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Quantification of renal perfusion based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) requires determination of signal intensity time courses in the region of renal parenchyma. Thus, selection of voxels representing the kidney must be accomplished with special care and constitutes one of the major technical limitations which hampers wider usage of this technique as a standard clinical routine. Manual segmentation of renal compartments-even if performed by experts-is a common source of decreased repeatability and reproducibility. In this paper, we present a processing framework for the automatic kidney segmentation in DCE-MR images. The framework consists of two stages. Firstly, kidney masks are generated using a convolutional neural network. Then, mask voxels are classified to one of three regions-cortex, medulla, and pelvis-based on DCE-MRI signal intensity time courses. The proposed approach was evaluated on a cohort of 10 healthy volunteers who underwent the DCE-MRI examination. MRI scanning was repeated on two time events within a 10-day interval. For semantic segmentation task we employed a classic U-Net architecture, whereas experiments on voxel classification were performed using three alternative algorithms-support vector machines, logistic regression and extreme gradient boosting trees, among which SVM produced the most accurate results. Both segmentation and classification steps were accomplished by a series of models, each trained separately for a given subject using the data from other participants only. The mean achieved accuracy of the whole kidney segmentation was 94% in terms of IoU coefficient. Cortex, medulla and pelvis were segmented with IoU ranging from 90 to 93% depending on the tissue and body side. The results were also validated by comparing image-derived perfusion parameters with ground truth measurements of glomerular filtration rate (GFR). The repeatability of GFR calculation, as assessed by the coefficient of variation was determined at the level of 14.5 and 17.5% for the left and right kidney, respectively and it improved relative to manual segmentation. Reproduciblity, in turn, was evaluated by measuring agreement between image-derived and iohexol-based GFR values. The estimated absolute mean differences were equal to 9.4 and 12.9 mL/min/1.73 m 2 for scanning sessions 1 and 2 and the proposed automated segmentation method. The result for session 2 was comparable with manual segmentation, whereas for session 1 reproducibility in the automatic pipeline was weaker.

• This paper proposes a novel architecture of a deep convolutional neural network consisting of a shared encoder and three parallel decoders specialized in kidney detection, renal parenchyma and cortex segmentation in contrast enhanced magnetic resonance imaging (CE-MRI). • The proposed network was validated in the leave-one-subject-out manner based on the cohort of 15 patients with diagnosed renal artery stenosis and 10 healthy volunteers. Healthy volunteers were scanned twice using the dynamic CE-MRI method. In between of the scanning sessions, renal performance of each subject was evaluated using iohexol clearance procedure to establish the ground-truth value of the glomerular filtration rate (GFR) and enable validation of the image-derived GFR measurements. • The obtained segmentation accuracy in terms of the Jaccard coefficient amounted to 94% for the whole kidney, and 76-92% for the renal cortex depending on the level of kidney tissue atrophy due to renal artery stenosis. • The average calculated volume of the whole kidney and renal cortex in the group of healthy subjects amounted to 160 and 102 cm3 respectively, as determined using the automatically found renal segments. The whole kidney volume decreases with the advancement of renal artery stenosis from 130 (mild stage) to 86 cm3 (severe). Similarly, the average renal cortex volume decreased from 106 to 69 cm3. • Repeatability of whole kidney and renal cortex volume measurements (healthy subjects) based on the proposed automatic segmentation procedure was in the range between 5.2% and 9.9% of the coefficient of variation. Repeatability of the image-derived single-kidney GFR measurements for the left and right kidney were correspondingly 15% and 21.0%, and stability improves in relation to calculations based on manual segmentations. Contrast-enhanced magnetic resonance imaging (CE-MRI) is one of the methods routinely used in clinics for the diagnosis of renal impairments. It allows assessment of kidney perfusion and also visualization of various lesions and tissue atrophy due to e.g. renal artery stenosis (RAS). An important indicator of the renal tissue state is the volume and shape of the kidney. Therefore it is highly desirable to equip radiological units in clinics with the software capable of automatic segmentation of the kidneys in CE-MRI images. This paper proposes a solution to this task using an original architecture of a deep neural network. The proposed design employs a three-branch convolutional neural network specialized in: 1) detection of renal parenchyma within an MR image patch, 2) segmentation of the whole kidney and 3) annotation of the renal cortex. We tested our architecture for normal kidneys in healthy subjects and for poorly perfused organs in RAS patients. The accuracy of renal parenchyma segmentation was equal to 0.94 in terms of the intersection over union (IoU) ratio. Accuracy of the cortex segmentation depends on the level of tissue health condition and ranges from 0.76 up to 0.92 of IoU. [ABSTRACT FROM AUTHOR]

In this paper we present a numerical framework for validating methods of quantitative analysis of non-invasive MR angiography imaging protocols such as Time-of-Flight (ToF) and Phase Contrast Angiography (PCA). Our study is motivated by the need to reliably and objectively verify blood flow and geometry measurements derived from image data. Both factors are important predictors in diagnosing of carotid artery stenosis. Credibility of the tested image processing methods is verified by comparing their results against reference models designed using integrated flow and MRA imaging simulator. [ABSTRACT FROM PUBLISHER]

Background: With the development of versatile magnetic resonance acquisition techniques there arises a need for more advanced imaging simulation tools to enable adequate image appearance prediction, measurement sequence design and testing thereof. Recently, there is a growing interest in phase contrast angiography (PCA) sequence due to the capabilities of blood flow quantification that it offers. Moreover, as it is a non-contrast enhanced protocol, it has become an attractive option in areas, where usage of invasive contrast agents is not indifferent for the imaged tissue. Monitoring of the kidney function is an example of such an application.Results: We present a computer framework for simulation of the PCA protocol, both conventional and accelerated with echo-planar imaging (EPI) readout, and its application to the numerical models of kidney vasculatures. Eight patient-specific renal arterial trees were reconstructed following vessel segmentation in real computed tomography angiograms. In addition, a synthetic model was designed using a vascular tree growth simulation algorithm. The results embrace a series of synthetic PCA images of the renal arterial trees giving insight into the image formation and quantification of kidney hemodynamics.Conclusions: The designed simulation framework enables quantification of the PCA measurement error in relation to ground-truth flow velocity data. The mean velocity measurement error for the reconstructed renal arterial trees range from 1.5 to 12.8% of the aliasing velocity value, depending on image resolution and flip angle. No statistically significant difference was observed between measurements obtained using EPI with a number of echos (NETL) = 4 and conventional PCA. In case of higher NETL factors peak velocity values can be underestimated up to 34%. [ABSTRACT FROM AUTHOR]

Abstract: Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correction of partial volume effect in PET images, acquired with PET/MR scanners. This article presents briefly a MaZda software package, which supports 2D and 3D medical image analysis aiming at quantification of image texture. MaZda implements procedures for evaluation, selection and extraction of highly discriminative texture attributes combined with various classification, visualization and segmentation tools. Examples of MaZda application in medical studies are also provided. [Copyright &y& Elsevier]

Abstract: MaZda, a software package for 2D and 3D image texture analysis is presented. It provides a complete path for quantitative analysis of image textures, including computation of texture features, procedures for feature selection and extraction, algorithms for data classification, various data visualization and image segmentation tools. Initially, MaZda was aimed at analysis of magnetic resonance image textures. However, it revealed its effectiveness in analysis of other types of textured images, including X-ray and camera images. The software was utilized by numerous researchers in diverse applications. It was proven to be an efficient and reliable tool for quantitative image analysis, even in more accurate and objective medical diagnosis. MaZda was also successfully used in food industry to assess food product quality. MaZda can be downloaded for public use from the Institute of Electronics, Technical University of Lodz webpage. [Copyright &y& Elsevier]