Grading Prenatal Hydronephrosis from Ultrasound Imaging Using Deep Convolutional Neural Networks

Dhindsa, Kiret; Smail, Lauren Christine; McGrath, M. A.; Braga, Luis H.; Becker, Suzanna; Sonnadara, Ranil

doi:10.1109/crv.2018.00021

Cited by 13 publications

(7 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering the issue of subjectivity, our model's current level of performance in classifying HN is promising and in line with previous research from our group (14). Our findings suggest that applying these algorithms into clinical practice through decision aids and teaching aids has potential.…”

Section: Implications For Clinical Practicesupporting

confidence: 86%

“…Owing to the ability of deep learning algorithms to classify images into diagnostic categories based solely on data-driven pattern recognition, the main purpose of this study was to extend on our previous work (14) to investigate whether deep learning algorithms can effectively grade the severity of HN using a prospectively collected HN database and separate them into 5 main classes. Secondary investigations were also conducted to assess whether the same model can effectively discriminate between low and high HN grades (SFU 0, I, II vs. III, IV), and between moderate (SFU II vs. III) cases.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Deep Learning Algorithms to Grade Hydronephrosis Severity: Toward a Clinical Adjunct

et al. 2020

Self Cite

View full text Add to dashboard Cite

Grading hydronephrosis severity relies on subjective interpretation of renal ultrasound images. Deep learning is a data-driven algorithmic approach to classifying data, including images, presenting a promising option for grading hydronephrosis. The current study explored the potential of deep convolutional neural networks (CNN), a type of deep learning algorithm, to grade hydronephrosis ultrasound images according to the 5-point Society for Fetal Urology (SFU) classification system, and discusses its potential applications in developing decision and teaching aids for clinical practice. We developed a five-layer CNN to grade 2,420 sagittal hydronephrosis ultrasound images [191 SFU 0 (8%), 407 SFU I (17%), 666 SFU II (28%), 833 SFU III (34%), and 323 SFU IV (13%)], from 673 patients ranging from 0 to 116.29 months old (M age = 16.53, SD = 17.80). Five-way (all grades) and two-way classification problems [i.e., II vs. III, and low (0-II) vs. high (III-IV)] were explored. The CNN classified 94% (95% CI, 93-95%) of the images correctly or within one grade of the provided label in the five-way classification problem. Fifty-one percent of these images (95% CI, 49-53%) were correctly predicted, with an average weighted F1 score of 0.49 (95% CI, 0.47-0.51). The CNN achieved an average accuracy of 78% (95% CI, 75-82%) with an average weighted F1 of 0.78 (95% CI, 0.74-0.82) when classifying low vs. high grades, and an average accuracy of 71% (95% CI, 68-74%) with an average weighted F1 score of 0.71 (95% CI, 0.68-0.75) when discriminating between grades II vs. III. Our model performs well above chance level, and classifies almost all images either correctly or within one grade of the provided label. We have demonstrated the applicability of a CNN approach to hydronephrosis ultrasound image classification. Further investigation into a deep learning-based clinical adjunct for hydronephrosis is warranted.

show abstract

Section: Implications For Clinical Practicesupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Using Deep Learning Algorithms to Grade Hydronephrosis Severity: Toward a Clinical Adjunct

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…As neither of these are the desired ROI, both are misleading with respect to segmenting the kidney. Further details concerning this dataset can be found in [44,45,46].…”

Section: Dataset 1: Renal Ultrasound Imagesmentioning

confidence: 99%

“…We follow a similar methodology used for preprocessing renal ultrasound images described in [44]. We crop the images to remove white borders, despeckle them to remove speckle noise caused by interference with the ultrasound probe during imaging [49], and re-scale to 256×256 pixels for consistency.…”

Section: Image Preprocessingmentioning

confidence: 99%

TricycleGAN: Unsupervised Image Synthesis and Segmentation Based on Shape Priors

Sivanesan,

Braga,

Sonnadara

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Medical image segmentation is routinely performed to isolate regions of interest, such as organs and lesions. Currently, deep learning is the state of the art for automatic segmentation, but is usually limited by the need for supervised training with large datasets that have been manually segmented by trained clinicians. The goal of semi-superised and unsupervised image segmentation is to greatly reduce, or even eliminate, the need for training data and therefore to minimze the burden on clinicians when training segmentation models. To this end we introduce a novel network architecture for capable of unsupervised and semi-supervised image segmentation called TricycleGAN. This approach uses three generative models to learn translations between medical images and segmentation maps using edge maps as an intermediate step. Distinct from other approaches based on generative networks, TricycleGAN relies on shape priors rather than colour and texture priors. As such, it is particularly well-suited for several domains of medical imaging, such as ultrasound imaging, where commonly used visual cues may be absent. We present experiments with TricycleGAN on a clinical dataset of kidney ultrasound images and the benchmark ISIC 2018 skin lesion dataset.

show abstract

“…We use a dataset of renal ultrasound images developed for prenatal hydronephrosis, a congenital kidney disorder marked by excessive and potentially dangerous fluid retention in the kidneys [14]. The dataset consists of 2492 2D sagittal kidney ultrasound images from 773 patients across multiple hospital visits.…”

Section: Dataset 1: Renal Ultrasound Imagesmentioning

confidence: 99%

Unsupervised Medical Image Segmentation with Adversarial Networks: From Edge Diagrams to Segmentation Maps

Sivanesan,

Braga,

Sonnadara

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

We develop and approach to unsupervised semantic medical image segmentation that extends previous work with generative adversarial networks. We use existing edge detection methods to construct simple edge diagrams, train a generative model to convert them into synthetic medical images, and construct a dataset of synthetic images with known segmentations using variations on extracted edge diagrams. This synthetic dataset is then used to train a supervised image segmentation model. We test our approach on a clinical dataset of kidney ultrasound images and the benchmark ISIC 2018 skin lesion dataset. We show that our unsupervised approach is more accurate than previous unsupervised methods, and performs reasonably compared to supervised image segmentation models. All code and trained models are available at https://github.com/kiretd/Unsupervised-MIseg.

show abstract

Grading Prenatal Hydronephrosis from Ultrasound Imaging Using Deep Convolutional Neural Networks

Cited by 13 publications

References 37 publications

Using Deep Learning Algorithms to Grade Hydronephrosis Severity: Toward a Clinical Adjunct

Using Deep Learning Algorithms to Grade Hydronephrosis Severity: Toward a Clinical Adjunct

TricycleGAN: Unsupervised Image Synthesis and Segmentation Based on Shape Priors

Unsupervised Medical Image Segmentation with Adversarial Networks: From Edge Diagrams to Segmentation Maps

Contact Info

Product

Resources

About