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CLFSeg: A Fuzzy-Logic based Solution for Boundary Clarity and Uncertainty Reduction in Medical Image Segmentation

Anshul Kaushal, Kunal Jangid, Vinod K. Kurmi

TL;DR

This work tackles boundary uncertainty in medical image segmentation by introducing CLFSeg, an encoder-decoder framework that fuses conventional CNN features with a Fuzzy-Convolution (FC) module to better capture local and global context while reducing boundary ambiguity. The FC module comprises five parallel branches and ConvGLU, with a streamlined single-ResNet path to cut computational cost by approximately $30\%$ and improve gradient flow. Across four public datasets (polyp and cardiac MRI), CLFSeg achieves state-of-the-art DSC scores and favorable HD95 boundary metrics, supported by Grad-CAM++ visualizations for interpretability. The approach demonstrates strong cross-domain performance and computational efficiency, making it suitable for real-world clinical deployment and paving the way for broader dataset coverage and privacy-aware applications.

Abstract

Accurate polyp and cardiac segmentation for early detection and treatment is essential for the diagnosis and treatment planning of cancer-like diseases. Traditional convolutional neural network (CNN) based models have represented limited generalizability, robustness, and inability to handle uncertainty, which affects the segmentation performance. To solve these problems, this paper introduces CLFSeg, an encoder-decoder based framework that aggregates the Fuzzy-Convolutional (FC) module leveraging convolutional layers and fuzzy logic. This module enhances the segmentation performance by identifying local and global features while minimizing the uncertainty, noise, and ambiguity in boundary regions, ensuring computing efficiency. In order to handle class imbalance problem while focusing on the areas of interest with tiny and boundary regions, binary cross-entropy (BCE) with dice loss is incorporated. Our proposed model exhibits exceptional performance on four publicly available datasets, including CVC-ColonDB, CVC-ClinicDB, EtisLaribPolypDB, and ACDC. Extensive experiments and visual studies show CLFSeg surpasses the existing SOTA performance and focuses on relevant regions of interest in anatomical structures. The proposed CLFSeg improves performance while ensuring computing efficiency, which makes it a potential solution for real-world medical diagnostic scenarios. Project page is available at https://visdomlab.github.io/CLFSeg/

CLFSeg: A Fuzzy-Logic based Solution for Boundary Clarity and Uncertainty Reduction in Medical Image Segmentation

TL;DR

This work tackles boundary uncertainty in medical image segmentation by introducing CLFSeg, an encoder-decoder framework that fuses conventional CNN features with a Fuzzy-Convolution (FC) module to better capture local and global context while reducing boundary ambiguity. The FC module comprises five parallel branches and ConvGLU, with a streamlined single-ResNet path to cut computational cost by approximately and improve gradient flow. Across four public datasets (polyp and cardiac MRI), CLFSeg achieves state-of-the-art DSC scores and favorable HD95 boundary metrics, supported by Grad-CAM++ visualizations for interpretability. The approach demonstrates strong cross-domain performance and computational efficiency, making it suitable for real-world clinical deployment and paving the way for broader dataset coverage and privacy-aware applications.

Abstract

Accurate polyp and cardiac segmentation for early detection and treatment is essential for the diagnosis and treatment planning of cancer-like diseases. Traditional convolutional neural network (CNN) based models have represented limited generalizability, robustness, and inability to handle uncertainty, which affects the segmentation performance. To solve these problems, this paper introduces CLFSeg, an encoder-decoder based framework that aggregates the Fuzzy-Convolutional (FC) module leveraging convolutional layers and fuzzy logic. This module enhances the segmentation performance by identifying local and global features while minimizing the uncertainty, noise, and ambiguity in boundary regions, ensuring computing efficiency. In order to handle class imbalance problem while focusing on the areas of interest with tiny and boundary regions, binary cross-entropy (BCE) with dice loss is incorporated. Our proposed model exhibits exceptional performance on four publicly available datasets, including CVC-ColonDB, CVC-ClinicDB, EtisLaribPolypDB, and ACDC. Extensive experiments and visual studies show CLFSeg surpasses the existing SOTA performance and focuses on relevant regions of interest in anatomical structures. The proposed CLFSeg improves performance while ensuring computing efficiency, which makes it a potential solution for real-world medical diagnostic scenarios. Project page is available at https://visdomlab.github.io/CLFSeg/

Paper Structure

This paper contains 8 sections, 2 equations, 4 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. CLFSeg Model
  5. Experimental Results
  6. Comparison with SOTA
  7. Ablation Study
  8. Conclusion

Figures (4)

  • Figure 1: Overview of the CLFSeg model, showing the encoder-decoder structure with skip connections and downscaling layers. It highlights the integration of ResNet blocks, convolutional layers, and the Fuzzy-Convolution (FC) module designed to handle uncertainty and ambiguity in medical image segmentation, especially for polyp and cardiac structures. (Best view in color).
  • Figure 2: Overview of FC Module within the CLFSeg architecture, composed of five parallel branches: Midscope, Widescope, Separable, ResNet, and Fuzzy Module, followed by ConvGLU and batch normalization layers. (Best view in color).
  • Figure 3: Comparison of GradCam++ Visualization between DuckNet and CLFSeg model on CVC-ColonDB dataset. (Best view in color).
  • Figure 4: Comparison of Segmentation Map from proposed CLFSeg model for CVC-ColonDB (left side), and ACDC dataset (right side). (Best view in color).