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Related Experiment Video

Updated: Jul 4, 2025

An Orthotopic Bladder Tumor Model and the Evaluation of Intravesical saRNA Treatment
08:43

An Orthotopic Bladder Tumor Model and the Evaluation of Intravesical saRNA Treatment

Published on: July 28, 2012

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A lightweight bladder tumor segmentation method based on attention mechanism.

Xiushun Zhao1, Libing Lai2, Yunjiao Li1

  • 1School of Automation, Guangdong University of Technology, Guangzhou, 510006, China.

Medical & Biological Engineering & Computing
|February 2, 2024
PubMed
Summary

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This summary is machine-generated.

A novel segmentation network, NAFF-Net, accurately identifies bladder tumors in endoscopic images. This method improves diagnostic accuracy and treatment planning by effectively segmenting tumors with blurred boundaries and variable shapes.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Accurate bladder tumor segmentation in endoscopic images is crucial for clinical diagnosis and treatment planning.
  • Challenges include blurred boundaries and highly variable tumor shapes, complicating segmentation efforts.

Purpose of the Study:

  • To develop an advanced segmentation network for improved bladder tumor detection in endoscopic imaging.
  • To enhance diagnostic accuracy and facilitate better patient treatment strategies.

Main Methods:

  • Proposed a nested attentional feature fusion segmentation network (NAFF-Net) utilizing an encoder-decoder architecture.
  • Incorporated a weighted pyramid pooling module (WPPM) with atrous convolution for enhanced feature extraction and a nested attentional feature fusion (NAFF) module for detail focus.
Keywords:
Attentional feature fusionBladder tumor segmentationWeighted mixed loss functionWeighted pyramid pooling module

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  • Developed a weighted mixed loss function to address class imbalance issues.

    • Main Results:

    • NAFF-Net achieved superior segmentation performance with a mean Intersection over Union (MIoU) of 84.05%, MPrecision of 91.52%, MRecall of 90.81%, and F1-score of 91.16%.
    • Demonstrated strong results on public datasets Kvasir-SEG and CVC-ClinicDB.
    • Exhibited a reduced parameter count compared to other models, enhancing deployability.

    Conclusions:

    • NAFF-Net offers a significant advancement in bladder tumor segmentation accuracy and efficiency.
    • The network's ability to handle complex image features and its computational efficiency make it suitable for clinical deployment.