Fully Automated Deep Learning Based Segmentation of Normal, Infarcted and Edema Regions from Multiple Cardiac MRI Sequences

Zhang, Xiaoran; Noga, Michelle; Punithakumar, Kumaradevan

doi:10.1007/978-3-030-65651-5_8

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…To verify the effectiveness of the employed NVTrans-UNet in multi-modal cardiac image segmentation, we compared it with other advanced methods. These methods include baseline MFU-Net, 26 FCDensenet, 27 FADLS, 28 U-Net, 29 PyMIC, 30 MVMM, 14 and CMRadjustNet. 31 The results show that the Dice score of MI was 0.642 ± 0.171, and the Dice score of MI + ME was 0.574 ± 0.110. parameter size of the model.…”

Section: Experiments Resultsmentioning

confidence: 99%

NVTrans‐UNet: Neighborhood vision transformer based U‐Net for multi‐modal cardiac MR image segmentation

Zhao

2023

J Applied Clin Med Phys

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With the rapid development of artificial intelligence and image processing technology, medical imaging technology has turned into a critical tool for clinical diagnosis and disease treatment. The extraction and segmentation of the regions of interest in cardiac images are crucial to the diagnosis of cardiovascular diseases. Due to the erratically diastolic and systolic cardiac, the boundaries of Magnetic Resonance (MR) images are quite fuzzy. Moreover, it is hard to provide complete information using a single modality due to the complex structure of the cardiac image. Furthermore, conventional CNN-based segmentation methods are weak in feature extraction. To overcome these challenges, we propose a multi-modal method for cardiac image segmentation, called NVTrans-UNet. Firstly, we employ the Neighborhood Vision Transformer (NVT) module, which takes advantage of Neighborhood Attention (NA) and inductive biases. It can better extract the local information of the cardiac image as well as reduce the computational cost. Secondly, we introduce a Multi-modal Gated Fusion (MGF) network, which can automatically adjust the contributions of different modal feature maps and make full use of multi-modal information. Thirdly, the bottleneck layer with Atrous Spatial Pyramid Pooling (ASPP) is proposed to expand the feature receptive field. Finally, the mixed loss is added to the cardiac image to focus the fuzzy boundary and realize accurate segmentation. We evaluated our model on MyoPS 2020 dataset. The Dice score of myocardial infarction (MI) was 0.642 ± 0.171, and the Dice score of myocardial infarction + edema (MI + ME) was 0.574 ± 0.110. Compared with the baseline, the MI increases by 11.2%, and the MI + ME increases by 12.5%. The results show the effectiveness of the proposed NVTrans-UNet in the segmentation of MI and ME.

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Section: Experiments Resultsmentioning

confidence: 99%

NVTrans‐UNet: Neighborhood vision transformer based U‐Net for multi‐modal cardiac MR image segmentation

Zhao

2023

J Applied Clin Med Phys

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“…Zhang et al proposed two cascade network, one is anatomical structure segmentation network, the other is pathological region segmentation network, modality specific feature fused by channel attention block with layer-level fusion strategy [53], this framework achieves good result but requires the image to go through segmentation network twice to get the final result. Zhang et al trained three parallel segmentation networks, and averaged the prediction with threshold 0.5 in the decision-level [51]. Their method gets a competitive segmentation performance but needs sufficient powerful GPU to train the three parallel model.…”

Section: Pathology Segmentationmentioning

confidence: 99%

TAUNet: a triple-attention-based multi-modality MRI fusion U-Net for cardiac pathology segmentation

Peng

Guo

et al. 2022

Complex Intell. Syst.

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Automated segmentation of cardiac pathology in MRI plays a significant role for diagnosis and treatment of some cardiac disease. In clinical practice, multi-modality MRI is widely used to improve the cardiac pathology segmentation, because it can provide multiple or complementary information. Recently, deep learning methods have presented implausible performance in multi-modality medical image segmentation. However, how to fuse the underlying multi-modality information effectively to segment the pathology with irregular shapes and small region at random locations, is still a challenge task. In this paper, a triple-attention-based multi-modality MRI fusion U-Net was proposed to learn complex relationship between different modalities and pay more attention on shape information, thus to achieve improved pathology segmentation. First, three independent encoders and one fusion encoder were applied to extract specific and multiple modality features. Secondly, we concatenate the modality feature maps and use the channel attention to fuse specific modal information at every stage of the three dedicate independent encoders, then the three single modality feature maps and channel attention feature maps are together concatenated to the decoder path. Spatial attention was adopted in decoder path to capture the correlation of various positions. Once more, we employ shape attention to focus shape-dependent information. Lastly, the training approach is made efficient by introducing deep supervision mechanism with object contextual representations block to ensure precisely boundary prediction. Our proposed network was evaluated on the public MICCAI 2020 Myocardial pathology segmentation dataset which involves patients suffering from myocardial infarction. Experiments on the dataset with three modalities demonstrate the effectiveness of fusion mode of our model, and attention mechanism can integrate various modality information well. We demonstrated that such a deep learning approach could better fuse complementary information to improve the segmentation performance of cardiac pathology.

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“…In addition, NPU [26] and USTB [20] simply removes the isolated regions. UOA [27] solely retains the largest connected component of predicted LV, and then fills the holes.…”

Section: Myops: Myocardial Pathology Segmentation From Multi-sequence...mentioning

confidence: 99%

Multi-Modality Cardiac Image Analysis with Deep Learning

Li¹,

Wu²,

Wang³

et al. 2021

Preprint

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Accurate cardiac computing, analysis and modeling from multi-modality images are important for the diagnosis and treatment of cardiac disease. Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is a promising technique to visualize and quantify myocardial infarction (MI) and atrial scars. Automating quantification of MI and atrial scars can be challenging due to the low image quality and complex enhancement patterns of LGE MRI. Moreover, compared with the other sequences LGE MRIs with gold standard labels are particularly limited, which represents another obstacle for developing novel algorithms for automatic segmentation and quantification of LGE MRIs. This chapter aims to summarize the state-of-the-art and our recent advanced contributions on deep learning based multi-modality cardiac image analysis. Firstly, we introduce two benchmark works for multi-sequence cardiac MRI based myocardial and pathology segmentation. Secondly, two novel frameworks for left atrial scar segmentation and quantification from LGE MRI were presented. Thirdly, we present three unsupervised domain adaptation techniques for cross-modality cardiac image segmentation.

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Fully Automated Deep Learning Based Segmentation of Normal, Infarcted and Edema Regions from Multiple Cardiac MRI Sequences

Cited by 8 publications

References 12 publications

NVTrans‐UNet: Neighborhood vision transformer based U‐Net for multi‐modal cardiac MR image segmentation

NVTrans‐UNet: Neighborhood vision transformer based U‐Net for multi‐modal cardiac MR image segmentation

TAUNet: a triple-attention-based multi-modality MRI fusion U-Net for cardiac pathology segmentation

Multi-Modality Cardiac Image Analysis with Deep Learning

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