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Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks
Spleen volume estimation using automated image segmentation technique ma...
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Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks
Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI)...
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Fully Automatic Intervertebral Disc Segmentation Using Multimodal 3D U-Net
Intervertebral discs (IVDs), as small joints lying between adjacent vert...
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3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes
With the introduction of fully convolutional neural networks, deep learn...
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Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning
Brain segmentation is a fundamental first step in neuroimage analysis. I...
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Exclusive Independent Probability Estimation using Deep 3D Fully Convolutional DenseNets for IsoIntense Infant Brain MRI Segmentation
The most recent fast and accurate image segmentation methods are built u...
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Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems
Automatic segmentation methods based on deep learning have recently demo...
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Kidney segmentation using 3D U-Net localized with Expectation Maximization
Kidney volume is greatly affected in several renal diseases. Precise and automatic segmentation of the kidney can help determine kidney size and evaluate renal function. Fully convolutional neural networks have been used to segment organs from large biomedical 3D images. While these networks demonstrate state-of-the-art segmentation performances, they do not immediately translate to small foreground objects, small sample sizes, and anisotropic resolution in MRI datasets. In this paper we propose a new framework to address some of the challenges for segmenting 3D MRI. These methods were implemented on preclinical MRI for segmenting kidneys in an animal model of lupus nephritis. Our implementation strategy is twofold: 1) to utilize additional MRI diffusion images to detect the general kidney area, and 2) to reduce the 3D U-Net kernels to handle small sample sizes. Using this approach, a Dice similarity coefficient of 0.88 was achieved with a limited dataset of n=196. This segmentation strategy with careful optimization can be applied to various renal injuries or other organ systems.
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