Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation

Published in

(2017)

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TL;DR

This work is the first ensemble of 3D CNNs for suggesting annotations within images, and allows the efficient propagation of gradients during training, while limiting the number of parameters, requiring one order of magnitude less parameters than popular medical image segmentation networks such as 3D U-Net.

Authors

Christian Desrosiers

6 papers

D. Shen

4 papers

Jing Yuan

3 papers

Ismail Ben Ayed

11 papers

J. Dolz

14 papers

Li Wang

1 papers

References63 items

Deep Learning in Medical Image Analysis

Benchmark on Automatic Six-Month-Old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge

Boundary loss for highly unbalanced segmentation

Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images

HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation

Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation

Published in

(2017)

External Links:

Generate Graph DownloadPDF

TL;DR

Authors

Christian Desrosiers

6 papers

D. Shen

4 papers

Jing Yuan

3 papers

Ismail Ben Ayed

11 papers

J. Dolz

14 papers

Li Wang

1 papers

References63 items

Deep Learning in Medical Image Analysis

Benchmark on Automatic Six-Month-Old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge

Boundary loss for highly unbalanced segmentation

Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images

HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation

Esophagus segmentation in CT via 3D fully convolutional neural network and random walk

Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation

Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets

A review on automatic fetal and neonatal brain MRI segmentation

Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation

A survey on deep learning in medical image analysis

3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study

Densely Connected Convolutional Networks

3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks

Automatic 3D liver location and segmentation via convolutional neural network and graph cut

Fully convolutional networks for multi-modality isointense infant brain image segmentation

Automatic Segmentation of MR Brain Images With a Convolutional Neural Network

Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

Identity Mappings in Deep Residual Networks

Automatic segmentation of MR brain images of preterm infants using supervised classification

Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey

Deep convolutional neural networks for multi-modality isointense infant brain image segmentation

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Fully convolutional networks for semantic segmentation

LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images

Very Deep Convolutional Networks for Large-Scale Image Recognition

Mapping longitudinal hemispheric structural asymmetries of the human cerebral cortex from birth to 2 years of age.

Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation

Segmentation of neonatal brain MR images using patch-driven level sets

Visualizing and Understanding Convolutional Networks

AdaPT: An adaptive preterm segmentation algorithm for neonatal brain MRI

Automated detection of white matter signal abnormality using T2 relaxometry: Application to brain segmentation on term MRI in very preterm infants

ImageNet classification with deep convolutional neural networks

Morphology-driven automatic segmentation of MR images of the neonatal brain

4D Multi-Modality Tissue Segmentation of Serial Infant Images

Automatic segmentation of neonatal images using convex optimization and coupled level sets

Infant Brain Atlases from Neonates to 1- and 2-Year-Olds

Automatic Segmentation of Neonatal Images Using Convex Optimization and Coupled Level Set Method

Construction of multi-region-multi-reference atlases for neonatal brain MRI segmentation

Neonatal brain image segmentation in longitudinal MRI studies

Automatic segmentation of newborn brain MRI

Probabilistic Brain Tissue Segmentation in Neonatal Magnetic Resonance Imaging

Automatic segmentation and reconstruction of the cortex from neonatal MRI

Clinical Neonatal Brain MRI Segmentation Using Adaptive Nonparametric Data Models and Intensity-Based Markov Priors

Detailed semiautomated MRI based morphometry of the neonatal brain: Preliminary results

Segmentation of newborn brain MRI

Automatic segmentation of MR images of the developing newborn brain

Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation

Maturation of white matter in the human brain: a review of magnetic resonance studies

Measures of the Amount of Ecologic Association Between Species

Automatic Registration-Based Segmentation for Neonatal Brains Using ANTs and Atropos

Automatic tissue and structural segmentation of neonatal brain MRI using Expectation-Maximization

Neonatal brain segmentation using second order neighborhood information

An atlas - based method for neonatal MR brain tissue segmentation

Theano: A CPU and GPU Math Compiler in Python

Gradient-based learning applied to document recognition

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Neobrains 12 challenge : adaptive neonatal MRI brain segmentation with myelinated white matter class and automated extraction of ventricles i - iv

Field of Study

Computer ScienceMedicine

Journal Information

Name

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

Page

102373

Volume

114