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author:

Ding, Wangbin (Ding, Wangbin.) [1] | Li, Lei (Li, Lei.) [2] | Zhuang, Xiahai (Zhuang, Xiahai.) [3] | Huang, Liqin (Huang, Liqin.) [4] (Scholars:黄立勤)

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EI Scopus

Abstract:

Both image registration and label fusion in the multi-atlas segmentation (MAS) rely on the intensity similarity between target and atlas images. However, such similarity can be problematic when target and atlas images are acquired using different imaging protocols. High-level structure information can provide reliable similarity measurement for cross-modality images when cooperating with deep neural networks (DNNs). This work presents a new MAS framework for cross-modality images, where both image registration and label fusion are achieved by DNNs. For image registration, we propose a consistent registration network, which can jointly estimate forward and backward dense displacement fields (DDFs). Additionally, an invertible constraint is employed in the network to reduce the correspondence ambiguity of the estimated DDFs. For label fusion, we adapt a few-shot learning network to measure the similarity of atlas and target patches. Moreover, the network can be seamlessly integrated into the patch-based label fusion. The proposed framework is evaluated on the MM-WHS dataset of MICCAI 2017. Results show that the framework is effective in both cross-modality registration and segmentation. © 2020, Springer Nature Switzerland AG.

Keyword:

Deep neural networks Image fusion Image registration Image segmentation Medical computing Medical imaging Neural networks

Community:

  • [ 1 ] [Ding, Wangbin]College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
  • [ 2 ] [Li, Lei]School of Data Science, Fudan University, Shanghai, China
  • [ 3 ] [Li, Lei]School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
  • [ 4 ] [Li, Lei]School of Biomedical Engineering and Imaging Sciences, Kings College London, London, United Kingdom
  • [ 5 ] [Zhuang, Xiahai]School of Data Science, Fudan University, Shanghai, China
  • [ 6 ] [Huang, Liqin]College of Physics and Information Engineering, Fuzhou University, Fuzhou, China

Reprint 's Address:

  • 黄立勤

    [huang, liqin]college of physics and information engineering, fuzhou university, fuzhou, china

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ISSN: 0302-9743

Year: 2020

Volume: 12263 LNCS

Page: 233-242

Language: English

0 . 4 0 2

JCR@2005

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 18

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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