mlsanityData to 14 Oct 2026

Anatomical Consistency Distillation and Inconsistency Synthesis for Brain Tumor Segmentation with Missing Modalities

arXiv 2408.13733 ↗First posted 25 Aug 2024eess.IV cs.CVcited by 2

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Abstract (arXiv)

Multi-modal Magnetic Resonance Imaging (MRI) is imperative for accurate brain tumor segmentation, offering indispensable complementary information. Nonetheless, the absence of modalities poses significant challenges in achieving precise segmentation. Recognizing the shared anatomical structures between mono-modal and multi-modal representations, it is noteworthy that mono-modal images typically exhibit limited features in specific regions and tissues. In response to this, we present Anatomical Consistency Distillation and Inconsistency Synthesis (ACDIS), a novel framework designed to transfer anatomical structures from multi-modal to mono-modal representations and synthesize modality-specific features. ACDIS consists of two main components: Anatomical Consistency Distillation (ACD) and Modality Feature Synthesis Block (MFSB). ACD incorporates the Anatomical Feature Enhancement Block (AFEB), meticulously mining anatomical information. Simultaneously, Anatomical Consistency ConsTraints (ACCT) are employed to facilitate the consistent knowledge transfer, i.e., the richness of information and the similarity in anatomical structure, ensuring precise alignment of structural features across mono-modality and multi-modality. Complementarily, MFSB produces modality-specific features to rectify anatomical inconsistencies, thereby compensating for missing information in the segmented features. Through validation on the BraTS2018 and BraTS2020 datasets, ACDIS substantiates its efficacy in the segmentation of brain tumors with missing MRI modalities.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 30 of its 41 references that are in the map. First posted on arXiv 25 Aug 2024.

Builds on

30 of 41 references.

References · 30

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  11. 2021TransBTS: Multimodal Brain Tumor Segmentation Using Transformercited by 265
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18 more
  1. 2015Cross Modal Distillation for Supervision Transfercited by 175
  2. 2023Multi-modal Learning with Missing Modality via Shared-Specific Feature Modellingcited by 163
  3. 2016HeMIS: Hetero-Modal Image Segmentationcited by 103
  4. 2022mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentationcited by 95
  5. 2020Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesiscited by 86
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  10. 2023M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalitiescited by 56
  11. 2021ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalitiescited by 43
  12. 2021Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalitiescited by 42
  13. 2023MMANet: Margin-aware Distillation and Modality-aware Regularization for Incomplete Multimodal Learningcited by 40
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  16. 2022Medical Image Segmentation on MRI Images with Missing Modalities: A Reviewcited by 18
  17. 2023Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentationcited by 14
  18. 2022MouseGAN++: Unsupervised Disentanglement and Contrastive Representation for Multiple MRI Modalities Synthesis and Structural Segmentation of Mouse Braincited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing Modalitiescited by 7
  2. 2026AMGFormer: Adaptive Multi-Granular Transformer for Brain Tumor Segmentation with Missing Modalitiescited by 0