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

Asaturyan, H., Villarini, Barbara, Sarao, Karen, Chow, Jeanne S, Afacan, Onur and Kurugol, Sila 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 (23) 7942. https://doi.org/10.3390/s21237942

TitleImproving Automatic Renal Segmentation in Clinically Normal and Abnormal Paediatric DCE-MRI via Contrast Maximisation and Convolutional Networks for Computing Markers of Kidney Function.
TypeJournal article
AuthorsAsaturyan, H., Villarini, Barbara, Sarao, Karen, Chow, Jeanne S, Afacan, Onur and Kurugol, Sila
AbstractThere 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.
KeywordsGFR
renal compartment
Biomarkers
Humans
medulla
Kidney - diagnostic imaging - physiology
Magnetic Resonance Imaging
kidney
segmentation
Neural Networks, Computer
MR urography
Contrast Media
Child
DCE-MRI
time–intensity curve
cortex
Image Processing, Computer-Assisted
Article number7942
JournalSensors
Journal citation21 (23)
ISSN1424-8220
Year2021
PublisherMDPI
Publisher's version
License
CC BY 4.0
File Access Level
Open (open metadata and files)
Digital Object Identifier (DOI)https://doi.org/10.3390/s21237942
PubMed ID34883946
Web address (URL)https://www.mdpi.com/1424-8220/21/23/7942
Publication dates
Published28 Nov 2021
Published online28 Nov 2021
Supplemental file
File Access Level
Open (open metadata and files)
ProjectLTRF1920\16\26
1R21DK123569-01
FunderLeverhulme Trust
NIDDK NIH HHS

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