MS-GAN

GAN-based semantic segmentation of multiple sclerosis lesions in brain magnetic resonance imaging

Chaoyi Zhang, Yang Song, Sidong Liu, Scott Lill, Chenyu Wang, Zihao Tang, Yuyi You, Yang Gao, Alexander Klistorner, Michael Barnett, Weidong Cai

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

2 Citations (Scopus)

Abstract

Automated segmentation of multiple sclerosis (MS) lesions in brain imaging is challenging due to the high variability in lesion characteristics. Based on the generative adversarial network (GAN), we propose a semantic segmentation framework MS-GAN to localize MS lesions in multimodal brain magnetic resonance imaging (MRI), which consists of one multimodal encoder-decoder generator G and multiple discriminators D corresponding to the multiple input modalities. For the design of the generator, we adopt an encoder-decoder deep learning architecture with bypass of spatial information from encoder to the corresponding decoder, which helps to reduce the network parameters while improving the localization performance. Our generator is also designed to integrate multimodal imaging data in end-to-end learning with multi-path encoding and cross-modality fusion. An additional classification-related constraint is proposed for the adversarial training process of the GAN model, with the aim of alleviating the hard-to-converge issue in classification-based image-to-image translation problems. For evaluation, we collected a database of 126 cases from patients with relapsing MS. We also experimented with other semantic segmentation models as well as patch-based deep learning methods for performance comparison. The results show that our method provides more accurate segmentation than the state-of-the-art techniques.

Original languageEnglish
Title of host publication2018 International Conference on Digital Image Computing
Subtitle of host publicationTechniques and Applications, DICTA 2018
EditorsManzur Murshed, Manoranjan Paul, Md Asikuzzaman, Mark Pickering, Ambarish Natu, Antonio Robles-Kelly, Shaodi You, Lihong Zheng, Ashfaqur Rahman
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages84-91
Number of pages8
ISBN (Electronic)9781538666029
DOIs
Publication statusPublished - 2018
Event2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018 - Canberra, Australia
Duration: 10 Dec 201813 Dec 2018

Conference

Conference2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
CountryAustralia
CityCanberra
Period10/12/1813/12/18

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Keywords

  • GAN
  • Image-to-image Translation
  • Multiple Sclerosis
  • Semantic Segmentation

Cite this

Zhang, C., Song, Y., Liu, S., Lill, S., Wang, C., Tang, Z., ... Cai, W. (2018). MS-GAN: GAN-based semantic segmentation of multiple sclerosis lesions in brain magnetic resonance imaging. In M. Murshed, M. Paul, M. Asikuzzaman, M. Pickering, A. Natu, A. Robles-Kelly, S. You, L. Zheng, ... A. Rahman (Eds.), 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018 (pp. 84-91). [8615771] Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/DICTA.2018.8615771