Multiple sclerosis lesion filling using a non-lesion attention based convolutional network

Hao Xiong, Chaoyue Wang, Michael Barnett, Chenyu Wang

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

Abstract

Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system (CNS) that results in focal injury to the grey and white matter. The presence of white matter lesions biases morphometric analyses such as registration, individual longitudinal measurements and tissue segmentation for brain volume measurements. Lesion-inpainting with intensities derived from surrounding healthy tissue represents one approach to alleviate such problems. However, existing methods fill lesions based on texture information derived from local surrounding tissue, often leading to inconsistent inpainting and the generation of artifacts such as intensity discrepancy and blurriness. Based on these observations, we propose a non-lesion attention network (NLAN) that integrates an elaborately designed network with non-lesion attention modules and a designed loss function. The non-lesion attention module is exploited to capture long range dependencies between the lesion area and remaining normal-appearing brain regions, and also eliminates the impact of other lesions on local lesion filling. Meanwhile, the designed loss function ensures that high-quality output can be generated. As a result, this method generates inpainted regions that appear more realistic; more importantly, quantitative morphometric analyses incorporating our NLAN demonstrate superiority of this technique of existing state-of-the-art lesion filling methods.
Original languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publication27th International Conference, ICONIP 2020. Proceedings, Part I
EditorsHaiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
Place of PublicationCham
PublisherSpringer, Springer Nature
Pages448-460
Number of pages13
ISBN (Electronic)9783030638306
ISBN (Print)9783030638290
DOIs
Publication statusPublished - 2020
Event27th International Conference on Neural Information Processing, ICONIP 2020 - Bangkok, Thailand
Duration: 23 Nov 202027 Nov 2020

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume12532
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Neural Information Processing, ICONIP 2020
CountryThailand
CityBangkok
Period23/11/2027/11/20

Keywords

  • Multiple sclerosis
  • MS Lesion Filling
  • Non-lesion attention

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