Segmentation of ADPKD Computed Tomography Images with Deep Learning Approach for Predicting Total Kidney Volume Article Swipe
Ting‐Wen Sheng
,
Djeane Debora Onthoni
,
Pushpanjali Gupta
,
Tsong‐Hai Lee
,
Prasan Kumar Sahoo
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/biomedicines13020263
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/biomedicines13020263
Background: Total Kidney Volume (TKV) is widely used globally to predict the progressive loss of renal function in patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD). Typically, TKV is calculated using Computed Tomography (CT) images by manually locating, delineating, and segmenting the ADPKD kidneys. However, manual localization and segmentation are tedious, time-consuming tasks and are prone to human error. Specifically, there is a lack of studies that focus on CT modality variation. Methods: In contrast, our work develops a step-by-step framework, which robustly handles both Non-enhanced Computed Tomography (NCCT) and Contrast-enhanced Computed Tomography (CCT) images, ensuring balanced sample utilization and consistent performance across modalities. To achieve this, Artificial Intelligence (AI)-enabled localization and segmentation models are proposed for estimating TKV, which is designed to work robustly on both NCCT and Contrast-Computed Tomography (CCT) images. These AI-based models incorporate various image preprocessing techniques, including dilation and global thresholding, combined with Deep Learning (DL) approaches such as the adapted Single Shot Detector (SSD), Inception V2, and DeepLab V3+. Results: The experimental results demonstrate that the proposed AI-based models outperform other DL architectures, achieving a mean Average Precision (mAP) of 95% for automatic localization, a mean Intersection over Union (mIoU) of 92% for segmentation, and a mean R2 score of 97% for TKV estimation. Conclusions: These results clearly indicate that the proposed AI-based models can robustly localize and segment ADPKD kidneys and estimate TKV using both NCCT and CCT images.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/biomedicines13020263
- https://www.mdpi.com/2227-9059/13/2/263/pdf?version=1737528132
- OA Status
- gold
- Cited By
- 1
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406706840
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4406706840Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/biomedicines13020263Digital Object Identifier
- Title
-
Segmentation of ADPKD Computed Tomography Images with Deep Learning Approach for Predicting Total Kidney VolumeWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-22Full publication date if available
- Authors
-
Ting‐Wen Sheng, Djeane Debora Onthoni, Pushpanjali Gupta, Tsong‐Hai Lee, Prasan Kumar SahooList of authors in order
- Landing page
-
https://doi.org/10.3390/biomedicines13020263Publisher landing page
- PDF URL
-
https://www.mdpi.com/2227-9059/13/2/263/pdf?version=1737528132Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2227-9059/13/2/263/pdf?version=1737528132Direct OA link when available
- Concepts
-
Artificial intelligence, Segmentation, Thresholding, Computer science, Image segmentation, Computer vision, Pattern recognition (psychology), Autosomal dominant polycystic kidney disease, Preprocessor, Medicine, Image (mathematics), Radiology, CystTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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