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
Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction, which increases significantly the learning representation.
Ranked #1 on
Medical Image Segmentation
on iSEG 2017 Challenge
BRAIN SEGMENTATION IMAGE CLASSIFICATION MULTI-MODAL IMAGE SEGMENTATION REPRESENTATION LEARNING SEMANTIC SEGMENTATION
The proposed network architecture provides a dense connection between layers that aims to improve the information flow in the network.
3D MEDICAL IMAGING SEGMENTATION BRAIN SEGMENTATION INFANT BRAIN MRI SEGMENTATION SEMANTIC SEGMENTATION VOLUMETRIC MEDICAL IMAGE SEGMENTATION
Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors.
Ranked #2 on
Brain Segmentation
on Brain MRI segmentation
3D MEDICAL IMAGING SEGMENTATION BRAIN SEGMENTATION BRAIN TUMOR SEGMENTATION TUMOR SEGMENTATION TWO-SAMPLE TESTING
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
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for segmentation tasks in computer vision and medical imaging.
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging.
Fully convolutional neural networks (F-CNNs) have set the state-of-the-art in image segmentation for a plethora of applications.
BRAIN SEGMENTATION IMAGE CLASSIFICATION SEMANTIC SEGMENTATION