ArticleDiagnostics (Basel, Switzerland)2022
A Comparative Study of Deep Neural Networks for Real-Time Semantic Segmentation during the Transurethral Resection of Bladder Tumors.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A multi-modal approach for decision making in bladder cancer.Nature reviews. Urology · 2026Review
- Artificial intelligence and predictive tools in non-muscle invasive bladder cancer: a narrative review of current insights and advances.Translational andrology and urology · 2026Review
- ASO Author Reflections: Artificial Intelligence-Augmented Detection of Bladder Tumors.Annals of surgical oncology · 2025Article
- Leveraging Deep Learning in Real-Time Intelligent Bladder Tumor Detection During Cystoscopy: A Diagnostic Study.Annals of surgical oncology · 2025Article
- Optimizing cystoscopy and TURBT: enhanced imaging and artificial intelligence.Nature reviews. Urology · 2025Review
- PlaqueNet: deep learning enabled coronary artery plaque segmentation from coronary computed tomography angiography.Visual computing for industry, biomedicine, and art · 2024Article
- Emerging Trends in Artificial Intelligence-Based Urological Imaging Technologies and Practical Applications.International neurourology journal · 2023Article
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Authors and funding
2 authors.
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Abstract
Bladder cancer is a common and often fatal disease. Papillary bladder tumors are well detectable using cystoscopic imaging, but small or flat lesions are frequently overlooked by urologists. However, detection accuracy can be improved if the images from the cystoscope are segmented in real time by a deep neural network (DNN). In this paper, we compare eight state-of-the-art DNNs for the semantic segmentation of white-light cystoscopy images: U-Net, UNet++, MA-Net, LinkNet, FPN, PAN, DeepLabv3, and DeepLabv3+. The evaluation includes per-image classification accuracy, per-pixel localization accuracy, prediction speed, and model size. Results show that the best F-score for bladder cancer (91%), the best segmentation map precision (92.91%), and the lowest size (7.93 MB) are also achieved by the PAN model, while the highest speed (6.73 ms) is obtained by DeepLabv3+. These results indicate better tumor localization accuracy than reported in previous studies. It can be concluded that deep neural networks may be extremely useful in the real-time diagnosis and therapy of bladder cancer, and among the eight investigated models, PAN shows the most promising results.
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Registered trials
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