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

Published in

(2016)

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TL;DR

An efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data, and improves on the state-of-the‐art for all three applications.

Authors

D. Rueckert

14 papers

Ben Glocker

10 papers

C. Ledig

2 papers

K. Kamnitsas

2 papers

V. Newcombe

1 papers

Joanna P. Simpson

1 papers

A. D. Kane

1 papers

D. Menon

1 papers

References84 items

Mastering the game of Go with deep neural networks and tree search

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation.

Segmentation of Ischemic Stroke Lesions in Multi-spectral MR Images Using Weighting Suppressed FCM and Three Phase Level Set

Deep Convolutional Neural Networks for the Segmentation of Gliomas in Multi-sequence MRI

Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation

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

Published in

(2016)

External Links:

Generate Graph DownloadPDF

TL;DR

Authors

D. Rueckert

14 papers

Ben Glocker

10 papers

C. Ledig

2 papers

K. Kamnitsas

2 papers

V. Newcombe

1 papers

Joanna P. Simpson

1 papers

A. D. Kane

1 papers

D. Menon

1 papers

References84 items

Mastering the game of Go with deep neural networks and tree search

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation.

Segmentation of Ischemic Stroke Lesions in Multi-spectral MR Images Using Weighting Suppressed FCM and Three Phase Level Set

Deep Convolutional Neural Networks for the Segmentation of Gliomas in Multi-sequence MRI

Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation

ISLES (SISS) Challenge 2015: Segmentation of Stroke Lesions Using Spatial Normalization, Random Forest Classification and Contextual Clustering

Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI

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Gradient-based learning applied to document recognition

We employ parallel convolutional pathways for multi-scale processing, a solution to eﬃciently incorporate both local and contextual information which greatly improves segmentation results