Enhancing nuclei segmentation in breast histopathology images using U-Net with backbone architectures.
Authors
Affiliations (3)
Affiliations (3)
- Department of ECE, College of Engineering Trivandrum (Affiliated to APJ Abdul Kalam Technological University), Sreekariyam, Thiruvananthapuram, 695016, Kerala, India. Electronic address: [email protected].
- Department of ECE, College of Engineering Munnar, Munnar, 685612, Kerala, India.
- Department of ECE, College of Engineering Trivandrum (Affiliated to APJ Abdul Kalam Technological University), Sreekariyam, Thiruvananthapuram, 695016, Kerala, India.
Abstract
Breast cancer remains a leading cause of mortality among women worldwide, underscoring the need for accurate and timely diagnostic methods. Precise segmentation of nuclei in breast histopathology images is crucial for effective diagnosis and prognosis, offering critical insights into tumor characteristics and informing treatment strategies. This paper presents an enhanced U-Net architecture utilizing ResNet-34 as an advanced backbone, aimed at improving nuclei segmentation performance. The proposed model is evaluated and compared with standard U-Net and its other variants, including U-Net with VGG-16 and Inception-v3 backbones, using the BreCaHad dataset with nuclei masks generated through ImageJ software. The U-Net model with ResNet-34 backbone achieved superior performance, recording an Intersection over Union (IoU) score of 0.795, significantly outperforming the basic U-Net's IoU score of 0.725. The integration of advanced backbones and data augmentation techniques substantially improved segmentation accuracy, especially on limited medical imaging datasets. Comparative analysis demonstrated that ResNet-34 consistently surpassed other configurations across multiple metrics, including IoU, accuracy, precision, and F1 score. Further validation on the BNS and MoNuSeg-2018 datasets confirmed the robustness of the proposed model. This study highlights the potential of advanced deep learning architectures combined with augmentation methods to address challenges in nuclei segmentation, contributing to the development of more effective clinical diagnostic tools and improved patient care outcomes.