mlsanityData to 7 Oct 2026

3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint

arXiv 2102.03111 ↗First posted 5 Feb 2021eess.IV cs.CV cs.LGcited by 3

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Abstract (arXiv)

In the field of multimodal segmentation, the correlation between different modalities can be considered for improving the segmentation results. In this paper, we propose a multi-modality segmentation network with a correlation constraint. Our network includes N model-independent encoding paths with N image sources, a correlation constraint block, a feature fusion block, and a decoding path. The model independent encoding path can capture modality-specific features from the N modalities. Since there exists a strong correlation between different modalities, we first propose a linear correlation block to learn the correlation between modalities, then a loss function is used to guide the network to learn the correlated features based on the linear correlation block. This block forces the network to learn the latent correlated features which are more relevant for segmentation. Considering that not all the features extracted from the encoders are useful for segmentation, we propose to use dual attention based fusion block to recalibrate the features along the modality and spatial paths, which can suppress less informative features and emphasize the useful ones. The fused feature representation is finally projected by the decoder to obtain the segmentation result. Our experiment results tested on BraTS-2018 dataset for brain tumor segmentation demonstrate the effectiveness of our proposed method.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 33 references that are in the map. First posted on arXiv 5 Feb 2021.

Builds on

17 of 33 references.

References · 17

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5 more
  1. 2018Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challengecited by 56
  2. 2017A deep learning model integrating FCNNs and CRFs for brain tumor segmentationcited by 35
  3. 2017Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networkscited by 33
  4. 2017Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentationcited by 15
  5. 2018Hierarchical multi-class segmentation of glioma images using networks with multi-level activation functioncited by 5

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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 1.

3 papers.

  1. 2022NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentationcited by 33
  2. 2021Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalitiescited by 13
  3. 2023Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learningcited by 1