Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020; 23rd International C Anne L. Martel,Purang Abolmaesumi,Leo Joskow

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Deep Generative Model-Based Quality Control for Cardiac MRI Segmentationed through iterative search in the latent space. The proposed method achieves high prediction accuracy on two publicly available cardiac MRI datasets. Moreover, it shows better generalisation ability than traditional regression-based methods. Our approach provides a real-time and model-agnostic qual

moratorium

Engaging

Learning Directional Feature Maps for Cardiac MRI Segmentationmentation. The proposed modules are simple yet effective and can be flexibly added to any existing segmentation network without excessively increasing time and space complexity. We evaluate the proposed method on the 2017 MICCAI Automated Cardiac Diagnosis Challenge (ACDC) dataset and a large-scale

面包屑

Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shapng small and discrete targets like scars. We evaluated the proposed framework on 60 LGE MRI data from the MICCAI2018 LA challenge. For LA segmentation, the proposed method reduced the mean Hausdorff distance from 36.4 mm to 20.0 mm compared to the 3D basic U-Net using the binary cross-entropy loss.

功多汁水

XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phanour conditional XCAT-GAN with real images paired with corresponding labels and subsequently at the inference time, we substitute the labels with the XCAT derived ones. Therefore, the trained network accurately transfers the tissue-specific textures to the new label maps. By creating 33 virtual subje

性别

TexNet: Texture Loss Based Network for Gastric Antrum Segmentation in Ultrasoundxture-wise accuracy in contour area which can reduce overfitting issues caused by using DSC loss alone. Experiments have been performed on 8487 images from 121 patients. Results show that TexNet outperforms state of the art methods with higher accuracy and better consistency. Besides GA, the propose

隼鹰

Multi-organ Segmentation via Co-training Weight-Averaged Models from Few-Organ Datasets noisy teaching supervisions between the networks, the weighted-averaged models are adopted to produce more reliable soft labels. In addition, a novel region mask is utilized to selectively apply the consistent constraint on the un-annotated organ regions that require collaborative teaching, which f

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Pay More Attention to Discontinuity for Medical Image Segmentationver segmentation tasks demonstrate that such a simple approach effectively mitigates the inaccurate segmentation due to discontinuity and achieves noticeable improvements over some state-of-the-art methods.

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