3D Medical Imaging Segmentation
25 papers with code • 1 benchmarks • 5 datasets
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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This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening.
Inspired by the recent success of transformers for Natural Language Processing (NLP) in long-range sequence learning, we reformulate the task of volumetric (3D) medical image segmentation as a sequence-to-sequence prediction problem.
On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
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.
To the best of our knowledge, our work is the first to study subcortical structure segmentation on such large-scale and heterogeneous data.
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors.
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.
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation.
In this paper, we test whether this algorithm, which was shown to improve semantic segmentation for 2D RGB images, is able to improve segmentation quality for 3D multi-modal medical images.
AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy
Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generating masks of all OARs of interest in one shot.