Infant Brain Mri Segmentation

5 papers with code • 1 benchmarks • 0 datasets

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Most implemented papers

Skip-connected 3D DenseNet for volumetric infant brain MRI segmentation

mindspore-ai/models Biomedical Signal Processing and Control 2019

The proposed network, called 3D-SkipDenseSeg, exploits the advantage of the recently DenseNet for classification task and extends this to segment the 6-month infant brain tissue segmentation of magnetic resonance imaging (MRI).

3D Densely Convolutional Networks for Volumetric Segmentation

tbuikr/3D_DenseSeg 11 Sep 2017

The proposed network architecture provides a dense connection between layers that aims to improve the information flow in the network.

3D Densely Convolutional Networks for VolumetricSegmentation

black0017/MedicalZooPytorch arXiv preprint 2017

The proposed network architecture provides a dense connection between layers that aims to improve the information flow in the network.

Isointense Infant Brain Segmentation with a Hyper-dense Connected Convolutional Neural Network

josedolz/LiviaNET 16 Oct 2017

Neonatal brain segmentation in magnetic resonance (MR) is a challenging problem due to poor image quality and low contrast between white and gray matter regions.

Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation

josedolz/SemiDenseNet 14 Dec 2017

We report evaluations of our method on the public data of the MICCAI iSEG-2017 Challenge on 6-month infant brain MRI segmentation, and show very competitive results among 21 teams, ranking first or second in most metrics.