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Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks [article]

Marzieh Haghighi, Simon K. Warfield, Sila Kurugol
2017 arXiv   pre-print
Automatic segmentation of renal parenchyma is an important step in this process.  ...  Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children.  ...  METHODS In this section, we describe a memory efficient renal segmentation framework which automatically segments kidneys given a 4D DCE-MRI as input.  ... 
arXiv:1712.07022v1 fatcat:o3bqhosmsncfxgywgky4p7aku4

Automatic renal segmentation in DCE-MRI using convolutional neural networks

Marzieh Haghighi, Simon K. Warfield, Sila Kurugol
2018 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)  
Automatic segmentation of renal parenchyma is an important step in this process.  ...  Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children.  ...  METHODS In this section, we describe a memory efficient renal segmentation framework which automatically segments kidneys given a 4D DCE-MRI as input.  ... 
doi:10.1109/isbi.2018.8363865 pmid:30473744 pmcid:PMC6248325 dblp:conf/isbi/HaghighiWK18 fatcat:g7nivbeke5dsppjhy535w63zra

Improving Automatic Renal Segmentation in Clinically Normal and Abnormal Paediatric DCE-MRI via Contrast Maximisation and Convolutional Networks for Computing Markers of Kidney Function

Hykoush Asaturyan, Barbara Villarini, Karen Sarao, Jeanne S. Chow, Onur Afacan, Sila Kurugol
2021 Sensors  
This paper presents a dual-stage fully modular framework for automatic renal compartment segmentation in 4D DCE-MRI volumes. (1) Memory-efficient 3D deep learning is integrated to localise each kidney  ...  by harnessing residual convolutional neural networks for improved convergence; segmentation is performed by efficiently learning spatial–temporal information coupled with boundary-preserving fully convolutional  ...  Acknowledgments: We gratefully acknowledge the support of the NVIDIA Corporation with the donation of the Quadro P6000 used for this research.  ... 
doi:10.3390/s21237942 pmid:34883946 fatcat:vxkncgydubg7diq5kmkddsop64

Healthy Kidney Segmentation in the Dce-Mr Images Using a Convolutional Neural Network and Temporal Signal Characteristics

Artur Klepaczko, Eli Eikefjord, Arvid Lundervold
2021 Sensors  
Firstly, kidney masks are generated using a convolutional neural network.  ...  In this paper, we present a processing framework for the automatic kidney segmentation in DCE-MR images. The framework consists of two stages.  ...  U-Net architecture of the convolutional neural network implemented for semantic segmentation of kidneys in the DCE-MR images. Figure 3 . 3 Figure 3.  ... 
doi:10.3390/s21206714 pmid:34695931 fatcat:h5f7k5y545c7dhppqsbuh2xmhe

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

Front Matter: Volume 10134

2017 Medical Imaging 2017: Computer-Aided Diagnosis  
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.  ...  09 3D convolutional neural network for automatic detection of lung nodules in chest CT 10134 0A Automatic detection of lung nodules: false positive reduction using convolution neural networks and handcrafted  ... 
doi:10.1117/12.2277119 dblp:conf/micad/X17 fatcat:ika7pheqxngdxejyvkss4dkbv4

Spectral Decomposition in Deep Networks for Segmentation of Dynamic Medical Images [article]

Edgar A. Rios Piedra, Morteza Mardani, Frank Ong, Ukash Nakarmi, Joseph Y. Cheng, Shreyas Vasanawala
2020 arXiv   pre-print
Dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI) is a widely used multi-phase technique routinely used in clinical practice.  ...  This work attempts to increase the training efficacy and performance of deep networks by determining redundant information in the spatial and spectral components and show that the performance of segmentation  ...  In lieu of the manual segmentation, deep neural networks have been proved to outperform previous methods [1, 2] by significant margins, but there are challenges that still need to be solved.  ... 
arXiv:2010.00003v1 fatcat:fkmz2ebatnaormkponq2o6qjjy

Kidney segmentation in renal magnetic resonance imaging – current status and prospects

Frank G. Zollner, Marek Kocinski, Laura Hansen, Alena-Kathrin Golla, Amira Serifovic Trbalic, Arvid Lundervold, Andrzej Materka, Peter Rogelj
2021 IEEE Access  
Volume estimation in renal MRI is based on image segmentation of the kidney and/or its compartments.  ...  We also provide pointers to open source software related to renal image segmentation.  ...  FIGURE 10 : 10 FIGURE 10: Fast semi-supervised segmentation of the kidneys in 4D DCE-MRI using convolutional neural networks and transfer learning from brain hippocampus segmentation.  ... 
doi:10.1109/access.2021.3078430 fatcat:lmzeqqaf4jcahgzk2p5t45qroi

A Multi-Layer Perceptron Network for Perfusion Parameter Estimation in DCE-MRI Studies of the Healthy Kidney

Artur Klepaczko, Michał Strzelecki, Marcin Kociołek, Eli Eikefjord, Arvid Lundervold
2020 Applied Sciences  
The advantages of using a neural network are twofold. Firstly, it can estimate a GFR value without the need to determine the AIF for each individual patient.  ...  Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an imaging technique which helps in visualizing and quantifying perfusion—one of the most important indicators of an organ's state.  ...  In this study, we implicitly follow the population-based approach. The neural network is trained using a pool of AIFs automatically derived for a subset of available DCE images.  ... 
doi:10.3390/app10165525 fatcat:azxu3n3w3zhrthi67f4toct74i

An overview of deep learning in medical imaging focusing on MRI

Alexander Selvikvåg Lundervold, Arvid Lundervold
2018 Zeitschrift für Medizinische Physik  
Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry.  ...  The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks.  ...  Acknowledgements We thank Renate Grüner for useful discussions. The anonymous reviewers gave us excellent constructive feedback that led to several improvements throughout the article.  ... 
doi:10.1016/j.zemedi.2018.11.002 fatcat:kkimovnwcrhmth7mg6h6cpomjm

Image registration in dynamic renal MRI—current status and prospects

Frank G. Zöllner, Amira Šerifović-Trbalić, Gordian Kabelitz, Marek Kociński, Andrzej Materka, Peter Rogelj
2019 Magnetic Resonance Materials in Physics, Biology and Medicine  
However, a standardized evaluation of image registration in renal MRI is still missing.  ...  To overcome motion artifacts in time-resolved renal MRI, a wide range of strategies have been proposed.  ...  Unlike in Buerger et al. where a local adaptive affine registration algorithm (LREG) was used [54] , the motion-induced image deformations were corrected with the use of a convolutional neural network  ... 
doi:10.1007/s10334-019-00782-y pmid:31598799 fatcat:vmbq3ehs55gctf3mmdxh4xppzu

Multiparametric Functional MRI of the Kidney: Current State and Future Trends with Deep Learning Approaches
Multiparametrische funktionelle Nierenbildgebung in der MRT: Aktueller Status und zukunftsweisende Entwicklungen mit Deep Learning

Cecilia Zhang, Martin Schwartz, Thomas Küstner, Petros Martirosian, Ferdinand Seith
2022 RöFo. Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren (Print)  
Results and Conclusion Functional multiparametric MRI is a promising technique for assessing renal function in a more comprehensive approach by combining multiple parameters such as perfusion, diffusion  ...  Thus, it can be hoped that multiparametric fMRI protocols can be sufficiently optimized to be used for routine renal examination and to assist clinicians in the diagnostics, monitoring, and treatment of  ...  DL techniques with application in medical imaging are mostly based on convolutional neural networks (CNNs) to learn useful representations of images and other structured data [50] .  ... 
doi:10.1055/a-1775-8633 pmid:35272360 fatcat:ddtlacc7jvewleepmoh6jtxnnq

Imaging-Based Staging of Hepatic Fibrosis in Patients with Hepatitis B: A Dynamic Radiomics Model Based on Gd-EOB-DTPA-Enhanced MRI

Rencheng Zheng, Chunzi Shi, Chengyan Wang, Nannan Shi, Tian Qiu, Weibo Chen, Yuxin Shi, He Wang
2021 Biomolecules  
Imaging features were extracted from the deep learning-based segmented liver volume, and time-domain features were further explored to analyze the variation in features during contrast enhancement.  ...  Time-domain features were found to play an important role in the classification models. The dynamic radiomics model can be applied for highly accurate automatic hepatic fibrosis staging.  ...  Acknowledgments: The authors wish to acknowledge the medical doctors in the Shanghai Public Health Clinical Center for data collection, and all members of the Institute of Science and Technology for Brain-inspired  ... 
doi:10.3390/biom11020307 pmid:33670596 pmcid:PMC7922315 fatcat:gzlvmdo7krdipdjnigpb66h6li

BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning

Jiejie Zhou, Yan-Lin Liu, Yang Zhang, Jeon-Hor Chen, Freddie J. Combs, Ritesh Parajuli, Rita S. Mehta, Huiru Liu, Zhongwei Chen, Youfan Zhao, Zhifang Pan, Meihao Wang (+2 others)
2021 Frontiers in Oncology  
For each case, the 3D tumor mask was generated using Fuzzy-C-Means segmentation.  ...  BackgroundA wide variety of benign and malignant processes can manifest as non-mass enhancement (NME) in breast MRI.  ...  Prediction of Breast Cancer Molecular Subtypes on DCE-MRI Using Convolutional Neural Network With Transfer Learning Between Two Centers.  ... 
doi:10.3389/fonc.2021.728224 pmid:34790569 pmcid:PMC8591227 fatcat:sf2pp7nu2vavndcfkdyugsoznq

Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model

Fuat Türk, Murat Lüy, Necaattin Barışçı
2020 Mathematics  
In such cases where segmentation is difficult, V-Net-based models are mostly used. This paper proposes a new hybrid model using the superior features of existing V-Net models.  ...  The hybrid V-Net model exhibited average Dice coefficients of 97.7% and 86.5% for kidney and tumor segmentation, respectively, and, therefore, could be used as a reliable method for soft tissue organ segmentation  ...  Acknowledgments: We would like to thank Tubitak TRUBA for its services in artificial intelligence. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/math8101772 fatcat:llqd3k5ulbg25dlydqzbqtc6k4
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