Bridging the Applicator Gap with Data-Doping:Dual-Domain Learning for Precise Bladder Segmentation in CT-Guided Brachytherapy

2026-01-28

AI Summary

This study addresses the performance degradation of deep learning models in bladder segmentation for CT-guided gynecological brachytherapy, which arises from covariate shift and the scarcity, severe deformation, and artifacts in with-applicator (WA) CT images. To overcome this challenge, the authors propose a dual-domain learning strategy that enables cross-distribution knowledge transfer by jointly training on abundant without-applicator (NA) CT data and limited WA data. An innovative “data doping” mechanism is introduced, achieving segmentation performance comparable to full WA training using only 10%–30% WA data. By integrating multiplanar inputs and diverse deep architectures, the method attains a Dice coefficient of 0.94 and an IoU of 0.92, significantly enhancing both segmentation accuracy and clinical robustness.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Segmentation
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Performance degradation due to covariate shift remains a major challenge for deep learning models in medical image segmentation. An open question is whether samples from a shifted distribution can effectively support learning when combined with limited target domain data. We investigate this problem in the context of bladder segmentation in CT guided gynecological brachytherapy, a critical task for accurate dose optimization and organ at risk sparing. While CT scans without brachytherapy applicators (no applicator: NA) are widely available, scans with applicators inserted (with applicator: WA) are scarce and exhibit substantial anatomical deformation and imaging artifacts, making automated segmentation particularly difficult. We propose a dual domain learning strategy that integrates NA and WA CT data to improve robustness and generalizability under covariate shift. Using a curated assorted dataset, we show that NA data alone fail to capture the anatomical and artifact related characteristics of WA images. However, introducing a modest proportion of WA data into a predominantly NA training set leads to significant performance improvements. Through systematic experiments across axial, coronal, and sagittal planes using multiple deep learning architectures, we demonstrate that doping only 10 to 30 percent WA data achieves segmentation performance comparable to models trained exclusively on WA data. The proposed approach attains Dice similarity coefficients of up to 0.94 and Intersection over Union scores of up to 0.92, indicating effective domain adaptation and improved clinical reliability. This study highlights the value of integrating anatomically similar but distribution shifted datasets to overcome data scarcity and enhance deep learning based segmentation for brachytherapy treatment planning.

Problem

• covariate shift
• bladder segmentation
• brachytherapy
• data scarcity
• medical image segmentation

Innovation

• data-doping
• dual-domain learning
• covariate shift
• bladder segmentation
• brachytherapy