EfficientSeg: A Simple But Efficient Solution to Myocardial Pathology Segmentation Challenge

Jianpeng Zhang, Yutong Xie, Zhibin Liao, Johan Verjans, Yong Xia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Myocardial pathology segmentation is an essential but challenging task in the computer-aided diagnosis of myocardial infraction. Although deep convolutional neural networks (DCNNs) have achieved remarkable success in medical image segmentation, accurate segmentation of myocardial pathology remains challenging, due to the low soft-tissue contrast, irregularity of pathological targets, and limited training data. In this paper, we propose a simple but efficient DCNN model called EfficientSeg to segment the regions of edema and scar in multi-sequence cardiac magnetic resonance (CMR) data. In this model, the encoder uses EfficientNet as its backbone for feature extraction, and the decoder employs a weighted bi-directional feature pyramid network (BiFPN) to predict the segmentation mask. The former has a much improved image representation ability but with less computation cost than traditional convolutional networks, while the latter allows easy and fast multi-scale feature fusion. The loss function of EfficientSeg is defined as the combination of Dice loss, cross entropy loss, and boundary loss. We evaluated EfficientSeg on the Myocardial Pathology Segmentation (MyoPS 2020) Challenge dataset and achieved a Dice score of 64.71% for scar segmentation and a Dice score of 70.87% for joint edema and scar segmentation. Our results indicate the effectiveness of the proposed EfficientSeg model for myocardial pathology segmentation.

Original languageEnglish
Title of host publicationMyocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images - First Challenge, MyoPS 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsXiahai Zhuang, Lei Li
PublisherSpringer Science and Business Media Deutschland GmbH
Pages17-25
Number of pages9
ISBN (Print)9783030656508
DOIs
Publication statusPublished or Issued - 1 Jan 2020
Event1st Myocardial Pathology Segmentation Combining Multi-Sequence CMR Challenge, MyoPS 2020 held in conjunction with 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 4 Oct 20204 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12554 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st Myocardial Pathology Segmentation Combining Multi-Sequence CMR Challenge, MyoPS 2020 held in conjunction with 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
CountryPeru
CityLima
Period4/10/204/10/20

Keywords

  • Cardiac magnetic resonance imaging
  • Deep learning
  • Myocardial pathology segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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