Automated renal segmentation in healthy and chronic kidney disease subjects using a convolutional neural network

Daniel, Alexander; Buchanan, Charlotte; Allcock, Thomas Tunstall; Scerri, Daniel; Cox, Eleanor; Prestwich, Benjamin; Francis, Susan T.

doi:10.1002/mrm.28768

Cited by 49 publications

(38 citation statements)

References 35 publications

(63 reference statements)

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“…We achieved further improvement with quick manual adjustment of the CNN-segmentations. The Dice similarity metrics we achieved are comparable to what is reported for inter/intra-operator variability in manual organ segmentation (29). A possible refinement is to train the CNN to use both CT and SPECT information, which may be beneficial to identify cysts included in kidney contours (Fig.…”

Section: Discussionsupporting

confidence: 73%

A pipeline for automated voxel dosimetry: application in patients with multi-SPECT/CT imaging following ¹⁷⁷Lu peptide receptor radionuclide therapy

et al. 2022

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Patient-specific dosimetry in radiopharmaceutical therapy (RPT) is impeded by the lack of tools that are accurate and practical for the clinic. The aims were to construct and test an integrated voxel-level pipeline that automates key components (organ segmentation, registration, doserate estimation, and curve fitting) of the RPT dosimetry process and then to use it to report patient specific dosimetry in 177Lu-DOTATATE therapy. Methods. An integrated workflow that automates the entire dosimetry process, except tumor segmentation, was constructed. 1) Convolutional neural networks (CNNs) are used to auto-segment organs on CT of SPECT/CT;2) Local contour intensity-based SPECT-SPECT alignment results in volume-of-interest propagation to other timepoints; 3) Dose-rate estimation is performed by explicit Monte Carlo (MC) using the fast, Dose-Planning Method code; 4) The optimal function for dose-rate fitting is automatically selected for each voxel. When reporting mean dose, partial volume correction is applied, and uncertainty is estimated by an empirical approach of perturbing segmentations.Results. The workflow was used with 4-timepoint SPECT/CT imaging data from 20 patients with 77 neuroendocrine tumors, segmented by a radiologist. CNN-defined kidneys resulted in high Dice values (0.91-0.94) and only small differences (2-5%) in mean dose when compared with manual segmentation. Contour intensity-based registration led to visually enhanced alignment and the voxel-level fitting had high R 2 values. Across patients, dosimetry results were highly variable, for example, the average (range) of the mean absorbed dose in Gy/GBq were:

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Section: Discussionsupporting

confidence: 73%

A pipeline for automated voxel dosimetry: application in patients with multi-SPECT/CT imaging following ¹⁷⁷Lu peptide receptor radionuclide therapy

et al. 2022

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“…Recent studies on machine learning‐based renal segmentation using neural networks reported processing times as good as 1 to 10 s per subject 76‐78 . Although these processing times are superior to our analytic approach, the effort needed to setup meticulously annotated imaging data that can be used to train, validate and test artificial intelligence algorithms must also be included in order to make a fair benchmarking of processing times.…”

Section: Discussionmentioning

confidence: 96%

“…55,[72][73][74][75][76] Recent studies on machine learning-based renal segmentation using neural networks reported processing times as good as 1 to 10 s per subject. [76][77][78] Although these processing times are superior to our analytic approach, the effort needed to setup meticulously annotated imaging data that can be used to train, validate and test artificial intelligence algorithms must also be included in order to make a fair benchmarking of processing times. Nevertheless, the processing time of the proposed ABSM can be substantially reduced by the application of state-of-the-art global optimization methods like evolutionary algorithms, swarm-based methods, or simulated annealing.…”

Section: Discussionmentioning

confidence: 99%

Reliable kidney size determination by magnetic resonance imaging in pathophysiological settings

et al. 2021

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Aim Kidney diseases constitute a major health challenge, which requires noninvasive imaging to complement conventional approaches to diagnosis and monitoring. Several renal pathologies are associated with changes in kidney size, offering an opportunity for magnetic resonance imaging (MRI) biomarkers of disease. This work uses dynamic MRI and an automated bean‐shaped model (ABSM) for longitudinal quantification of pathophysiologically relevant changes in kidney size. Methods A geometry‐based ABSM was developed for kidney size measurements in rats using parametric MRI (T2, T2* mapping). The ABSM approach was applied to longitudinal renal size quantification using occlusion of the (a) suprarenal aorta or (b) the renal vein, (c) increase in renal pelvis and intratubular pressure and (d) injection of an X‐ray contrast medium into the thoracic aorta to induce pathophysiologically relevant changes in kidney size. Results The ABSM yielded renal size measurements with accuracy and precision equivalent to the manual segmentation, with >70‐fold time savings. The automated method could detect a ~7% reduction (aortic occlusion) and a ~5%, a ~2% and a ~6% increase in kidney size (venous occlusion, pelvis and intratubular pressure increase and injection of X‐ray contrast medium, respectively). These measurements were not affected by reduced image quality following administration of ferumoxytol. Conclusion Dynamic MRI in conjunction with renal segmentation using an ABSM supports longitudinal quantification of changes in kidney size in pathophysiologically relevant experimental setups mimicking realistic clinical scenarios. This can potentially be instrumental for developing MRI‐based diagnostic tools for various kidney disorders and for gaining new insight into mechanisms of renal pathophysiology.

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“…Manual kidney segmentation, such as region-of-interest (ROI) border tracing [ 20 ] or stereology [ 21 ] by experienced and qualified professionals, is considered the gold standard. However, because of the comparable signal intensities across the kidneys’ surrounding organs, and imaging artifacts, these manual methods are time-demanding (lasting 15–30 min) and might be skewed by investigator judgment [ 22 ]. An automated deep learning system that provides automatic segmentation would be a valuable supporting tool for clinicians [ 23 , 24 ].…”

Section: Related Workmentioning

confidence: 99%

“…The proposed work focused on validating a Mask R-CNN for the automated segmentation of kidneys. In one study, a 2D convolutional neural network was applied to an MRI modality to segment left and right kidneys in healthy and chronic kidney disease subjects [ 22 ]. Timothy et al developed a convolutional neural network to apply an automated deep learning approach for cyst segmentation in MRI images [ 31 ].…”

Section: Related Workmentioning

confidence: 99%

Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images

Xiao

Liu

et al. 2022

Diagnostics

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Recent breakthroughs of deep learning algorithms in medical imaging, automated detection, and segmentation techniques for renal (kidney) in abdominal computed tomography (CT) images have been limited. Radiomics and machine learning analyses of renal diseases rely on the automatic segmentation of kidneys in CT images. Inspired by this, our primary aim is to utilize deep semantic segmentation learning models with a proposed training scheme to achieve precise and accurate segmentation outcomes. Moreover, this work aims to provide the community with an open-source, unenhanced abdominal CT dataset for training and testing the deep learning segmentation networks to segment kidneys and detect kidney stones. Five variations of deep segmentation networks are trained and tested both dependently (based on the proposed training scheme) and independently. Upon comparison, the models trained with the proposed training scheme enable the highly accurate 2D and 3D segmentation of kidneys and kidney stones. We believe this work is a fundamental step toward AI-driven diagnostic strategies, which can be an essential component of personalized patient care and improved decision-making in treating kidney diseases.

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Automated renal segmentation in healthy and chronic kidney disease subjects using a convolutional neural network

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 49 publications

References 35 publications

A pipeline for automated voxel dosimetry: application in patients with multi-SPECT/CT imaging following ¹⁷⁷Lu peptide receptor radionuclide therapy

A pipeline for automated voxel dosimetry: application in patients with multi-SPECT/CT imaging following ¹⁷⁷Lu peptide receptor radionuclide therapy

Reliable kidney size determination by magnetic resonance imaging in pathophysiological settings

Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images

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Automated renal segmentation in healthy and chronic kidney disease subjects using a convolutional neural network

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 49 publications

References 35 publications

A pipeline for automated voxel dosimetry: application in patients with multi-SPECT/CT imaging following 177Lu peptide receptor radionuclide therapy

A pipeline for automated voxel dosimetry: application in patients with multi-SPECT/CT imaging following 177Lu peptide receptor radionuclide therapy

Reliable kidney size determination by magnetic resonance imaging in pathophysiological settings

Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images

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Product

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A pipeline for automated voxel dosimetry: application in patients with multi-SPECT/CT imaging following ¹⁷⁷Lu peptide receptor radionuclide therapy

A pipeline for automated voxel dosimetry: application in patients with multi-SPECT/CT imaging following ¹⁷⁷Lu peptide receptor radionuclide therapy