research-article

Binary segmentation of medical images using implicit spline representations and deep learning

Authors:

Oliver J.D. Barrowclough,

Georg Muntingh,

Varatharajan Nainamalai,

Ivar StangebyAuthors Info & Claims

Volume 85, Issue C

https://doi.org/10.1016/j.cagd.2021.101972

Published: 01 February 2021 Publication History

Abstract

We propose a novel approach to image segmentation based on combining implicit spline representations with deep convolutional neural networks. This is done by predicting the control points of a bivariate spline function whose zero-set represents the segmentation boundary. We adapt several existing neural network architectures and design novel loss functions that are tailored towards providing implicit spline curve approximations. The method is evaluated on a congenital heart disease computed tomography medical imaging dataset. Experiments are carried out by measuring performance in various standard metrics for different networks and loss functions. We determine that splines of bidegree ( 1, 1 ) with 128 × 128 coefficient resolution performed optimally for 512 × 512 resolution CT images. For our best network, we achieve an average volumetric test Dice score of close to 92%, which reaches the state of the art for this congenital heart disease dataset.

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Index Terms

Binary segmentation of medical images using implicit spline representations and deep learning
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies

Index terms have been assigned to the content through auto-classification.

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cover image Computer Aided Geometric Design

Computer Aided Geometric Design Volume 85, Issue C

Feb 2021

127 pages

ISSN:0167-8396

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Elsevier Science Publishers B. V.

Netherlands

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Published: 01 February 2021

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