SC-Unext: A Lightweight Image Segmentation Model with Cellular Mechanism for Breast Ultrasound Tumor Diagnosis

作者全名："Cai, Fenglin; Wen, Jiaying; He, Fangzhou; Xia, Yulong; Xu, Weijun; Zhang, Yong; Jiang, Li; Li, Jie"

作者地址："[Cai, Fenglin; He, Fangzhou; Li, Jie] Chongqing Univ Sci & Technol, Dept Intelligent Technol & Engn, Chongqing 401331, Peoples R China; [Wen, Jiaying; Xia, Yulong; Xu, Weijun; Zhang, Yong; Jiang, Li] Chongqing Med Univ, Affiliated Hosp 1, Dept Neurosurg, Chongqing 400016, Peoples R China"

通信作者："Li, J (通讯作者)，Chongqing Univ Sci & Technol, Dept Intelligent Technol & Engn, Chongqing 401331, Peoples R China.; Jiang, L (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Neurosurg, Chongqing 400016, Peoples R China."

来源：JOURNAL OF IMAGING INFORMATICS IN MEDICINE

ESI学科分类：　

WOS号：WOS:001284805400040

JCR分区：　

影响因子：　

年份：2024

卷号：37

期号：4

开始页：1505

结束页：1515

文献类型：Article

关键词：Breast ultrasound; Deep learning; Tumor segmentation; Lightweight

摘要："Automatic breast ultrasound image segmentation plays an important role in medical image processing. However, current methods for breast ultrasound segmentation suffer from high computational complexity and large model parameters, particularly when dealing with complex images. In this paper, we take the Unext network as a basis and utilize its encoder-decoder features. And taking inspiration from the mechanisms of cellular apoptosis and division, we design apoptosis and division algorithms to improve model performance. We propose a novel segmentation model which integrates the division and apoptosis algorithms and introduces spatial and channel convolution blocks into the model. Our proposed model not only improves the segmentation performance of breast ultrasound tumors, but also reduces the model parameters and computational resource consumption time. The model was evaluated on the breast ultrasound image dataset and our collected dataset. The experiments show that the SC-Unext model achieved Dice scores of 75.29% and accuracy of 97.09% on the BUSI dataset, and on the collected dataset, it reached Dice scores of 90.62% and accuracy of 98.37%. Meanwhile, we conducted a comparison of the model's inference speed on CPUs to verify its efficiency in resource-constrained environments. The results indicated that the SC-Unext model achieved an inference speed of 92.72 ms per instance on devices equipped only with CPUs. The model's number of parameters and computational resource consumption are 1.46M and 2.13 GFlops, respectively, which are lower compared to other network models. Due to its lightweight nature, the model holds significant value for various practical applications in the medical field."

基金机构：Chongqing Municipal undergraduate universities and institutes affiliated to the Chinese Academy of Sciences; First Affiliated Hospital of Chongqing Medical University

基金资助正文："Thanks to the following medical centers for providing data support: the First Affiliated Hospital of Chongqing Medical University, the Second Affiliated Hospital of Chongqing Medical University, University-Town Hospital of Chongqing Medical University."