Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (9): 16-24.doi: 10.11896/jsjkx.250300159

• Intelligent Medical Engineering • Previous Articles     Next Articles

Deep Learning-based Kidney Segmentation in Ultrasound Imaging:Current Trends and Challenges

YIN Shi1, SHI Zhenyang1, WU Menglin1,2, CAI Jinyan1, YU De3   

  1. 1 College of Computer and Information Engineering,Nanjing Tech University,Nanjing 211816,China
    2 Carbon Medical Device Ltd.,Shenzhen,Guangdong 518000,China
    3 School of Information Technology,Jiangsu Open University,Nanjing 210036,China
  • Received:2025-03-31 Revised:2025-06-19 Online:2025-09-15 Published:2025-09-11
  • About author:YIN Shi,born in 1991,associate researcher,is a member of CCF(No.50991G).Her main research interests include medical image analysis and deep learning.
    YU De,born in 1987,lecturer.His main research interests include signal processing and medical image analysis.
  • Supported by:
    Natural Science Foundation of Jiangsu Province(BK20230312).

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Abstract: Kidney ultrasound segmentation plays a pivotal role in clinical diagnosis and treatment planning.This review systema-tically reviews key developments in renal segmentation techniques from 2017 to 2024,focusing on 2D/3D approaches and pathological tissue analysis.Current 2D methods encompass four categories:traditional texture-based techniques,U-Net variants,shape-prior integrated deep learning,and multimodal fusion approaches.The study comprehensively evaluates available datasets and standardized metrics,establishing critical benchmarks for the field.While significant progress has been made in 2D segmentation,persistent challenges include limited precision in fine structures,immature 3D techniques,inadequate pathological analysis,and data scarcity.Overcoming these limitations is crucial for clinical translation.Future directions emphasize refining structural segmentation,advancing 3D reconstruction,developing cross-modal learning,and creating comprehensive datasets.These efforts will enhance the clinical utility of renal ultrasound segmentation,bridging the gap between technical innovation and medical application.

Key words: Kidney US segmentation, Deep learning, Renal abnormalities, Evaluation metrics, Open datasets

CLC Number: 

  • TP391.41
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