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

Basavasagar Patil¹, Lubomir Hadjiiski², Di Sun¹

¹Department of Radiology, University of Michigan, Ann Arbor, MI, USA.

Scientific Reports

|May 13, 2026

Summary

This summary is machine-generated.

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Anatomy of the Genitourinary System II: Bladder and Urethra

Anatomy of the Genitourinary System II: Bladder and Urethra

The lower urinary system consists of the urinary bladder and urethra, which are essential in storing and expelling urine from the body. Together with the internal and external sphincters, these structures work together to regulate urination effectively.Anatomy of the BladderThe urinary bladder is a muscular, stretchable organ behind the pubic bone and in front of the rectum. In females, the bladder is positioned anterior to the vagina and inferior to the uterus, while in males, it is located...

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A new Crop U-Net model significantly improves bladder cancer segmentation accuracy from CT urography scans. This AI approach simplifies the process, offering a more precise tool for treatment response assessment.

Area of Science:

Medical Imaging
Artificial Intelligence in Oncology
Computational Pathology

Background:

Accurate segmentation of bladder cancer lesions in CT urography (CTU) is crucial for treatment response assessment.
Previous methods like deep learning convolutional neural network + level sets (DL-CNN+LS) have limitations.
Transformer-based models (DATTNet, Med-SAM) are emerging for medical image analysis.

Purpose of the Study:

To develop and compare novel U-Net based deep learning models for bladder cancer segmentation.
To evaluate the performance of these models against existing methods (DL-CNN+LS, DATTNet, Med-SAM).
To simplify the segmentation pipeline by removing the need for level set refinement.

Main Methods:

Designed and implemented several U-Net based deep learning models for bladder cancer segmentation.

Keywords:

Bladder Lesion CNNs Deep Learning U-Net

Related Experiment Videos

Proposed a 'Crop U-Net' model using a user-defined box to focus attention on the lesion region.

Trained and evaluated models using radiologist-annotated 3D contours as the ground truth.

Compared performance using metrics like Average Jaccard Index (AJI) and Average Minimum Distance (AMD).

Main Results:

The Crop U-Net model outperformed other investigated models, including DL-CNN+LS, DATTNet, and Med-SAM.
Crop U-Net achieved an AJI of 48.1±18.0% and AMD of 4.3±3.0 mm on an independent test set.
This represents a significant improvement over the previous DL-CNN+LS method (AJI 33.2±20.0%, AMD 5.3±2.2 mm).
The Crop U-Net approach simplified the segmentation pipeline by eliminating the level set refinement stage.

Conclusions:

The Crop U-Net model demonstrates superior accuracy for bladder cancer segmentation compared to previous methods.
This AI-driven approach offers a more efficient and accurate tool for bladder cancer treatment response assessment using CTU.
The simplified pipeline enhances the practicality of using deep learning for clinical decision support in oncology.