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Multi-label Whole Heart Segmentation Using CNNs and Anatomical Label Configurations

  • Christian Payer
  • Darko Štern
  • Horst Bischof
  • Martin Urschler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10663)

Abstract

We propose a pipeline of two fully convolutional networks for automatic multi-label whole heart segmentation from CT and MRI volumes. At first, a convolutional neural network (CNN) localizes the center of the bounding box around all heart structures, such that the subsequent segmentation CNN can focus on this region. Trained in an end-to-end manner, the segmentation CNN transforms intermediate label predictions to positions of other labels. Thus, the network learns from the relative positions among labels and focuses on anatomically feasible configurations. Results on the MICCAI 2017 Multi-Modality Whole Heart Segmentation (MM-WHS) challenge show that the proposed architecture performs well on the provided CT and MRI training volumes, delivering in a three-fold cross validation an average Dice Similarity Coefficient over all heart substructures of 88.9% and 79.0%, respectively. Moreover, on the MM-WHS challenge test data we rank first for CT and second for MRI with a whole heart segmentation Dice score of 90.8% and 87%, respectively, leading to an overall first ranking among all participants.

Keywords

Heart Segmentation Multi-label Convolutional neural network Anatomical label configurations 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute for Computer Graphics and VisionGraz University of TechnologyGrazAustria
  2. 2.Ludwig Boltzmann Institute for Clinical Forensic ImagingGrazAustria
  3. 3.BioTechMed-GrazGrazAustria

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