Deep Nested level sets: fully automated segmentation of cardiac MR images in patients with pulmonary hypertension

Jinming Duan, Jo Schlemper, Wenjia Bai, Timothy J.W. Dawes, Ghalib Bello, Georgia Doumou, Antonio De Marvao, Declan P. O’Regan, Daniel Rueckert

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

In this paper we introduce a novel and accurate optimisation method for segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension (PH). The proposed method explicitly takes into account the image features learned from a deep neural network. To this end, we estimate simultaneous probability maps over region and edge locations in CMR images using a fully convolutional network. Due to the distinct morphology of the heart in patients with PH, these probability maps can then be incorporated in a single nested level set optimisation framework to achieve multi-region segmentation with high efficiency. The proposed method uses an automatic way for level set initialisation and thus the whole optimisation is fully automated. We demonstrate that the proposed deep nested level set (DNLS) method outperforms existing state-of-the-art methods for CMR segmentation in PH patients.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018
Subtitle of host publication21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part IV
EditorsAlejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger
PublisherSpringer
Pages595-603
Number of pages9
Edition1
ISBN (Electronic)9783030009373
ISBN (Print)9783030009366
DOIs
Publication statusPublished - 13 Sept 2018
Event21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: 16 Sept 201820 Sept 2018

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11073
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Country/TerritorySpain
CityGranada
Period16/09/1820/09/18

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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