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Segmentation of ADPKD Computed Tomography Images with Deep Learning Approach for Predicting Total Kidney Volume.

Ting-Wen Sheng1, Djeane Debora Onthoni2, Pushpanjali Gupta2

  • 1Department of Medical Imaging and Intervention, New Taipei Municipal TuCheng Hospital, Chang Gung Medical Foundation, New Taipei City 236017, Taiwan.

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|February 26, 2025
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Summary

Artificial Intelligence (AI) models accurately estimate Total Kidney Volume (TKV) in Autosomal Dominant Polycystic Kidney Disease (ADPKD) patients using both Non-enhanced Computed Tomography (NCCT) and Contrast-enhanced Computed Tomography (CCT) scans.

Keywords:
contrast computed tomographydeep learninglocalizationnon-contrast computed tomographypolycystic kidney diseasesegmentationtotal kidney volume

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Nephrology

Background:

  • Total Kidney Volume (TKV) is crucial for predicting renal function decline in Autosomal Dominant Polycystic Kidney Disease (ADPKD).
  • Current manual TKV calculation from CT scans is time-consuming, labor-intensive, and susceptible to human error.
  • A significant gap exists in studies addressing CT modality variations for TKV estimation.

Purpose of the Study:

  • To develop and validate an AI-enabled framework for robust TKV estimation in ADPKD patients.
  • To ensure consistent performance across both Non-enhanced Computed Tomography (NCCT) and Contrast-enhanced Computed Tomography (CCT) modalities.
  • To overcome the limitations of manual kidney localization and segmentation.

Main Methods:

  • Proposed a step-by-step AI framework for kidney localization and segmentation.
  • Integrated image preprocessing techniques (dilation, global thresholding) with Deep Learning (DL) models (SSD, Inception V2, DeepLab V3+).
  • Ensured balanced sample utilization and robust performance across NCCT and CCT images.

Main Results:

  • AI models achieved 95% mean Average Precision (mAP) for localization.
  • Segmentation accuracy reached a 92% mean Intersection over Union (mIoU).
  • TKV estimation yielded a high R2 score of 97%.

Conclusions:

  • The developed AI models robustly localize and segment ADPKD kidneys.
  • AI-based TKV estimation is effective using both NCCT and CCT images.
  • This AI framework offers a reliable and efficient alternative to manual methods for ADPKD management.