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AI improves medical imaging segmentation for cancer trials

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to improve the accuracy of deep learning models for segmenting clinical target volumes (CTVs) in medical imaging, specifically for the AGITG TOPGEAR clinical trial involving gastric cancer. By incorporating anatomical priors derived from surrounding organ segmentations and employing active learning to iteratively refine the training dataset, the model's performance was significantly enhanced. The combined approach achieved the highest accuracy, demonstrating the potential for automated contour quality assurance in radiotherapy clinical trials. AI

IMPACT Enhances accuracy in medical image segmentation, potentially improving radiotherapy planning and clinical trial efficiency.

RANK_REASON Academic paper detailing a novel methodology for medical image segmentation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI improves medical imaging segmentation for cancer trials

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Academic paper detailing a novel methodology for medical image segmentation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Phillip Chlap, Mark Lee, Trevor Leong, Matthew Field, Jason Dowling, Hang Min, Julie Chu, Jennifer Tan, Phillip K. Tran, Tomas Kron, Annette Haworth, Martin A. Ebert, Shalini K. Vinod, Lois Holloway ·

    Improving Clinical Target Volume Segmentation Accuracy using Anatomical Priors and Active Learning for the AGITG TOPGEAR Clinical Trial

    arXiv:2609.03186v1 Announce Type: cross Abstract: Training deep learning-based medical image segmentation models is challenging with limited curated datasets. For AGITG TOPGEAR, a gastric cancer trial, the Clinical Target Volume (CTV) is complex and defined by multiple anatomical…

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