An Intuitionistic Fuzzy Logic Driven UNet architecture: Application to Brain Image segmentation

TL;DR

This paper introduces IF-UNet, a novel deep learning architecture that integrates intuitionistic fuzzy logic into UNet to better handle uncertainty and improve brain MRI segmentation accuracy.

Contribution

It presents a new UNet variant incorporating intuitionistic fuzzy logic to address tissue ambiguity and boundary uncertainties in brain image segmentation.

Findings

Improved segmentation accuracy on IBSR dataset.

Enhanced handling of uncertainty in brain images.

Higher Dice coefficient and IoU scores compared to baseline UNet.

Abstract

Accurate segmentation of MRI brain images is essential for image analysis, diagnosis of neuro-logical disorders and medical image computing. In the deep learning approach, the convolutional neural networks (CNNs), especially UNet, are widely applied in medical image segmentation. However, it is difficult to deal with uncertainty due to the partial volume effect in brain images. To overcome this limitation, we propose an enhanced framework, named UNet with intuitionistic fuzzy logic (IF-UNet), which incorporates intuitionistic fuzzy logic into UNet. The model processes input data in terms of membership, nonmembership, and hesitation degrees, allowing it to better address tissue ambiguity resulting from partial volume effects and boundary uncertainties. The proposed architecture is evaluated on the Internet Brain Segmentation Repository (IBSR) dataset, and its performance is computed using accuracy, Dice coefficient, and intersection over union (IoU). Experimental results confirm that IF-UNet improves segmentation quality with handling uncertainty in brain images.