end-to-end training stage of the network. For comparison, we extend the state-of-the-art shape descriptor BrainPrint to the AbdomenPrint. Our results demonstrate that the network learns shape representations that better separates healthy and diabetic individuals than traditional representations.

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DO - 10.1007/978-3-030-04747-4_21

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AN - SCOPUS:85057380560

SN - 9783030047467

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

SP - 223

EP - 231

BT - Shape in Medical Imaging - International Workshop, ShapeMI 2018, Held in Conjunction with MICCAI 2018, Proceedings

A2 - Lombaert, Hervé

A2 - Paniagua, Beatriz

A2 - Egger, Bernhard

A2 - Lüthi, Marcel

A2 - Reuter, Martin

A2 - Wachinger, Christian

PB - Springer Verlag

T2 - International Workshop on Shape in Medical Imaging, ShapeMI 2018 held in conjunction with 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018

Y2 - 20 September 2018 through 20 September 2018

ER -

Gutiérrez-Becker B, Gatidis S, Gutmann D, Peters A, Schlett C, Bamberg F et al. Deep Shape Analysis on Abdominal Organs for Diabetes Prediction. In Lombaert H, Paniagua B, Egger B, Lüthi M, Reuter M, Wachinger C, editors, Shape in Medical Imaging - International Workshop, ShapeMI 2018, Held in Conjunction with MICCAI 2018, Proceedings. Springer Verlag. 2018. p. 223-231. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-04747-4_21

Deep Shape Analysis on Abdominal Organs for Diabetes Prediction

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