A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI
[article]
2021
arXiv
pre-print
Preserved Fulltext
Other Versions
Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. ...
Our method exploits new deep attentive modules with mixed kernel convolutions within a fully convolutional neural network architecture that utilizes deep supervision and residual connections. ...
Index Terms-Cortical plate, Automatic segmentation, Fetal MRI, Deep learning, Convolutional neural network, Attention
I. ...
arXiv:2004.12847v3
fatcat:r26i6yn4tnhhrn66ld2kfnrp6m
Automatic extraction of the intracranial volume in fetal and neonatal MR scans using convolutional neural networks
2019
NeuroImage: Clinical
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We propose an automatic method for segmentation of the ICV in fetal and neonatal MRI scans. ...
Hence, the algorithm provides a generic tool for segmentation of the ICV that may be used as a preprocessing step for brain tissue segmentation in fetal and neonatal brain MR scans. ...
In another study, Rajchl et al. (2016) investigated the use of crowd sourcing platform for ICV segmentation of fetal MRI using convolutional neural network. ...
doi:10.1016/j.nicl.2019.102061
pmid:31835284
pmcid:PMC6909142
fatcat:kpdiz5b4mbgfzekpvq7xn2pwbu
Automatic segmentation of the intracranialvolume in fetal MR images
[article]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Quantitative analysis of fetal brain development requires automatic brain tissue segmentation that is typically preceded by segmentation of the intracranial volume (ICV). ...
The method employs a multi-scale convolutional neural network in 2D slices to enable learning spatial information from larger context as well as detailed local information. ...
This network has been developed for brain tissue segmentation in neonatal and adult brain MR scans. ...
arXiv:1708.02282v1
fatcat:ltqt5u7fg5drrfaec567q3yxse
Real-time automatic fetal brain extraction in fetal MRI by deep learning
2018
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2017 pre-print arXiv:1710.09338v1 fatcat:kn3hm57jdjc2xf2xdjk6t4mcxa
We aimed to develop a fully automatic segmentation method that independently segments sections of the fetal brain in 2D fetal MRI slices in real-time. ...
With an unprecedented performance and a test run time of about 1 second, our network can be used to segment the fetal brain in real-time while fetal MRI slices are being acquired. ...
CONCLUSION We developed and tested a 2D U-net and a voxelwise fully convolutional network to automatically segment fetal brain in fetal MRI. ...
doi:10.1109/isbi.2018.8363675
dblp:conf/isbi/SalehiHVOEEWG18
fatcat:eqrw7alghjhhdpdtez2hyg6h2e
Front Matter: Volume 10574
2018
Medical Imaging 2018: Image Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
using a Base 36 numbering system employing both numerals and letters. ...
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model. ...
multi-label V-Net 10574 08 Iterative convolutional neural networks for automatic vertebra identification and segmentation in CT images 10574 09 Splenomegaly segmentation using global convolutional kernels ...
doi:10.1117/12.2315755
fatcat:jdfbaent6vhu5dwlrqrqt66vce
Front Matter: Volume 12032
2022
Medical Imaging 2022: Image Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
using a Base 36 numbering system employing both numerals and letters. ...
SPIE uses a seven-digit CID article numbering system structured as follows: The first five digits correspond to the SPIE volume number. The last two digits indicate publication order within the volume ...
[12032-128] 3O Automatic lung segmentation in dynamic thoracic MRI using two-stage deep convolutional neural networks [12032-129] 3P COVID-19 lesion segmentation using convolutional LSTM for self-attention ...
doi:10.1117/12.2638192
fatcat:ikfgnjefaba2tpiamxoftyi6sa
Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review
2021
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
are convolutional neural networks (CNN) and recurrent neural networks (RNN). ...
In this paper we reviewed what have researchers have done in their researches to solve fetal problems, then summarize and carefully discuss the applications in different tasks identified for segmentation ...
[73] Proposed two main methods automatically identifying six normal deep convolutional neural networks planning of the fetus brain. ...
doi:10.5281/zenodo.4621288
fatcat:vzff53p4enfvlonl6kvnsqxqre
Auto-context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging
[article]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Other Versions
fetal brain in MRI. ...
With the aim of designing a learning-based, geometry-independent and registration-free brain extraction tool in this study, we present a technique based on an auto-context convolutional neural network ...
Index Terms-Brain extraction, Whole brain segmentation, MRI, Convolutional neural network, CNN, U-net, Auto-Context.
I. ...
arXiv:1703.02083v2
fatcat:ygwwjqinqzefpn5pnk6dtdl4au
FetalGAN: Automated Segmentation of Fetal Functional Brain MRI Using Deep Generative Adversarial Learning and Multi-Scale 3D U-Net
2022
Frontiers in Neuroscience
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Recently, deep learning-based segmentation models such as convolutional neural networks (CNNs) have been increasingly used for automated segmentation of medical images, including the fetal brain. ...
Here, we propose a computationally efficient end-to-end generative adversarial neural network (GAN) for segmenting the fetal brain. ...
ACKNOWLEDGMENTS We thank the pregnant women who participated in this study. We are grateful to our study team for supporting recruitment, enrollment, and for performing the fetal MRI studies. ...
doi:10.3389/fnins.2022.887634
fatcat:qfaugeueoretllrgqnlngl4puu
Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In fetal brain MRI, abnormalities exacerbate the variability of the developing brain anatomy compared to non-pathological cases. ...
We validated our approach using a dataset of 368 fetal brain T2w MRIs, including 124 MRIs of open spina bifida cases and 51 MRIs of cases with other severe abnormalities of brain development. ...
Introduction The segmentation of fetal brain tissues in MRI is essential for the study of abnormal fetal brain developments [2] . ...
arXiv:2108.04175v1
fatcat:d3zhadjudbhwtfsd4nbcz2ylty
An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
To facilitate this analysis, automatic multi-tissue fetal brain segmentation algorithms are needed, which in turn requires open databases of segmented fetal brains. ...
In addition, we quantitatively evaluate the accuracy of several automatic multi-tissue segmentation algorithms of the developing human fetal brain. ...
Max Cloetta Foundation, the Anna Müller Grocholski Foundation, the Foundation for Research in Science and the Humanities at the UZH, the EMDO Foundation, the Hasler Foundation, the FZK Grant, the Swiss ...
arXiv:2010.15526v3
fatcat:w4bav4vmzjhcxcoecrfbukghc4
Front Matter: Volume 11597
2021
Medical Imaging 2021: Computer-Aided Diagnosis
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
and multiclass image segmentation using deep learning in fetal echocardiography
[11597-45]
v
Proc. of SPIE Vol. 11597 1159701-5
Multi-scale view-based convolutional neural network for breast cancer ...
prostate and dominant lesion segmentation using deep neural network 11597 1M Clinically significant prostate cancer detection on MRI with self-supervised learning using image context restoration 11597 ...
segmentation of small metastatic brain tumors using liquid state machine ensemble 11597 2M Renal parenchyma segmentation in abdominal MR images based on cascaded deep convolutional neural network with ...
doi:10.1117/12.2595447
fatcat:u25cvo7adbgcxb363rsnsgnsju
Longitudinal analysis of fetal MRI in patients with prenatal spina bifida repair
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Using a robust super-resolution algorithm, we reconstructed fetal brains at both pre-operative and post-operative time points and trained a U-Net CNN in order to automatically segment the ventricles. ...
In this work, we present a fetal MRI neuro-imaging analysis pipeline for fetuses with SB, including automated fetal ventricle segmentation and deformation-based morphometry, and demonstrate its applicability ...
Another category of automatic segmentation methods that do not rely on atlases are convolutional neural networks (CNNs). ...
arXiv:1911.06542v1
fatcat:wy6fmynb6nb2hj7on7ogchtw4m
FMRI BRAIN IMAGE SEGMENTATION AND CLASSIFIATION USING BIG DATA ANALYTICS
2020
International Journal of Recent Trends in Engineering and Research
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Enormous progress in accessing brain injury and exploring brain anatomy has been made using magnetic resonance imaging (MRI). ...
Brain MRI segmentation is an essential task in many clinical applications because it influences the outcome of the entire analysis. ...
Alansary, et.al,…presented a fully automatic segmentation framework for the human placenta from motion corrupted fetal MRI scans. ...
doi:10.23883/ijrter.conf.20200315.033.snada
fatcat:rmsslknwwvaozbn6qt2xirp33a
Partial supervision for the FeTA challenge 2021
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
However, perinatal brain MRIs segmented in different datasets typically come with different annotation protocols. This makes it challenging to combine those datasets to train a deep neural network. ...
The performance of convolutional neural networks for medical image segmentation is thought to correlate positively with the number of training data. ...
Methods and Materials In this section, we give the detail of our segmentation pipeline and the data used for training the deep neural networks. ...
arXiv:2111.02408v1
fatcat:p7wgtsswjrh5vfo4uoe5yekbhe
« Previous
Showing results 1 — 15 out of 404 results