Differentiation of Urinary Stone and Vascular Calcifications on Non-contrast CT Images: An Initial Experience using Computer Aided Diagnosis

Lee, Hak Jong; Kim, Kwang Gi; Hwang, Sung Il; Kim, Seung Hyup; Byun, Soo-Hwan; Lee, Sang Eun; Hong, Seong Kyu; Cho, Jeong Yeon; Seong, Chang Gyu

doi:10.1007/s10278-009-9181-0

Cited by 11 publications

(7 citation statements)

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“…In 2010, Lee et al [ 11 ] created an artificial neural network (ANN) that used combinations of various shape and internal texture parameters of 112 calcifications to classify them into ureteral stones or vascular calcifications. They reached an AUC of 0.85 for the shape parameters and 0.88 for texture parameters.…”

Section: Discussionmentioning

confidence: 99%

“…Differentiation using these methods is based on automatically or semi-automatically derived local image features of the calcifications. Recently, a semi-quantitative method was applied using cut-off values for the volume and attenuation of the calcification to discriminate stones from phleboliths [ 10 ]; while another method used image features that were fed into an artificial network [ 11 ]. Irrespective of the CAD method used, a key question for their development is whether the images of the calcification and its local surroundings can provide sufficient information for differentiation, or whether distant information, such as from visible upper ureters, is also needed.…”

Section: Introductionmentioning

confidence: 99%

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Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network

2020

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The objectives were to develop and validate a Convolutional Neural Network (CNN) using local features for differentiating distal ureteral stones from pelvic phleboliths, compare the CNN method with a semi-quantitative method and with radiologists' assessments and to evaluate whether the assessment of a calcification and its local surroundings is sufficient for discriminating ureteral stones from pelvic phleboliths in non-contrast-enhanced CT (NECT). We retrospectively included 341 consecutive patients with acute renal colic and a ureteral stone on NECT showing either a distal ureteral stone, a phlebolith or both. A 2.5-dimensional CNN (2.5D-CNN) model was used, where perpendicular axial, coronal and sagittal images through each calcification were used as input data for the CNN. The CNN was trained on 384 calcifications, and evaluated on an unseen dataset of 50 stones and 50 phleboliths. The CNN was compared to the assessment by seven radiologists who reviewed a local 5 × 5 × 5 cm image stack surrounding each calcification, and to a semi-quantitative method using cutoff values based on the attenuation and volume of the calcifications. The CNN differentiated stones and phleboliths with a sensitivity, specificity and accuracy of 94%, 90% and 92% and an AUC of 0.95. This was similar to a majority vote accuracy of 93% and significantly higher (p = 0.03) than the mean radiologist accuracy of 86%. The semi-quantitative method accuracy was 49%. In conclusion, the CNN differentiated ureteral stones from phleboliths with higher accuracy than the mean of seven radiologists' assessments using local features. However, more than local features are needed to reach optimal discrimination. Keywords Computed tomography • Ureteral calculi • Pelvic phlebolith • Deep learning • Convolutional neural networks Abbreviations NECT Non-contrast-enhanced computed tomography CTU CT urography CNN Convolutional neural network 2.5D-CNN 2.5-Dimensional CNN including axial, coronal and sagittal images CAD Computer-aided diagnosis ANN Artificial neural network AI Artificial intelligence FC layer Fully connected layer PACS Picture archiving and communication system MPR Multiplanar reconstructions CI Confidence interval ROC Receiver operating characteristic AUC Area under the curve ROI Region of interest Key points A Convolutional Neural Network for classifying pelvic calcifications was developed and validated.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network

2020

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“…A similar approach could be found in Shah's work 24 except that comprehensive gray level statistical texture features were constructed for classification. Lee et al 25 developed a computer-aided diagnosis system to differentiate urinary stone and vascular calcifications on precontrast CT images. In their method, they semiautomatically chose calculi candidates using a region-growing method followed by computation of statistical and shape features.…”

Section: Introductionmentioning

confidence: 99%

Computer‐aided detection of renal calculi from noncontrast CT images using TV‐flow and MSER features

Liu¹,

Wang²,

Türkbey³

et al. 2015

Medical Physics

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“…Relatively few works have been published that tackle the challenge of computer‐aided detection of kidney stones in CT. Lee et al. used texture‐ and intensity‐based features to train an artificial neural network to distinguish kidney stones from vascular calcifications 23 . Liu et al.…”

Section: Introductionmentioning

confidence: 99%

“…22 Relatively few works have been published that tackle the challenge of computer-aided detection of kidney stones in CT. Lee et al used texture-and intensity-based features to train an artificial neural network to distinguish kidney stones from vascular calcifications. 23 Liu et al segmented the kidneys and then used total-variation flow denoising followed by the maximal stable extremal regions method to segment stones. 24 Features from the segmented stones were fed into an support vector machine (SVM) classifier to classify kidney stones versus false positives, achieving a sensitivity of 60.0% at an average of two false positives per scan.…”

Section: Introductionmentioning

confidence: 99%

A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans

et al. 2022

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Purpose Early detection and size quantification of renal calculi are important for optimizing treatment and preventing severe kidney stone disease. Prior work has shown that volumetric measurements of kidney stones are more informative and reproducible than linear measurements. Deep learning‐based systems that use abdominal noncontrast computed tomography (CT) scans may assist in detection and reduce workload by removing the need for manual stone volume measurement. Prior to this work, no such system had been developed for use on noisy low‐dose CT or tested on a large‐scale external dataset. Methods We used a dataset of 91 CT colonography (CTC) scans with manually marked kidney stones combined with 89 CTC scans without kidney stones. To compare with a prior work half the data was used for training and half for testing. A set of CTC scans from 6185 patients from a separate institution with patient‐level labels were used as an external validation set. A 3D U‐Net model was employed to segment the kidneys, followed by gradient‐based anisotropic denoising, thresholding, and region growing. A 13 layer convolutional neural network classifier was then applied to distinguish kidney stones from false positive regions. Results The system achieved a sensitivity of 0.86 at 0.5 false positives per scan on a challenging test set of low‐dose CT with many small stones, an improvement over an earlier work that obtained a sensitivity of 0.52. The stone volume measurements correlated well with manual measurements (r2=0.95$r^2 = 0.95$). For patient‐level classification, the system achieved an area under the receiver‐operating characteristic of 0.95 on an external validation set (sensitivity = 0.88, specificity = 0.91 at the Youden point). A common cause of false positives were small atherosclerotic plaques in the renal sinus that simulated kidney stones. Conclusions Our deep‐learning‐based system showed improvements over a previously developed system that did not use deep learning, with even higher performance on an external validation set.

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Differentiation of Urinary Stone and Vascular Calcifications on Non-contrast CT Images: An Initial Experience using Computer Aided Diagnosis

Cited by 11 publications

References 29 publications

Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network

Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network

Computer‐aided detection of renal calculi from noncontrast CT images using TV‐flow and MSER features

A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans

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