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 SEMANTIC SEGMENTATION IMAGE REGISTRATION ZERO SHOT SEGMENTATION
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
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 SEMANTIC SEGMENTATION SEMI-SUPERVISED SEMANTIC SEGMENTATION
In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis.
3D MEDICAL IMAGING SEGMENTATION BRAIN IMAGE SEGMENTATION BRAIN LESION SEGMENTATION FROM MRI BRAIN SEGMENTATION LESION SEGMENTATION
Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients.
3D MEDICAL IMAGING SEGMENTATION BRAIN IMAGE SEGMENTATION BRAIN LESION SEGMENTATION FROM MRI BRAIN SEGMENTATION LESION SEGMENTATION
In this work, we propose the non-local U-Nets, which are equipped with flexible global aggregation blocks, for biomedical image segmentation.
NeuroNet is a deep convolutional neural network mimicking multiple popular and state-of-the-art brain segmentation tools including FSL, SPM, and MALPEM.
BRAIN IMAGE SEGMENTATION BRAIN SEGMENTATION SEMANTIC SEGMENTATION
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 ELECTRON MICROSCOPY IMAGE CLASSIFICATION SEMANTIC SEGMENTATION
Medical image segmentation is one of the major challenges addressed by machine learning methods.
BRAIN IMAGE SEGMENTATION BRAIN SEGMENTATION SEMANTIC SEGMENTATION