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Quantifying Intermembrane Distances with Serial Image Dilations

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

Adaptive boundary-enhanced Dice loss for image segmentation.

Yanyan Zheng¹, Bihan Tian^2,3, Shuchen Yu^2,3

¹Department of Neurology, Wenzhou People's Hospital, The Third Affiliated Hospital of Shanghai University, Wenzhou 325041, China.

Biomedical Signal Processing and Control

|March 10, 2025

Summary

This summary is machine-generated.

A novel adaptive boundary-enhanced Dice (ABeDice) loss function improves medical image segmentation by enhancing boundary detection. This deep learning approach offers superior accuracy and faster convergence compared to traditional methods.

Keywords:

Boundary regions Dice loss Image segmentation Loss function Swin-Unet

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Area of Science:

Medical Image Analysis
Deep Learning
Computer Vision

Background:

Deep learning models are crucial for medical image segmentation.
Loss function selection significantly impacts segmentation performance.
Accurate segmentation relies on precise boundary localization.

Purpose of the Study:

To introduce and evaluate an adaptive boundary-enhanced Dice (ABeDice) loss function.
To improve medical image segmentation accuracy and efficiency.
To enhance the detection and localization of object boundaries.

Main Methods:

Developed the ABeDice loss function by integrating an exponential recursive complementary (ERC) function with the Dice loss.
Utilized the ERC function to leverage pixel prediction probabilities for boundary enhancement.
Employed dynamic adjustment of prediction probabilities to prioritize higher values.
Validated the ABeDice loss using the Swin-Unet architecture on public datasets (REFUGE, ISIC2018, RIT-Eyes).

Main Results:

The ABeDice loss achieved high average Dice similarity coefficients: 0.9114 (REFUGE), 0.8940 (ISIC2018), and 0.9418 (RIT-Eyes).
Demonstrated superior performance compared to traditional Dice loss and its variants (Generalized Dice, Tervkey, Sensitivity-Specificity).
Showcased improved quantization potential and convergence rate due to adaptive probability distribution.

Conclusions:

The ABeDice loss function effectively enhances medical image segmentation accuracy.
It improves boundary detection and localization, leading to better overall segmentation.
The proposed method offers a promising advancement for deep learning-based medical image segmentation tasks.