Automatic segmentation of polycystic kidneys from magnetic resonance images using decision tree classification and snake algorithm

O’Reilly, Jamie A.; Tanpradit, Sakuntala; Puttasakul, Tasawan; Sangworasil, Manas; Matsuura, T.; Wibulpolprasert, Pornphan; Chousangsuntorn, Khaisang

doi:10.1109/bmeicon47515.2019.8990257

Cited by 4 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Along with other traditional methods [15], CNNs [16,17] have a prominent role in medicinal imagebased analysis, including brain segmentation of magnetic resonance images (MRIs) [18]. MR imaging consists of harmful ionizing radiations, but these radiations do not affect the patients and provide information related to the brain tumor's size, shape, type, and position.…”

Section: Related Workmentioning

confidence: 99%

Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

Asiri¹,

Shaf²,

Ali³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

The brain tumor is an abnormal and hysterical growth of brain tissues, and the leading cause of death affected patients worldwide. Even in this technology-based arena, brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones. The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data. To overcome the highlighted issue, a Generative Adversarial Network (GAN) deep learning technique in which two neural networks compete to become more accurate in creating artificially realistic data for MRI images. The GAN network contains mainly two parts known as generator and discriminator. Commonly, a generator is the convolutional neural network, and a discriminator is the deconvolutional neural network. In this research, the publicly accessible Contrast-Enhanced Magnetic Resonance Imaging (CE-MRI) dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been used. Our proposed method is simple and achieved an accuracy of 96%. We compare our technique results with the existing results, indicating that our proposed technique outperforms the best results associated with the existing methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

Asiri¹,

Shaf²,

Ali³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…Beside other semi-automatic and automatic segmentation techniques such as image processing and model-based image segmentation, machine learning and especially deep learning approaches have again been shown to be the most promising to deal with the more complex multiparametric datasets [73]. DL has already been applied in a few studies for segmentation of the kidneys to estimate TKV [74][75][76][77][78].…”

Section: Data Postprocessing and Analysis: New Strategies With Deep L...mentioning

confidence: 99%

Multiparametric Functional MRI of the Kidney: Current State and Future Trends with Deep Learning Approaches

Zhang¹,

Schwartz

Küstner³

et al. 2022

Rofo

View full text Add to dashboard Cite

Background Until today, assessment of renal function has remained a challenge for modern medicine. In many cases, kidney diseases accompanied by a decrease in renal function remain undetected and unsolved, since neither laboratory tests nor imaging diagnostics provide adequate information on kidney status. In recent years, developments in the field of functional magnetic resonance imaging with application to abdominal organs have opened new possibilities combining anatomic imaging with multiparametric functional information. The multiparametric approach enables the measurement of perfusion, diffusion, oxygenation, and tissue characterization in one examination, thus providing more comprehensive insight into pathophysiological processes of diseases as well as effects of therapeutic interventions. However, application of multiparametric fMRI in the kidneys is still restricted mainly to research areas and transfer to the clinical routine is still outstanding. One of the major challenges is the lack of a standardized protocol for acquisition and postprocessing including efficient strategies for data analysis. This article provides an overview of the most common fMRI techniques with application to the kidney together with new approaches regarding data analysis with deep learning. Methods This article implies a selective literature review using the literature database PubMed in May 2021 supplemented by our own experiences in this field. 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, and BOLD imaging. New approaches with the application of deep learning techniques could substantially contribute to overcoming the challenge of handling the quantity of data and developing more efficient data postprocessing and analysis protocols. 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 kidney diseases in the future. Key Points: Citation Format

show abstract

“…O'Reilly et al [46] used a decision tree classification and snake algorithm for TKV segmentation in polycystic kidney disease. In this two step appraoch first a decision tree was trained to roughly detect the kidneys while in a second step an active contour algorithm was used to segment the kidney outline.…”

Section: Applications Using Image-and Model-based Techniquesmentioning

confidence: 99%

“…O'Reilley et al implemented a 3D fully-convolutional network (FCN) to estimate TKV. The network was adapted from [133] and trained on 155 datasets of about 50 patients [134]. In the work of Bevilacqua et al two approaches are combined [88].…”

Section: E Applications Using Deep Learning Based Segmentationmentioning

confidence: 99%

Kidney Segmentation in Renal Magnetic Resonance Imaging - Current Status and Prospects

et al. 2021

View full text Add to dashboard Cite

Magnetic resonance imaging has achieved an increasingly important role in the clinical workup of renal diseases such chronic kidney disease (CKD). A large panel of parameters have been proposed to diagnose CKD among them total kidney volume (TKV) which recently qualified as biomarker. Volume estimation in renal MRI is based on image segmentation of the kidney and/or its compartments. Beyond volume estimation renal segmentation supports also the quantification of other MR based parameters such as perfusion or filtration. The aim of the present article is to discuss the recent existing literature on renal image segmentation techniques and show today's limitations of the proposed techniques that might hinder clinical translation. We also provide pointers to open source software related to renal image segmentation.

show abstract

Automatic segmentation of polycystic kidneys from magnetic resonance images using decision tree classification and snake algorithm

Cited by 4 publications

References 15 publications

Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

Multiparametric Functional MRI of the Kidney: Current State and Future Trends with Deep Learning Approaches

Kidney Segmentation in Renal Magnetic Resonance Imaging - Current Status and Prospects

Contact Info

Product

Resources

About