ArticleJournal of medical imaging (Bellingham, Wash.)2023
Automatic measurement of kidney dimensions in two-dimensional ultrasonography is comparable to expert sonographers.
Article in Journal of medical imaging (Bellingham, Wash.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Deep learning in renal ultrasound: applications, challenges, and future outlook.Frontiers in oncology · 2025Review
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8 authors.
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Abstract
Purpose: Length and width measurements of the kidneys aid in the detection and monitoring of structural abnormalities and organ disease. Manual measurement results in intra- and inter-rater variability, is complex and time-consuming, and is fraught with error. We propose an automated approach based on machine learning for quantifying kidney dimensions from two-dimensional (2D) ultrasound images in both native and transplanted kidneys. Approach: An nnU-net machine learning model was trained on 514 images to segment the kidney capsule in standard longitudinal and transverse views. Two expert sonographers and three medical students manually measured the maximal kidney length and width in 132 ultrasound cines. The segmentation algorithm was then applied to the same cines, region fitting was performed, and the maximum kidney length and width were measured. Additionally, single kidney volume for 16 patients was estimated using either manual or automatic measurements. Results: The experts resulted in length of Conclusions: This pilot study demonstrates the feasibility of an automatic tool to measure
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