CNS: CycleGAN-Assisted Neonatal Segmentation Model for Cross-Datasets

Jian Chen, Zhenghan Fang, Deqiang Xiao, Duc Toan Bui, Kim Han Thung, Xianjun Li, Jian Yang, Weili Lin, Gang Li, Dinggang Shen, Li Wang

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

Abstract

Accurate segmentation of neonatal brain MR images is critical for studying early brain development. Recently, supervised learning-based methods, i.e., using convolutional neural networks (CNNs), have been successfully applied to infant brain segmentation. Although these CNN-based methods have achieved reasonable segmentation results on the testing subjects acquired with similar imaging protocol as the training subjects, they are typically not able to produce reasonable results for the testing subjects acquired with different imaging protocols. To address this practical issue, in this paper, we propose leveraging a cycle-consistent generative adversarial network (CycleGAN) to transfer each testing image (of a new dataset/cross-dataset) into the domain of training data, thus obtaining the transferred testing image with similar intensity appearance as the training images. Then, a densely-connected U-Net based segmentation model, which has been trained on the training data, can be utilized to robustly segment each transferred testing image. Experimental results demonstrate the superior performance of our proposed method, over existing methods, on segmenting cross-dataset of neonatal brain MR images.

Original languageEnglish
Title of host publicationGraph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings
EditorsDaoqiang Zhang, Luping Zhou, Biao Jie, Mingxia Liu
PublisherSpringer
Pages172-179
Number of pages8
ISBN (Print)9783030358167
DOIs
Publication statusPublished - 2019
Event1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 2019 Oct 172019 Oct 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11849 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period19/10/1719/10/17

Keywords

  • Cross-dataset
  • CycleGAN
  • Neonatal brain
  • Segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Chen, J., Fang, Z., Xiao, D., Bui, D. T., Thung, K. H., Li, X., Yang, J., Lin, W., Li, G., Shen, D., & Wang, L. (2019). CNS: CycleGAN-Assisted Neonatal Segmentation Model for Cross-Datasets. In D. Zhang, L. Zhou, B. Jie, & M. Liu (Eds.), Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings (pp. 172-179). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11849 LNCS). Springer. https://doi.org/10.1007/978-3-030-35817-4_21