Medical image segmentation is the task of segmenting objects of interest in a medical image - for example organs or lesions.
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There is large consent that successful training of deep networks requires many thousand annotated training samples.
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.
SOTA for Pancreas Segmentation on CT-150
Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields.
In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively.
SOTA for Lung Nodule Segmentation on LUNA
Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN.
A cascade of fully convolutional neural networks is proposed to segment multi-modal Magnetic Resonance (MR) images with brain tumor into background and three hierarchical regions: whole tumor, tumor core and enhancing tumor core.
We present a method for highly efficient landmark detection that combines deep convolutional neural networks with well established model-based fitting algorithms.
We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing.
#2 best model for Brain Tumor Segmentation on BRATS-2015
To the best of our knowledge, our work is the first to study subcortical structure segmentation on such large-scale and heterogeneous data.