Open Access

ARTICLE

Two Stages Segmentation Algorithm of Breast Tumor in DCE-MRI Based on Multi-Scale Feature and Boundary Attention Mechanism

Bing Li^1,2,*, Liangyu Wang¹, Xia Liu^1,2, Hongbin Fan¹, Bo Wang³, Shoudi Tong¹

1 School of Automation, Harbin University of Science and Technology, Harbin, 150080, China
2 Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin, 150080, China
3 School of Computer Engineering Technology, Guangdong Institute of Science and Technology, Zhuhai, 519090, China

* Corresponding Author: Bing Li. Email:

(This article belongs to the Special Issue: Deep Learning in Computer-Aided Diagnosis Based on Medical Image)

Computers, Materials & Continua 2024, 80(1), 1543-1561. https://doi.org/10.32604/cmc.2024.052009

Received 20 March 2024; Accepted 24 May 2024; Issue published 18 July 2024

Abstract

Nuclear magnetic resonance imaging of breasts often presents complex backgrounds. Breast tumors exhibit varying sizes, uneven intensity, and indistinct boundaries. These characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor segmentation. Thus, we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention mechanisms. Initially, the breast region of interest is extracted to isolate the breast area from surrounding tissues and organs. Subsequently, we devise a fusion network incorporating multi-scale features and boundary attention mechanisms for breast tumor segmentation. We incorporate multi-scale parallel dilated convolution modules into the network, enhancing its capability to segment tumors of various sizes through multi-scale convolution and novel fusion techniques. Additionally, attention and boundary detection modules are included to augment the network’s capacity to locate tumors by capturing nonlocal dependencies in both spatial and channel domains. Furthermore, a hybrid loss function with boundary weight is employed to address sample class imbalance issues and enhance the network’s boundary maintenance capability through additional loss. The method was evaluated using breast data from 207 patients at Ruijin Hospital, resulting in a 6.64% increase in Dice similarity coefficient compared to the benchmark U-Net. Experimental results demonstrate the superiority of the method over other segmentation techniques, with fewer model parameters.

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Keywords

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