ArticleProceedings. IEEE International Symposium on Biomedical Imaging2019
FULLY-AUTOMATIC SEGMENTATION OF KIDNEYS IN CLINICAL ULTRASOUND IMAGES USING A BOUNDARY DISTANCE REGRESSION NETWORK.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- A Segmentation Network with Two Distinct Attention Modules for the Segmentation of Multiple Renal Structures in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2025Article
- AI-assisted anatomical structure recognition and segmentation via mamba-transformer architecture in abdominal ultrasound images.Frontiers in artificial intelligence · 2025Article
- Advanced ultrasound methods to improve chronic kidney disease diagnosis.Npj imaging · 2024Review
- AI-based segmentation of renal enhanced CT images for quantitative evaluate of chronic kidney disease.Scientific reports · 2024Article
- Fully automated kidney image biomarker prediction in ultrasound scans using Fast-Unet+.Scientific reports · 2024Article
- Objective assessment of segmentation models for thyroid ultrasound images.Journal of ultrasound · 2023Article
- Artificial intelligence-aided ultrasound in renal diseases: a systematic review.Quantitative imaging in medicine and surgery · 2023Review
- Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review.Computational and structural biotechnology journal · 2023Review
- Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images.Diagnostics (Basel, Switzerland) · 2022Article
- Current status of deep learning applications in abdominal ultrasonography.Ultrasonography (Seoul, Korea) · 2021Article
- Article
- Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks.Medical image analysis · 2020Article
- Multi-instance Deep Learning with Graph Convolutional Neural Networks for Diagnosis of Kidney Diseases Using Ultrasound Imaging.Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures : first International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Sh... · 2019Article
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8 authors.
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Abstract
It remains challenging to automatically segment kidneys in clinical ultrasound images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance. In this study, we developed a novel boundary distance regression deep neural network to segment the kidneys, informed by the fact that the kidney boundaries are relatively consistent across images in terms of their appearance. Particularly, we first use deep neural networks pre-trained for classification of natural images to extract high-level image features from ultrasound images, then these feature maps 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. 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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