ArticleAcademic radiology2018
A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images.
Article in Academic radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 43 citations in OpenAlex.
- CPSN: Caputo Principal-Curve-Guided Segmentation Network on Ultrasound Kidney Databases.Journal of imaging informatics in medicine · 2026Article
- Quantifying ultrasound echogenicity difference for accurate chronic kidney disease diagnosis.Journal of nephrology · 2025Article
- Fully automated kidney image biomarker prediction in ultrasound scans using Fast-Unet+.Scientific reports · 2024Article
- Novel Solution for Using Neural Networks for Kidney Boundary Extraction in 2D Ultrasound Data.Biomolecules · 2023Article
- Classifying Kidney Disease in a Vervet Model Using Spatially Encoded Contrast-Enhanced Ultrasound Perfusion Parameters.Ultrasound in medicine & biology · 2023Article
- Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review.Computational and structural biotechnology journal · 2023Review
- Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements.Abdominal radiology (New York) · 2022Article
- Advanced Kidney Volume Measurement Method Using Ultrasonography with Artificial Intelligence-Based Hybrid Learning in Children.Sensors (Basel, Switzerland) · 2021Article
- Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks.Medical image analysis · 2020Article
- FULLY-AUTOMATIC SEGMENTATION OF KIDNEYS IN CLINICAL ULTRASOUND IMAGES USING A BOUNDARY DISTANCE REGRESSION NETWORK.Proceedings. IEEE International Symposium on Biomedical Imaging · 2019Article
- Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features.Journal of pediatric urology · 2019Article
- TRANSFER LEARNING FOR DIAGNOSIS OF CONGENITAL ABNORMALITIES OF THE KIDNEY AND URINARY TRACT IN CHILDREN BASED ON ULTRASOUND IMAGING DATA.Proceedings. IEEE International Symposium on Biomedical Imaging · 2018Article
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4 authors at 3 institutions in 2 countries.
Funding
Abstract
RATIONALE AND
objectivesAutomatic segmentation of kidneys in ultrasound (US) images remains a challenging task because of high speckle noise, low contrast, and large appearance variations of kidneys in US images. Because texture features may improve the US image segmentation performance, we propose a novel graph cuts method to segment kidney in US images by integrating image intensity information and texture feature maps. MATERIALS AND
methodsWe develop a new graph cuts-based method to segment kidney US images by integrating original image intensity information and texture feature maps extracted using Gabor filters. To handle large appearance variation within kidney images and improve computational efficiency, we build a graph of image pixels close to kidney boundary instead of building a graph of the whole image. To make the kidney segmentation robust to weak boundaries, we adopt localized regional information to measure similarity between image pixels for computing edge weights to build the graph of image pixels. The localized graph is dynamically updated and the graph cuts-based segmentation iteratively progresses until convergence. Our method has been evaluated based on kidney US images of 85 subjects. The imaging data of 20 randomly selected subjects were used as training data to tune parameters of the image segmentation method, and the remaining data were used as testing data for validation.
resultsExperiment results demonstrated that the proposed method obtained promising segmentation results for bilateral kidneys (average Dice index = 0.9446, average mean distance = 2.2551, average specificity = 0.9971, average accuracy = 0.9919), better than other methods under comparison (P < .05, paired Wilcoxon rank sum tests).
conclusionsThe proposed method achieved promising performance for segmenting kidneys in two-dimensional US images, better than segmentation methods built on any single channel of image information. This method will facilitate extraction of kidney characteristics that may predict important clinical outcomes such as progression of chronic kidney disease.
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