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Article

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

1
School of Computer Science and Engineering, University of Westminster, London W1W 6UW, UK
2
Department of Radiology, Harvard Medical School and Boston Children’s Hospital, Boston, MA 02115, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Marcin Ciecholewski and Cosimo Distante
Sensors 2021, 21(23), 7942; https://doi.org/10.3390/s21237942
Received: 18 October 2021 / Revised: 24 November 2021 / Accepted: 25 November 2021 / Published: 28 November 2021
(This article belongs to the Special Issue Advances in Image Segmentation: Theory and Applications)
There is a growing demand for fast, accurate computation of clinical markers to improve renal function and anatomy assessment with a single study. However, conventional techniques have limitations leading to overestimations of kidney function or failure to provide sufficient spatial resolution to target the disease location. In contrast, the computer-aided analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) could generate significant markers, including the glomerular filtration rate (GFR) and time–intensity curves of the cortex and medulla for determining obstruction in the urinary tract. 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 dense nets. (2) Renal contextual information is enhanced via non-linear transformation to segment the cortex and medulla. The proposed framework is evaluated on a paediatric dataset containing 60 4D DCE-MRI volumes exhibiting varying conditions affecting kidney function. Our technique outperforms a state-of-the-art approach based on a GrabCut and support vector machine classifier in mean dice similarity (DSC) by 3.8% and demonstrates higher statistical stability with lower standard deviation by 12.4% and 15.7% for cortex and medulla segmentation, respectively. View Full-Text
Keywords: cortex; DCE-MRI; GFR; kidney; medulla; MR urography; renal compartment; segmentation; time–intensity curve cortex; DCE-MRI; GFR; kidney; medulla; MR urography; renal compartment; segmentation; time–intensity curve
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MDPI and ACS Style

Asaturyan, H.; Villarini, B.; Sarao, K.; Chow, J.S.; Afacan, O.; Kurugol, S. Improving Automatic Renal Segmentation in Clinically Normal and Abnormal Paediatric DCE-MRI via Contrast Maximisation and Convolutional Networks for Computing Markers of Kidney Function. Sensors 2021, 21, 7942. https://doi.org/10.3390/s21237942

AMA Style

Asaturyan H, Villarini B, Sarao K, Chow JS, Afacan O, Kurugol S. Improving Automatic Renal Segmentation in Clinically Normal and Abnormal Paediatric DCE-MRI via Contrast Maximisation and Convolutional Networks for Computing Markers of Kidney Function. Sensors. 2021; 21(23):7942. https://doi.org/10.3390/s21237942

Chicago/Turabian Style

Asaturyan, Hykoush, Barbara Villarini, Karen Sarao, Jeanne S. Chow, Onur Afacan, and Sila Kurugol. 2021. "Improving Automatic Renal Segmentation in Clinically Normal and Abnormal Paediatric DCE-MRI via Contrast Maximisation and Convolutional Networks for Computing Markers of Kidney Function" Sensors 21, no. 23: 7942. https://doi.org/10.3390/s21237942

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