ArticleCancers2024
Deep Learning Algorithms for Bladder Cancer Segmentation on Multi-Parametric MRI.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A two-stage foundation model for bladder tumor segmentation: An international multi-site studyEuropean journal of radiology artificial intelligence · 2026Article
- Diagnostic and Prognostic Non-Invasive Markers in Bladder Cancer.Diagnostics (Basel, Switzerland) · 2026Review
- What's next for VI-RADS? Updates and future perspectives from the ACR VI-RADS steering committee.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Review
- Review
- Deep learning segmentation of paediatric brain tumours using diffusion-weighted MRI: towards an early,Brain communications · 2026Article
- TW-YOLO: An Innovative Blood Cell Detection Model Based on Multi-Scale Feature Fusion.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundBladder cancer (BC) segmentation on MRI images is the first step to determining the presence of muscular invasion. This study aimed to assess the tumor segmentation performance of three deep learning (DL) models on multi-parametric MRI (mp-MRI) images.
methodsWe studied 53 patients with bladder cancer. Bladder tumors were segmented on each slice of T2-weighted (T2WI), diffusion-weighted imaging/apparent diffusion coefficient (DWI/ADC), and T1-weighted contrast-enhanced (T1WI) images acquired at a 3Tesla MRI scanner. We trained Unet, MAnet, and PSPnet using three loss functions: cross-entropy (CE), dice similarity coefficient loss (DSC), and focal loss (FL). We evaluated the model performances using DSC, Hausdorff distance (HD), and expected calibration error (ECE).
resultsThe MAnet algorithm with the CE+DSC loss function gave the highest DSC values on the ADC, T2WI, and T1WI images. PSPnet with CE+DSC obtained the smallest HDs on the ADC, T2WI, and T1WI images. The segmentation accuracy overall was better on the ADC and T1WI than on the T2WI. The ECEs were the smallest for PSPnet with FL on the ADC images, while they were the smallest for MAnet with CE+DSC on the T2WI and T1WI.
conclusionsCompared to Unet, MAnet and PSPnet with a hybrid CE+DSC loss function displayed better performances in BC segmentation depending on the choice of the evaluation metric.
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