Functional Segmentation of Renal DCE-MRI Sequences Using Vector Quantization Algorithms

Chevaillier, Béatrice; Mandry, Damien; Collette, Jean-Luc; Claudon, M.; Galloy, M. A.; Pietquin, Olivier

doi:10.1007/s11063-011-9184-y

Cited by 11 publications

(16 citation statements)

References 16 publications

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“…Percentage extra (PE) represented the ratio of the number of pixels that were in the test segmentation and out of reference segmentation, to the number of pixels in the reference segmentation. A high PO associated with a low PE indicated a good agreement of ROI between the reference segmentation and test segmentation . The Dice coefficient (DC), a commonly used index, was used to validate the efficiency of the test segmentation algorithm .…”

Section: Methodsmentioning

confidence: 99%

“…Due to the existence of noise and respiratory motion, simply clustering the pixels using signal variation in the time domain without utilizing the information and prior knowledge of the spatial domain can easily result in obvious errors in the output clusters. This has been reflected in the fact that the output compartments obtained simply by clustering the time–intensity signals into three groups are not always satisfactory and the clustering algorithm is sensitive to the noise and deviation of the time–intensity signals . To tackle this problem, previous researchers tried to cluster the pixels into more than three groups and subsequently merge those groups into three compartments, that is, the cortex, the medulla, and the pelvis compartments.…”

Section: Introductionmentioning

confidence: 99%

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A self‐supervised strategy for fully automatic segmentation of renal dynamic contrast‐enhanced magnetic resonance images

Huang

Wang

et al. 2019

Medical Physics

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Purpose An automated accurate segmentation for dynamic contrast‐enhanced magnetic resonance (DCE‐MR) image sequences is essential for quantification of renal function. A self‐supervised strategy is proposed for fully automatic segmentation of the renal DCE‐MR images without using manually labeled data. Methods The proposed strategy employed both temporal and spatial information of the DCE‐MR image sequences. First, the kidney area, the seed regions of the cortex, the medulla, and the pelvis were automatically detected in the spatial domain. Subsequently, all the pixels in the kidney were automatically labeled as the cortex, the medulla, or the pelvis based on their time–intensity signal and spatial position using a supervised classifier. The feasibility of the proposed strategy was verified on a dataset of renal DCE‐MR images of 14 subjects without history of kidney diseases. Furthermore, the self‐supervised strategy and the commonly used traditional unsupervised method were quantitatively compared with a reference manual segmentation by an experienced radiologist, using similarity indexes. Results The average Dice coefficient (ADC) for the segmentations of the proposed self‐supervised method is 0.92 using a ransom walker model as the classifier or 0.86 using a K‐nearest neighbor model as the classifier. The ADC of the Kmeans‐based unsupervised methods with three and six clusters were 0.65 and 0.79, respectively. The Dice coefficients of the self‐supervised method were remarkably higher than that of the unsupervised method (one‐tailed paired‐sample t‐test, P‐values <10−3). Conclusions The results indicate that the proposed self‐supervised approach yields a satisfactory similarity with the reference manual segmentation. Compared with the traditional unsupervised clustering method, the new strategy does not require manual intervention during the segmentation process and achieves better results for the segmentation of renal DCE‐MR images.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A self‐supervised strategy for fully automatic segmentation of renal dynamic contrast‐enhanced magnetic resonance images

Huang

Wang

et al. 2019

Medical Physics

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“…A semi‐automated framework by Chevaillier et al . segmented the kidney and its internal structures (i.e. cortex, medulla) using the k ‐means partitioning algorithm.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive non‐invasive framework for automated evaluation of acute renal transplant rejection using DCE‐MRI

et al. 2013

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The objective was to develop a novel and automated comprehensive framework for the non-invasive identification and classification of kidney non-rejection and acute rejection transplants using 2D dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The proposed approach consists of four steps. First, kidney objects are segmented from the surrounding structures with a geometric deformable model. Second, a non-rigid registration approach is employed to account for any local kidney deformation. In the third step, the cortex of the kidney is extracted in order to determine dynamic agent delivery, since it is the cortex that is primarily affected by the perfusion deficits that underlie the pathophysiology of acute rejection. Finally, we use an analytical function-based model to fit the dynamic contrast agent kinetic curves in order to determine possible rejection candidates. Five features that map the data from the original data space to the feature space are chosen with a k-nearest-neighbor (KNN) classifier to distinguish between acute rejection and non-rejection transplants. Our study includes 50 transplant patients divided into two groups: 27 patients with stable kidney function and the remainder with impaired kidney function. All of the patients underwent DCE-MRI, while the patients in the impaired group also underwent ultrasound-guided fine needle biopsy. We extracted the kidney objects and the renal cortex from DCE-MRI for accurate medical evaluation with an accuracy of 0.97 ± 0.02 and 0.90 ± 0.03, respectively, using the Dice similarity metric. In a cohort of 50 participants, our framework classified all cases correctly (100%) as rejection or non-rejection transplant candidates, which is comparable to the gold standard of biopsy but without the associated deleterious side-effects. Both the 95% confidence interval (CI) statistic and the receiver operating characteristic (ROC) analysis document the ability to separate rejection and non-rejection groups. The average plateau (AP) signal magnitude and the gamma-variate model functional parameter α have the best individual discriminating characteristics.

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“…The constraint was built using a Poisson probability distribution and distance maps. Chevaillier et al [357,358] proposed a semi-automated method to segment internal structures (i.e., cortex, medulla) from DCE-MRI using k-means-based partitioning to classify pixels according to contrast evolution using a vector quantization algorithm.…”

Section: Related Work On Image Analysis For Acute Renal Rejectionmentioning

confidence: 99%

“…Chevaillier et al [357,358] • 2D, semi-automated • k-means based clustering based on contrast evolution using a vector quantization algorithm …”

Section: Related Work On Image Analysis For Acute Renal Rejectionmentioning

confidence: 99%

Analysis of contrast-enhanced medical images.

Khalifa¹

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DEDICATIONThis dissertation is dedicated to my mother. Words cannot express how much I wish that she was still with me to see the completion of this endeavor.iii ACKNOWLEDGMENTSIn the name of Allah the most merciful, the most compassionate. All deepest thanks are due to Almighty Allah for the uncountable gifts given to me.I would like to thank my dissertation advisor, Dr. Ayman El-Baz, for his continuous encouragement, guidance, and support over the course of my Ph.D. study. I thank him for giving me the opportunity to work in his research group and providing me the opportunity to work on promising applications and meet interesting people. Without his expertise, guidance, and commitment, my research would not have been possible. I truly appreciate his optimism whenever things looked impossible.I also express my deepest gratitude to Dr. Karla Conn Welch, my dissertation coadvisor, and Dr. Tamer Inanc, Dr. Cindy Harnett, Dr. Olfa Nasraoui, and Dr. Jake Wildstrom for being on my dissertation committee with enthusiasm and taking interest in my research in the midst of many other responsibilities and commitments.I also have to thank Dr. Georgy Gimel'farb for his useful discussions and valuable comments and feedback. He has never hesitated to share his experience in Markov-Gibbs random field and the field of image processing. I also would like to thank Dr. Mohamed Abou El-Ghar of the radiology department, Urology and Nephrology Center, University of Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection,(ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer.However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfuvi sion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building n...

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Functional Segmentation of Renal DCE-MRI Sequences Using Vector Quantization Algorithms

Cited by 11 publications

References 16 publications

A self‐supervised strategy for fully automatic segmentation of renal dynamic contrast‐enhanced magnetic resonance images

A self‐supervised strategy for fully automatic segmentation of renal dynamic contrast‐enhanced magnetic resonance images

A comprehensive non‐invasive framework for automated evaluation of acute renal transplant rejection using DCE‐MRI

Analysis of contrast-enhanced medical images.

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