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A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI [article]

Haoran Dou, Davood Karimi, Caitlin K. Rollins, Cynthia M. Ortinau, Lana Vasung, Clemente Velasco-Annis, Abdelhakim Ouaalam, Xin Yang, Dong Ni, Ali Gholipour
2021 arXiv   pre-print
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

N. Khalili, E. Turk, M.J.N.L. Benders, P. Moeskops, N.H.P. Claessens, R. de Heus, A. Franx, N. Wagenaar, J.M.P.J. Breur, M.A. Viergever, I. Išgum
2019 NeuroImage: Clinical  
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]

N. Khalili, P. Moeskops, N.H.P. Claessens, S. Scherpenzeel, E. Turk, R. de Heus, M.J.N.L. Benders, M.A. Viergever, J.P.W. Pluim, I. Išgum
2017 arXiv   pre-print
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

Seyed Sadegh Mohseni Salehi, Seyed Raein Hashemi, Clemente Velasco-Annis, Abdelhakim Ouaalam, Judy A. Estroff, Deniz Erdogmus, Simon K. Warfield, Ali Gholipour
2018 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)  
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

Proceedings of SPIE, Elsa D. Angelini, Bennett A. Landman
2018 Medical Imaging 2018: Image Processing  
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

Ivana Išgum, Olivier Colliot
2022 Medical Imaging 2022: Image Processing  
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

Fairoz Q. Kareem, Adnan Mohsin Abdulazeez
2021 Zenodo  
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]

Seyed Sadegh Mohseni Salehi, Deniz Erdogmus, Ali Gholipour
2017 arXiv   pre-print
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

Josepheen De Asis-Cruz, Dhineshvikram Krishnamurthy, Chris Jose, Kevin M. Cook, Catherine Limperopoulos
2022 Frontiers in Neuroscience  
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]

Lucas Fidon, Michael Aertsen, Nada Mufti, Thomas Deprest, Doaa Emam, Frédéric Guffens, Ernst Schwartz, Michael Ebner, Daniela Prayer, Gregor Kasprian, Anna L. David, Andrew Melbourne (+4 others)
2021 arXiv   pre-print
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]

Kelly Payette, Priscille de Dumast, Hamza Kebiri, Ivan Ezhov, Johannes C. Paetzold, Suprosanna Shit, Asim Iqbal, Romesa Khan, Raimund Kottke, Patrice Grehten, Hui Ji, Levente Lanczi (+8 others)
2021 arXiv   pre-print
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

Karen Drukker, Maciej A. Mazurowski
2021 Medical Imaging 2021: Computer-Aided Diagnosis  
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]

Kelly Payette, Ueli Moehrlen, Luca Mazzone, Nicole Ochsenbein-Koelble, Ruth Tuura, Raimund Kottke, Martin Meuli, Andras Jakab
2019 arXiv   pre-print
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

K. Mahadevan, S. parkavi
2020 International Journal of Recent Trends in Engineering and Research  
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]

Lucas Fidon, Michael Aertsen, Suprosanna Shit, Philippe Demaerel, Sébastien Ourselin, Jan Deprest, Tom Vercauteren
2021 arXiv   pre-print
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
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