Brain Image Segmentation
State-of-the-art leaderboards
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Greatest papers with code
Unsupervised Deep Learning for Bayesian Brain MRI Segmentation
To develop a deep learning-based segmentation model for a new image dataset (e. g., of different contrast), one usually needs to create a new labeled training dataset, which can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or augmentation approaches.
BRAIN IMAGE SEGMENTATION BRAIN SEGMENTATION FEW-SHOT IMAGE SEGMENTATION IMAGE REGISTRATION ZERO SHOT SEGMENTATION
Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning
• arnab39/FewShot_GAN-Unet3D
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In addition, our work presents a comprehensive analysis of different GAN architectures for semi-supervised segmentation, showing recent techniques like feature matching to yield a higher performance than conventional adversarial training approaches.
3D MEDICAL IMAGING SEGMENTATION BRAIN IMAGE SEGMENTATION BRAIN SEGMENTATION FEW-SHOT IMAGE SEGMENTATION SEMI-SUPERVISED SEMANTIC SEGMENTATION
Enforcing temporal consistency in Deep Learning segmentation of brain MR images
• bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets
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Proposed CNN based segmentation approaches demonstrate how 2D segmentation using prior slices can provide similar results to 3D segmentation while maintaining good continuity in the 3D dimension and improved speed.
3D MEDICAL IMAGING SEGMENTATION 4D SPATIO TEMPORAL SEMANTIC SEGMENTATION BRAIN IMAGE SEGMENTATION SEMANTIC SEGMENTATION
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics
• GunhoChoi/FusionNet-Pytorch
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Electron microscopic connectomics is an ambitious research direction with the goal of studying comprehensive brain connectivity maps by using high-throughput, nano-scale microscopy.
BRAIN IMAGE SEGMENTATION IMAGE CLASSIFICATION SEMANTIC SEGMENTATION
Global Deep Learning Methods for Multimodality Isointense Infant Brain Image Segmentation
• zhengyang-wang/3D-Unet--Tensorflow
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An important step in early brain development study is to perform automatic segmentation of infant brain magnetic resonance (MR) images into cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM) regions.
Large Scale Image Segmentation with Structured Loss based Deep Learning for Connectome Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence 2018 • funkey/mala
Our extension consists of two parts: First, we present a quasi-linear method to compute the loss gradient, improving over the original quadratic algorithm.