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 dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation.
SOTA for Lesion Segmentation on ISLES-2015
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
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
Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields.
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.
Fueled by the diversity of datasets, semantic segmentation is a popular subfield in medical image analysis with a vast number of new methods being proposed each year.
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