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Deep learning in medical image analysis
This review covers computer-assisted analysis of images in the field of medical imaging.
Recent advances in machine learning, especially with regard to deep learning, are hel** …
Recent advances in machine learning, especially with regard to deep learning, are hel** …
MRI segmentation of the human brain: challenges, methods, and applications
I Despotović, B Goossens… - … mathematical methods in …, 2015 - Wiley Online Library
Image segmentation is one of the most important tasks in medical image analysis and is
often the first and the most critical step in many clinical applications. In brain MRI analysis …
often the first and the most critical step in many clinical applications. In brain MRI analysis …
HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation
Recently, dense connections have attracted substantial attention in computer vision
because they facilitate gradient flow and implicit deep supervision during training …
because they facilitate gradient flow and implicit deep supervision during training …
The develo** human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction
Abstract The Develo** Human Connectome Project (dHCP) seeks to create the first 4-
dimensional connectome of early life. Understanding this connectome in detail may provide …
dimensional connectome of early life. Understanding this connectome in detail may provide …
Multi-atlas segmentation of biomedical images: a survey
Abstract Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering
work of Rohlfing, et al.(2004), Klein, et al.(2005), and Heckemann, et al.(2006), is becoming …
work of Rohlfing, et al.(2004), Klein, et al.(2005), and Heckemann, et al.(2006), is becoming …
Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation
Accurate and automatic organ segmentation from 3D radiological scans is an important yet
challenging problem for medical image analysis. Specifically, as a small, soft, and flexible …
challenging problem for medical image analysis. Specifically, as a small, soft, and flexible …
Fully convolutional networks for multi-modality isointense infant brain image segmentation
The segmentation of infant brain tissue images into white matter (WM), gray matter (GM),
and cerebrospinal fluid (CSF) plays an important role in studying early brain development. In …
and cerebrospinal fluid (CSF) plays an important role in studying early brain development. In …
Benchmark on automatic six-month-old infant brain segmentation algorithms: the iSeg-2017 challenge
Accurate segmentation of infant brain magnetic resonance (MR) images into white matter
(WM), gray matter (GM), and cerebrospinal fluid is an indispensable foundation for early …
(WM), gray matter (GM), and cerebrospinal fluid is an indispensable foundation for early …
Concatenated and connected random forests with multiscale patch driven active contour model for automated brain tumor segmentation of MR images
Segmentation of brain tumors from magnetic resonance imaging (MRI) data sets is of great
importance for improved diagnosis, growth rate prediction, and treatment planning …
importance for improved diagnosis, growth rate prediction, and treatment planning …
A review on automatic fetal and neonatal brain MRI segmentation
In recent years, a variety of segmentation methods have been proposed for automatic
delineation of the fetal and neonatal brain MRI. These methods aim to define regions of …
delineation of the fetal and neonatal brain MRI. These methods aim to define regions of …