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SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth
A key limitation of deep convolutional neural networks (DCNN) based imag...
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A Transfer Learning Approach for Automated Segmentation of Prostate Whole Gland and Transition Zone in Diffusion Weighted MRI
The segmentation of prostate whole gland and transition zone in Diffusio...
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Synthetic Perfusion Maps: Imaging Perfusion Deficits in DSC-MRI with Deep Learning
In this work, we present a novel convolutional neural net- work based me...
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Region-based Convolution Neural Network Approach for Accurate Segmentation of Pelvic Radiograph
With the increasing usage of radiograph images as a most common medical ...
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End-to-end Segmentation with Recurrent Attention Neural Network
Image segmentation quality depends heavily on the quality of the image. ...
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Convolutional Neural Networks for Skull-stripping in Brain MR Imaging using Consensus-based Silver standard Masks
Convolutional neural networks (CNN) for medical imaging are constrained ...
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Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets
Purpose: Lesion segmentation in medical imaging is key to evaluating tre...
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Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation
We present a novel approach of 2D to 3D transfer learning based on mapping pre-trained 2D convolutional neural network weights into planar 3D kernels. The method is validated by the proposed planar 3D res-u-net network with encoder transferred from the 2D VGG-16, which is applied for a single-stage unbalanced 3D image data segmentation. In particular, we evaluate the method on the MICCAI 2016 MS lesion segmentation challenge dataset utilizing solely fluid-attenuated inversion recovery (FLAIR) sequence without brain extraction for training and inference to simulate real medical praxis. The planar 3D res-u-net network performed the best both in sensitivity and Dice score amongst end to end methods processing raw MRI scans and achieved comparable Dice score to a state-of-the-art unimodal not end to end approach. Complete source code was released under the open-source license, and this paper complies with the Machine learning reproducibility checklist. By implementing practical transfer learning for 3D data representation, we could segment heavily unbalanced data without selective sampling and achieved more reliable results using less training data in a single modality. From a medical perspective, the unimodal approach gives an advantage in real praxis as it does not require co-registration nor additional scanning time during an examination. Although modern medical imaging methods capture high-resolution 3D anatomy scans suitable for computer-aided detection system processing, deployment of automatic systems for interpretation of radiology imaging is still rather theoretical in many medical areas. Our work aims to bridge the gap by offering a solution for partial research questions.
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