Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data

Zheng, Qian; Tastan, Gregory; Fan, Yong

doi:10.1109/isbi.2018.8363854

Cited by 27 publications

(9 citation statements)

References 18 publications

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“…In particular, anatomic characteristics derived from US imaging, such as renal elasticity, are associated with kidney function (Meola et al, 2016) and lower renal parenchymal area measured on US images is associated with increased risk of end-stage renal disease (ESRD) in boys with posterior urethral valves (Pulido et al, 2014). Imaging features computed from US data using deep convolutional neural networks (CNNs) improved the classification of children with congenital abnormalities of the kidney and urinary tract (CAKUT) and controls (Zheng et al, 2019;Zheng et al, 2018a). The computation of these anatomic measures typically involves manual or semi-automatic segmentation of kidneys in US images, which increases inter-operator variability, reduces reliability, and limits utility in clinical medicine.…”

Section: Introductionmentioning

confidence: 99%

Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks

Yin

Peng

et al. 2020

Medical Image Analysis

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It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance. In this study, we propose subsequent boundary distance regression and pixel classification networks to segment the kidneys, informed by the fact that the kidney boundaries have relatively homogenous texture patterns across images. Particularly, we first use deep neural networks pretrained for classification of natural images to extract high-level image features from US images, then these features are used as input to learn kidney boundary distance maps using a boundary distance regression network, and finally the predicted boundary distance maps are classified as kidney pixels or non-kidney pixels using a pixel classification network in an end-to-end learning fashion. We also adopted a dataaugmentation method based on kidney shape registration to generate enriched training data from a small number of US images with manually segmented kidney labels. Experimental results have demonstrated that our method could effectively improve the performance of automatic kidney segmentation, significantly better than deep learning-based pixel classification networks.

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Section: Introductionmentioning

confidence: 99%

Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks

Yin

Peng

et al. 2020

Medical Image Analysis

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“…Finally, we note that Deep Learning has also been applied in non-reconstruction tasks to ultrasound, including classification [60], [61] and segmentation [62].…”

Section: Deep Learningmentioning

confidence: 99%

A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound

Feigin

Freedman

Anthony³

2020

IEEE Trans. Biomed. Eng.

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Objective: Ultrasound elastography is gaining traction as an accessible and useful diagnostic tool for such things as cancer detection and differentiation and thyroid disease diagnostics. Unfortunately, state of the art shear wave imaging techniques, essential to promote this goal, are limited to highend ultrasound hardware due to high power requirements; are extremely sensitive to patient and sonographer motion, and generally, suffer from low frame rates.Motivated by research and theory showing that longitudinal wave sound speed carries similar diagnostic abilities to shear wave imaging, we present an alternative approach using single sided pressure-wave sound speed measurements from channel data.Methods: In this paper, we present a single-sided sound speed inversion solution using a fully convolutional deep neural network. We use simulations for training, allowing the generation of limitless ground truth data.Results: We show that it is possible to invert for longitudinal sound speed in soft tissue at high frame rates. We validate the method on simulated data. We present highly encouraging results on limited real data.Conclusion: Sound speed inversion on channel data has significant potential, made possible in real time with deep learning technologies.Significance: Specialized shear wave ultrasound systems remain inaccessible in many locations. longitudinal sound speed and deep learning technologies enable an alternative approach to diagnosis based on tissue elasticity. High frame rates are possible.Index Terms-deep learning, inverse problems, ultrasound, sound speed inversion

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“…The AUC for classifiers built on the combination features were 0.92, 0.88, and 0.92 for discriminating the left, right, and bilateral abnormal kidney scans from controls with classification rates of 84%, 81%, and 87%; specificity of 84%, 74%, and 88%; and sensitivity of 85%, 88%, and 86%, respectively. It is suggested that the combination of transfer learning features and conventional imaging features yielded the best classification performance for distinguishing CAKUT patients from normal controls based on their ultrasound kidney images [49,50].…”

Section: Imaging Diagnosismentioning

confidence: 99%

Role of Artificial Intelligence in Kidney Disease

Yuan¹,

Zhang²,

Deng³

et al. 2020

Int. J. Med. Sci.

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Artificial intelligence (AI), as an advanced science technology, has been widely used in medical fields to promote medical development, mainly applied to early detections, disease diagnoses, and management. Owing to the huge number of patients, kidney disease remains a global health problem. Challenges remain in its diagnosis and treatment. AI could take individual conditions into account, produce suitable decisions and promise to make great strides in kidney disease management. Here, we review the current studies of AI applications in kidney disease in alerting systems, diagnostic assistance, guiding treatment and evaluating prognosis. Although the number of studies related to AI applications in kidney disease is small, the potential of AI in the management of kidney disease is well recognized by clinicians; AI will greatly enhance clinicians' capacity in their clinical practice in the future.

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Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data

Cited by 27 publications

References 18 publications

Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks

Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks

A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound

Role of Artificial Intelligence in Kidney Disease

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