Machine learning techniques for biomedical image segmentation: an overview of technical aspects and introduction to state‐of‐art applications
H Seo, M Badiei Khuzani, V Vasudevan… - Medical …, 2020 - Wiley Online Library
In recent years, significant progress has been made in develo** more accurate and
efficient machine learning algorithms for segmentation of medical and natural images. In this …
efficient machine learning algorithms for segmentation of medical and natural images. In this …
Advances in auto-segmentation
Manual image segmentation is a time-consuming task routinely performed in radiotherapy to
identify each patient's targets and anatomical structures. The efficacy and safety of the …
identify each patient's targets and anatomical structures. The efficacy and safety of the …
DeepNAT: Deep convolutional neural network for segmenting neuroanatomy
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic
segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is …
segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is …
Machine learning for auto-segmentation in radiotherapy planning
Manual segmentation of target structures and organs at risk is a crucial step in the
radiotherapy workflow. It has the disadvantages that it can require several hours of clinician …
radiotherapy workflow. It has the disadvantages that it can require several hours of clinician …
QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy
Whole brain segmentation from structural magnetic resonance imaging (MRI) is a
prerequisite for most morphological analyses, but is computationally intense and can …
prerequisite for most morphological analyses, but is computationally intense and can …
Vision 20/20: perspectives on automated image segmentation for radiotherapy
Due to rapid advances in radiation therapy (RT), especially image guidance and treatment
adaptation, a fast and accurate segmentation of medical images is a very important part of …
adaptation, a fast and accurate segmentation of medical images is a very important part of …
A review of segmentation methods in short axis cardiac MR images
For the last 15 years, Magnetic Resonance Imaging (MRI) has become a reference
examination for cardiac morphology, function and perfusion in humans. Yet, due to the …
examination for cardiac morphology, function and perfusion in humans. Yet, due to the …
Comparison and evaluation of methods for liver segmentation from CT datasets
This paper presents a comparison study between 10 automatic and six interactive methods
for liver segmentation from contrast-enhanced CT images. It is based on results from the …
for liver segmentation from contrast-enhanced CT images. It is based on results from the …
A survey on brain tumor detection techniques for MR images
PK Chahal, S Pandey, S Goel - Multimedia Tools and Applications, 2020 - Springer
One of the most crucial tasks in any brain tumor detection system is the isolation of abnormal
tissues from normal brain tissues. Interestingly, domain of brain tumor analysis has …
tissues from normal brain tissues. Interestingly, domain of brain tumor analysis has …
Combination strategies in multi-atlas image segmentation: application to brain MR data
X Artaechevarria, A Munoz-Barrutia… - IEEE transactions on …, 2009 - ieeexplore.ieee.org
It has been shown that employing multiple atlas images improves segmentation accuracy in
atlas-based medical image segmentation. Each atlas image is registered to the target image …
atlas-based medical image segmentation. Each atlas image is registered to the target image …