Guiding 3D U-nets with signed distance fields for creating 3D models from images

Kristine Aavild Juhl, Rasmus Reinhold Paulsen, Anders Bjorholm Dahl, Vedrana Andersen Dahl, Ole De Backer, Klaus Fuglsang Kofoed, Oscar Camara

Research output: Contribution to conferencePaperResearchpeer-review

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Abstract

Morphological analysis of the left atrial appendage is an important tool to assess risk of ischemic stroke. Most deep learning approaches for 3D segmentation is guided by binary labelmaps, which results in voxelized segmentations unsuitable for morphological analysis. We propose to use signed distance fields to guide a deep network towards morphologically consistent 3D models. The proposed strategy is evaluated on a synthetic dataset of simple geometries, as well as a set of cardiac computed tomography images containing the left atrial appendage. The proposed method produces smooth surfaces with a closer resemblance to the true surface in terms of segmentation overlap and surface distance.
Original languageEnglish
Publication date2019
Number of pages4
Publication statusPublished - 2019
EventMedical Imaging with Deep Learning 2019 - Imperial College’s South Kensington Campus, London, United Kingdom
Duration: 8 Jul 201910 Jul 2019
http://2019.midl.io/

Conference

ConferenceMedical Imaging with Deep Learning 2019
LocationImperial College’s South Kensington Campus
CountryUnited Kingdom
CityLondon
Period08/07/201910/07/2019
Internet address

Bibliographical note

https://openreview.net/group?id=MIDL.io/2019/Conference/Abstract

Keywords

  • Signed distance fields
  • Pixel-wise regression
  • Left atrial appendage

Cite this

Juhl, K. A., Paulsen, R. R., Dahl, A. B., Dahl, V. A., De Backer, O., Kofoed, K. F., & Camara, O. (2019). Guiding 3D U-nets with signed distance fields for creating 3D models from images. Paper presented at Medical Imaging with Deep Learning 2019, London, United Kingdom.
Juhl, Kristine Aavild ; Paulsen, Rasmus Reinhold ; Dahl, Anders Bjorholm ; Dahl, Vedrana Andersen ; De Backer, Ole ; Kofoed, Klaus Fuglsang ; Camara, Oscar. / Guiding 3D U-nets with signed distance fields for creating 3D models from images. Paper presented at Medical Imaging with Deep Learning 2019, London, United Kingdom.4 p.
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title = "Guiding 3D U-nets with signed distance fields for creating 3D models from images",
abstract = "Morphological analysis of the left atrial appendage is an important tool to assess risk of ischemic stroke. Most deep learning approaches for 3D segmentation is guided by binary labelmaps, which results in voxelized segmentations unsuitable for morphological analysis. We propose to use signed distance fields to guide a deep network towards morphologically consistent 3D models. The proposed strategy is evaluated on a synthetic dataset of simple geometries, as well as a set of cardiac computed tomography images containing the left atrial appendage. The proposed method produces smooth surfaces with a closer resemblance to the true surface in terms of segmentation overlap and surface distance.",
keywords = "Signed distance fields, Pixel-wise regression, Left atrial appendage",
author = "Juhl, {Kristine Aavild} and Paulsen, {Rasmus Reinhold} and Dahl, {Anders Bjorholm} and Dahl, {Vedrana Andersen} and {De Backer}, Ole and Kofoed, {Klaus Fuglsang} and Oscar Camara",
note = "https://openreview.net/group?id=MIDL.io/2019/Conference/Abstract; Medical Imaging with Deep Learning 2019, MIDL 2019 ; Conference date: 08-07-2019 Through 10-07-2019",
year = "2019",
language = "English",
url = "http://2019.midl.io/",

}

Juhl, KA, Paulsen, RR, Dahl, AB, Dahl, VA, De Backer, O, Kofoed, KF & Camara, O 2019, 'Guiding 3D U-nets with signed distance fields for creating 3D models from images' Paper presented at Medical Imaging with Deep Learning 2019, London, United Kingdom, 08/07/2019 - 10/07/2019, .

Guiding 3D U-nets with signed distance fields for creating 3D models from images. / Juhl, Kristine Aavild; Paulsen, Rasmus Reinhold; Dahl, Anders Bjorholm; Dahl, Vedrana Andersen; De Backer, Ole; Kofoed, Klaus Fuglsang; Camara, Oscar.

2019. Paper presented at Medical Imaging with Deep Learning 2019, London, United Kingdom.

Research output: Contribution to conferencePaperResearchpeer-review

TY - CONF

T1 - Guiding 3D U-nets with signed distance fields for creating 3D models from images

AU - Juhl, Kristine Aavild

AU - Paulsen, Rasmus Reinhold

AU - Dahl, Anders Bjorholm

AU - Dahl, Vedrana Andersen

AU - De Backer, Ole

AU - Kofoed, Klaus Fuglsang

AU - Camara, Oscar

N1 - https://openreview.net/group?id=MIDL.io/2019/Conference/Abstract

PY - 2019

Y1 - 2019

N2 - Morphological analysis of the left atrial appendage is an important tool to assess risk of ischemic stroke. Most deep learning approaches for 3D segmentation is guided by binary labelmaps, which results in voxelized segmentations unsuitable for morphological analysis. We propose to use signed distance fields to guide a deep network towards morphologically consistent 3D models. The proposed strategy is evaluated on a synthetic dataset of simple geometries, as well as a set of cardiac computed tomography images containing the left atrial appendage. The proposed method produces smooth surfaces with a closer resemblance to the true surface in terms of segmentation overlap and surface distance.

AB - Morphological analysis of the left atrial appendage is an important tool to assess risk of ischemic stroke. Most deep learning approaches for 3D segmentation is guided by binary labelmaps, which results in voxelized segmentations unsuitable for morphological analysis. We propose to use signed distance fields to guide a deep network towards morphologically consistent 3D models. The proposed strategy is evaluated on a synthetic dataset of simple geometries, as well as a set of cardiac computed tomography images containing the left atrial appendage. The proposed method produces smooth surfaces with a closer resemblance to the true surface in terms of segmentation overlap and surface distance.

KW - Signed distance fields

KW - Pixel-wise regression

KW - Left atrial appendage

M3 - Paper

ER -

Juhl KA, Paulsen RR, Dahl AB, Dahl VA, De Backer O, Kofoed KF et al. Guiding 3D U-nets with signed distance fields for creating 3D models from images. 2019. Paper presented at Medical Imaging with Deep Learning 2019, London, United Kingdom.