Segmenting Multi-Source Images Using Hidden Markov Fields With Copula-Based Multivariate Statistical Distributions

@article{LapuyadeLahorgue2017SegmentingMI,
  title={Segmenting Multi-Source Images Using Hidden Markov Fields With Copula-Based Multivariate Statistical Distributions},
  author={J{\'e}r{\^o}me Lapuyade-Lahorgue and Jing-Hao Xue and Su Ruan},
  journal={IEEE Transactions on Image Processing},
  year={2017},
  volume={26},
  pages={3187-3195}
}
Nowadays, multi-source image acquisition attracts an increasing interest in many fields, such as multi-modal medical image segmentation. Such acquisition aims at considering complementary information to perform image segmentation, since the same scene has been observed by various types of images. However, strong dependence often exists between multi-source images. This dependence should be taken into account when we try to extract joint information for precisely making a decision. In order to… 
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19 Citations
Background Citations
9
Methods Citations
4

19 Citations

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This paper proposes a multimodality segmentation network with a correlation constraint that includes N model-independent encoding paths with N image sources, a correlation constrain block, a feature fusion block, and a decoding path that can capture modality-specific features from the N modalities.
A review: Deep learning for medical image segmentation using multi-modality fusion
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A co-clustering algorithm is proposed to concurrently segment 3D tumors in PET-CT images, considering that the two complementary imaging modalities can combine functional and anatomical information to improve segmentation performance.
Brain tumor segmentation with missing modalities via latent multi-source correlation representation
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A novel correlation representation block is proposed to specially discover the latent multi-source correlation between multi modalities and it outperforms the current state-of-the-art method and produces robust results when one or more modalities are missing.
Latent Correlation Representation Learning for Brain Tumor Segmentation With Missing MRI Modalities
TLDR
A novel brain tumor segmentation algorithm with missing modalities is presented, it outperforms the current state-of-the-art methods and produces robust results when one or more modalities are missing.
Fusion based on attention mechanism and context constraint for multi-modal brain tumor segmentation
Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation
Object Detection Improves Tumour Segmentation in MR Images of Rare Brain Tumours
TLDR
This work proposes an automatic segmentation model that, without model retraining or adaptation, showed good results when applied to a rare brain tumour, and proposes a resilient tumour lesion delineation strategy, based on the combination of established elementary networks that achieve detection and segmentation.
A Tri-attention Fusion Guided Multi-modal Segmentation Network
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