ArticleMedical image analysis2020
Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks.
Article in Medical image analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 115 citations in OpenAlex.
- CPSN: Caputo Principal-Curve-Guided Segmentation Network on Ultrasound Kidney Databases.Journal of imaging informatics in medicine · 2026Article
- Ultrasound Domain Adaptation for Robust Kidney Segmentation via Spectral-Similarity-Guided Translation.Journal of imaging informatics in medicine · 2026Article
- A Neural Network for Segmenting Tumours in Ultrasound Rectal Images.Journal of imaging informatics in medicine · 2025Article
- TRUSTED: The Paired 3D Transabdominal Ultrasound and CT Human Data for Kidney Segmentation and Registration Research.Scientific data · 2025Article
- AI-assisted anatomical structure recognition and segmentation via mamba-transformer architecture in abdominal ultrasound images.Frontiers in artificial intelligence · 2025Article
- URI-CADS: A Fully Automated Computer-Aided Diagnosis System for Ultrasound Renal Imaging.Journal of imaging informatics in medicine · 2024Article
- Integrated multi-omics with machine learning to uncover the intricacies of kidney disease.Briefings in bioinformatics · 2024Review
- Clinical, Safety, and Engineering Perspectives on Wearable Ultrasound Technology: A Review.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2024Review
- Transformative Deep Neural Network Approaches in Kidney Ultrasound Segmentation: Empirical Validation with an Annotated Dataset.Interdisciplinary sciences, computational life sciences · 2024Article
- Fully automated kidney image biomarker prediction in ultrasound scans using Fast-Unet+.Scientific reports · 2024Article
- Multi-Scale and Spatial Information Extraction for Kidney Tumor Segmentation: A Contextual Deformable Attention and Edge-Enhanced U-Net.Journal of imaging informatics in medicine · 2024Article
- Novel Solution for Using Neural Networks for Kidney Boundary Extraction in 2D Ultrasound Data.Biomolecules · 2023Article
- Objective assessment of segmentation models for thyroid ultrasound images.Journal of ultrasound · 2023Article
- Discrimination between leucine-rich glioma-inactivated 1 antibody encephalitis and gamma-aminobutyric acid B receptor antibody encephalitis based on ResNet18.Visual computing for industry, biomedicine, and art · 2023Article
- Artificial intelligence-aided ultrasound in renal diseases: a systematic review.Quantitative imaging in medicine and surgery · 2023Review
- Automatic measurement of kidney dimensions in two-dimensional ultrasonography is comparable to expert sonographers.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Classifying Kidney Disease in a Vervet Model Using Spatially Encoded Contrast-Enhanced Ultrasound Perfusion Parameters.Ultrasound in medicine & biology · 2023Article
- A narrative review on the application of artificial intelligence in renal ultrasound.Frontiers in oncology · 2023Review
- Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images.Diagnostics (Basel, Switzerland) · 2022Article
- 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
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
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 automatically. Particularly, we first use deep neural networks pre-trained for classification of natural images to extract high-level image features from US images. These features are used as input to learn kidney boundary distance maps using a boundary distance regression network and the predicted boundary distance maps are classified as kidney pixels or non-kidney pixels using a pixelwise classification network in an end-to-end learning fashion. We also adopted a data-augmentation 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 automatically segment the kidney with promising performance, significantly better than deep learning-based pixel classification networks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.