ConvNeXt-FD: A Fractal-Based Deep Model for Robust Biomedical Image Segmentation

TL;DR

ConvNeXt-FD is a new deep learning model that improves biomedical image segmentation by combining a ConvNeXt backbone with a boundary-aware loss based on Fractal Dimension, achieving superior results across multiple datasets.

Contribution

The paper introduces ConvNeXt-FD, a novel architecture that integrates a hybrid loss with a Fractal Dimension-based regularization to enhance boundary sensitivity in biomedical segmentation.

Findings

ConvNeXt-FD outperforms existing methods on six biomedical datasets.

The hybrid loss improves boundary delineation and shape fidelity.

Pre-training with ImageNet enhances segmentation performance.

Abstract

Biomedical image segmentation is a critical task in medical diagnosis and treatment planning, enabling precise delineation of anatomical structures and pathological regions. Despite significant advancements, challenges persist due to the inherent variability, noise, and complex morphology present in diverse medical imaging modalities. This paper introduces ConvNeXt-FD, a novel deep learning architecture for robust biomedical image segmentation, built upon a U-Net-like encoder-decoder framework leveraging the powerful ConvNeXt backbone. Our approach integrates a hybrid loss function combining the Dice coefficient with a boundary-aware regularization term inspired by a differentiable formulation of Fractal Dimension, designed to enhance the model's sensitivity to object boundaries and shape fidelity. We rigorously evaluate ConvNeXt-FD across six distinct biomedical datasets: BUSI (Breast Ultrasound Images), DDTI (Thyroid Ultrasound Images), FluoCells (Fluorescent Cell Images), IDRiD (Diabetic Retinopathy Images for Optic Disc Segmentation), ISIC2018 (Skin Lesion Images), and MoNuSeg (Nuclei Segmentation). Experimental results demonstrate that ConvNeXt-FD, particularly when initialized with ImageNet pre-trained weights, achieves competitive and often superior performance compared to existing state-of-the-art methods across various metrics, including Dice, Jaccard, Accuracy, Sensitivity, Specificity, and False Positive Rate. The integration of ConvNeXt as a strong encoder, coupled with the boundary-aware regularization, proves effective in capturing both high-level semantic features and fine-grained boundary details, leading to more accurate and reliable segmentations in challenging biomedical contexts.