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Found 6 papers, 2 papers with code
Offset Curves Loss for Imbalanced Problem in Medical Segmentation
Our proposed Offset Curves (OsC) loss consists of three main fitting terms.
Flow-based Deformation Guidance for Unpaired Multi-Contrast MRI Image-to-Image Translation
To capture temporal structures in the medical images, we explore the displacement between the consecutive slices using a deformation field.
Generative Adversarial Network
Image-to-Image Translation
+1
Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge
Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.
Skip-connected 3D DenseNet for volumetric infant brain MRI segmentation
The proposed network, called 3D-SkipDenseSeg, exploits the advantage of the recently DenseNet for classification task and extends this to segment the 6-month infant brain tissue segmentation of magnetic resonance imaging (MRI).
3D Densely Convolutional Networks for VolumetricSegmentation
The proposed network architecture provides a dense connection between layers that aims to improve the information flow in the network.
3D Densely Convolutional Networks for Volumetric Segmentation
The proposed network architecture provides a dense connection between layers that aims to improve the information flow in the network.
