Robust Divergence Learning for Missing-Modality Segmentation

Runze Cheng, Zhongao Sun, Ye Zhang, Chun Li*

*Corresponding author for this work

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

Abstract

Multimodal Magnetic Resonance Imaging (MRI) provides essential complementary information for analyzing brain tumor subregions. While methods using four common MRI modalities for automatic segmentation have shown success, they often face challenges with missing modalities due to image quality issues, inconsistent protocols, allergic reactions, or cost factors. Thus, developing a segmentation paradigm that handles missing modalities is clinically valuable. A novel single-modality parallel processing network framework based on Hölder divergence and mutual information is introduced. Each modality is independently input into a shared network backbone for parallel processing, preserving unique information. Additionally, a dynamic sharing framework is introduced that adjusts network parameters based on modality availability. A Hölder divergence and mutual information-based loss functions are used for evaluating discrepancies between predictions and labels. Extensive testing on the BraTS 2018 and BraTS 2020 datasets demonstrates that our method outperforms existing techniques in handling missing modalities and validates each component’s effectiveness.

Original languageEnglish
Title of host publicationProceedings - 2024 China Automation Congress, CAC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2077-2083
Number of pages7
ISBN (Electronic)9798350368604
DOIs
Publication statusPublished - 2024
Event2024 China Automation Congress, CAC 2024 - Qingdao, China
Duration: 1 Nov 20243 Nov 2024

Publication series

NameProceedings - 2024 China Automation Congress, CAC 2024

Conference

Conference2024 China Automation Congress, CAC 2024
Country/TerritoryChina
CityQingdao
Period1/11/243/11/24

Keywords

  • brain-tumor segmentation
  • divergence learning
  • knowledge distillation
  • Missing modality learning

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