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<p>We are developing a decision support system for treatment response assessment of bladder cancer by analyzing patients’ CT urography (CTU) examinations. Accurate segmentation of bladder lesions is a critical and challenging task. We previously developed a bladder cancer segmentation method using a deep learning convolutional neural network and level sets (DL-CNN + LS). In this study, we designed several deep learning models based on U-Net for bladder cancer segmentation and compared them with DL-CNN + LS and two transformer-based models developed for medical imaging - DATTNet and the Med-Segment Anything Model (Med-SAM). Our new U-Net models did not use the second-stage level set refinement, greatly simplifying the overall segmentation pipeline. We trained and evaluated the models by using radiologist’s hand-drawn 3D contours as the reference standard. The proposed Crop U-Net model, utilizing a user-defined box to direct the U-Net attention to the lesion region by masking out the structured background, was superior to other models being investigated. On the independent test set, the Crop U-Net achieved average Jaccard index (AJI) of 48.1 ± 18.0% and average minimum distance (AMD) of 4.3 ± 3.0&#xa0;mm, while the DL-CNN + LS achieved AJI of 33.2 ± 20.0% and AMD of 5.3 ± 2.2&#xa0;mm. The results demonstrated that the Crop U-Net could achieve a higher accuracy than the previous DL-CNN + LS while reducing the complexity of the segmentation pipeline.</p>

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Bladder cancer segmentation using u-net-based deep-learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月13日

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  • Basavasagar Patil,
  • Lubomir Hadjiiski,
  • Di Sun,
  • Heang-Ping Chan,
  • Richard H. Cohan,
  • Elaine M. Caoili,
  • Chuan Zhou

摘要

We are developing a decision support system for treatment response assessment of bladder cancer by analyzing patients’ CT urography (CTU) examinations. Accurate segmentation of bladder lesions is a critical and challenging task. We previously developed a bladder cancer segmentation method using a deep learning convolutional neural network and level sets (DL-CNN + LS). In this study, we designed several deep learning models based on U-Net for bladder cancer segmentation and compared them with DL-CNN + LS and two transformer-based models developed for medical imaging - DATTNet and the Med-Segment Anything Model (Med-SAM). Our new U-Net models did not use the second-stage level set refinement, greatly simplifying the overall segmentation pipeline. We trained and evaluated the models by using radiologist’s hand-drawn 3D contours as the reference standard. The proposed Crop U-Net model, utilizing a user-defined box to direct the U-Net attention to the lesion region by masking out the structured background, was superior to other models being investigated. On the independent test set, the Crop U-Net achieved average Jaccard index (AJI) of 48.1 ± 18.0% and average minimum distance (AMD) of 4.3 ± 3.0 mm, while the DL-CNN + LS achieved AJI of 33.2 ± 20.0% and AMD of 5.3 ± 2.2 mm. The results demonstrated that the Crop U-Net could achieve a higher accuracy than the previous DL-CNN + LS while reducing the complexity of the segmentation pipeline.