Title | Improving Automatic Renal Segmentation in Clinically Normal and Abnormal Paediatric DCE-MRI via Contrast Maximisation and Convolutional Networks for Computing Markers of Kidney Function. |
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Type | Journal article |
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Authors | Asaturyan, H., Villarini, Barbara, Sarao, Karen, Chow, Jeanne S, Afacan, Onur and Kurugol, Sila |
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Abstract | 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. |
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Keywords | GFR |
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| renal compartment |
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| Biomarkers |
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| Humans |
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| medulla |
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| Kidney - diagnostic imaging - physiology |
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| Magnetic Resonance Imaging |
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| kidney |
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| segmentation |
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| Neural Networks, Computer |
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| MR urography |
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| Contrast Media |
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| Child |
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| DCE-MRI |
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| time–intensity curve |
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| cortex |
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| Image Processing, Computer-Assisted |
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Article number | 7942 |
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Journal | Sensors |
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Journal citation | 21 (23) |
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ISSN | 1424-8220 |
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Year | 2021 |
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Publisher | MDPI |
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Publisher's version | License CC BY 4.0 File Access Level Open (open metadata and files) |
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Digital Object Identifier (DOI) | https://doi.org/10.3390/s21237942 |
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PubMed ID | 34883946 |
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Web address (URL) | https://www.mdpi.com/1424-8220/21/23/7942 |
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Publication dates |
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Published | 28 Nov 2021 |
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Published online | 28 Nov 2021 |
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Supplemental file | File Access Level Open (open metadata and files) |
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Project | LTRF1920\16\26 |
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| 1R21DK123569-01 |
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Funder | Leverhulme Trust |
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| NIDDK NIH HHS |
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