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Brain Tumor Segmentation

20 papers with code · Medical

Brain tumor segmentation is the task of segmenting tumors from other brain artefacts in MRI image of the brain.

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Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

18 Mar 2016Kamnitsask/deepmedic

We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation.

3D MEDICAL IMAGING SEGMENTATION BRAIN LESION SEGMENTATION FROM MRI BRAIN TUMOR SEGMENTATION LESION SEGMENTATION

Brain Tumor Segmentation with Deep Neural Networks

13 May 2015naldeborgh7575/brain_segmentation

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.

BRAIN TUMOR SEGMENTATION

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

1 Sep 2017taigw/brats17

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.

BRAIN TUMOR SEGMENTATION

Autofocus Layer for Semantic Segmentation

22 May 2018yaq007/Autofocus-Layer

We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing.

BRAIN TUMOR SEGMENTATION SEMANTIC SEGMENTATION

SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation

6 Jun 2017YuanXue1993/SegAN

Extensive experimental results demonstrate the effectiveness of the proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance comparable to the state-of-the-art for whole tumor and tumor core segmentation while achieves better precision and sensitivity for Gd-enhance tumor core segmentation; on BRATS 2015 SegAN achieves better performance than the state-of-the-art in both dice score and precision.

BRAIN TUMOR SEGMENTATION SEMANTIC SEGMENTATION

Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

19 Aug 2019MrGiovanni/ModelsGenesis

More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging.

BRAIN TUMOR SEGMENTATION LIVER SEGMENTATION LUNG NODULE DETECTION LUNG NODULE SEGMENTATION PULMONARY EMBOLISM DETECTION TRANSFER LEARNING

3D MRI brain tumor segmentation using autoencoder regularization

27 Oct 2018IAmSuyogJadhav/3d-mri-brain-tumor-segmentation-using-autoencoder-regularization

Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease.

BRAIN TUMOR SEGMENTATION SEMANTIC SEGMENTATION