Objectives Current development of kidney segmentation models has focused on using a single-phase CT, resulting in significant performance degradation caused by simple characteristic drift in testing datasets, e.g., difference in contrast phase appearance. Materials and methods We introduce a domain adaptation approach leveraging a latent space discriminator to train a robust model for segmenting kidneys from CT volume irrespective of the contrast dose and functional anomaly. We aim to handle three primary domain shifts between training and testing datasets—(i) contrast to non-contrast, (ii) arterial to venous phase, and (iii) normal to abnormal kidney. Results Our model is trained on two publicly available non-contrast and arterial phase image datasets and validated on both public (KiTS21 and STU) and private (Mayo Clinic) datasets with distinct contrast phases and abnormality in the kidney. On all four datasets with domain shift, the proposed model achieved a 0.8892 DICE score, and interestingly, it outperformed the baseline models, including TotalSegmentator, and popular domain adaptation methodologies on the external validation. Conclusion Evaluation of internal and external tests demonstrates improved segmentation quality with domain adaptation while leveraging less data than the baseline. An open-source codebase can be accessed. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Variations in contrast phase uptake are challenging for evaluating impaired kidney function due to differences in imaging appearance. <Emphasis Type="BoldItalic">Findings</Emphasis> The proposed open-source domain adaptation approach for kidney segmentation from CT volumes, handles domain shifts to accurately measure kidney volume regardless of contrast dose or functional anomaly. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> The domain-shift resilient kidney segmentation of the volumetric CT images is crucial for patients as it ensures accurate and automated assessment of kidney health, irrespective of contrast uptake, enabling timely diagnosis and personalized treatment plans, regardless of contrast uptake. Graphical Abstract

Open-source domain adaptation to handle data shift for volumetric segmentation—use case kidney segmentation

期刊论文
发表日期：

2025年6月28日

查看全文

Ramon Correa-Medero,
Umar Ghaffar,
Sam Fathizadeh,
Bhavik Patel,
Haidar Abdul-Muhsin,
Imon Banerjee

摘要

Objectives

Current development of kidney segmentation models has focused on using a single-phase CT, resulting in significant performance degradation caused by simple characteristic drift in testing datasets, e.g., difference in contrast phase appearance.

Materials and methods

We introduce a domain adaptation approach leveraging a latent space discriminator to train a robust model for segmenting kidneys from CT volume irrespective of the contrast dose and functional anomaly. We aim to handle three primary domain shifts between training and testing datasets—(i) contrast to non-contrast, (ii) arterial to venous phase, and (iii) normal to abnormal kidney.

Results

Our model is trained on two publicly available non-contrast and arterial phase image datasets and validated on both public (KiTS21 and STU) and private (Mayo Clinic) datasets with distinct contrast phases and abnormality in the kidney. On all four datasets with domain shift, the proposed model achieved a 0.8892 DICE score, and interestingly, it outperformed the baseline models, including TotalSegmentator, and popular domain adaptation methodologies on the external validation.

Conclusion

Evaluation of internal and external tests demonstrates improved segmentation quality with domain adaptation while leveraging less data than the baseline. An open-source codebase can be accessed.

Key Points

Question Variations in contrast phase uptake are challenging for evaluating impaired kidney function due to differences in imaging appearance.

Findings The proposed open-source domain adaptation approach for kidney segmentation from CT volumes, handles domain shifts to accurately measure kidney volume regardless of contrast dose or functional anomaly.

Clinical relevance The domain-shift resilient kidney segmentation of the volumetric CT images is crucial for patients as it ensures accurate and automated assessment of kidney health, irrespective of contrast uptake, enabling timely diagnosis and personalized treatment plans, regardless of contrast uptake.

Graphical Abstract