3D medical imaging segmentation is the task of segmenting medical objects of interest from 3D medical imaging.
( Image credit: Elastic Boundary Projection for 3D Medical Image Segmentation )
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We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation.
Ranked #1 on Lesion Segmentation on ISLES-2015
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.
The proposed network architecture provides a dense connection between layers that aims to improve the information flow in the network.
To illustrate its efficiency of learning 3D representation from large-scale image data, the proposed network is validated with the challenging task of parcellating 155 neuroanatomical structures from brain MR images.
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
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.
In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis.
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.
To the best of our knowledge, our work is the first to study subcortical structure segmentation on such large-scale and heterogeneous data.