3D deeply supervised network for automated segmentation of volumetric medical images.
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Cites background or methods from "3D deeply supervised network for au..."
...This system has achieved 0.823 Dice score for lesion segmentation in CT images and 0.85 in MRI images....
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...They even further expanded the idea by applying different modalities (i.e., brain MRI, breast MRI, and cardiac CTA) for each segmentation task....
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...The auxiliary losses together with the original loss of the hidden layer are combined to strengthening the gradient [23, 86, 87]....
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...The fact that the ROI in medical images usually distributed over multiple adjacent slices (e.g., in CT or MRI), results in having correlations in successive slices....
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...One of the earlier solutions for this issue is to apply pooling layers which can reduce the dimensionality of the parameters [23]....
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700 citations
Cites methods from "3D deeply supervised network for au..."
...Furthermore, a 3D deeply supervised approach for automated segmentation of volumetric medical images was presented in [36]....
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622 citations
Cites methods from "3D deeply supervised network for au..."
...the DBN [145], and blood pool (BP) and myocardium (MC) using the CNN [157]....
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582 citations
Additional excerpts
...For 3D volumes, patch-based CNNs have been proposed for segmentation of the brain tumor [7] and pancreas [14], and more powerful end-to-end 3D CNNs include V-Net [15], HighRes3DNet [8], and 3D deeply supervised network [16]....
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References
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"3D deeply supervised network for au..." refers methods in this paper
...3D end-to-end learning for volumetric segmentation In a classic CNN such as the AlexNet ( Krizhevsky et al., 2012 ) nd the VGGNet ( Simonyan and Zisserman, 2014 ), the last several ayers are typically fully-connected ones, which restrict the net- ork to take fixed-sized input image and generate…...
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