Abstract：
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.
摘要：
自动乳腺超声图像分割在医学图像处理中起着重要的作用。然而,目前的乳腺超声分割方法存在计算复杂度高、模型参数大等问题,特别是在处理复杂图像时。在本文中,我们以Unext网络为基础，并利用其编码器-解码器功能。并从细胞凋亡和分裂的机制中得到启示，我们设计了凋亡和划分算法来提高模型性能。我们提出了一种新颖的分割模型，该模型集成了分割和凋亡算法，并在模型中引入了空间和通道卷积块。我们提出的模型不仅提高了乳腺超声肿瘤的分割性能,同时也减少了模型参数和计算资源消耗时间。在乳腺超声图像数据集和我们收集的数据集上评估模型。实验表明,SC-Unext模型在BUSI数据集上取得了75.29%的Dice得分和97.09%的准确率,在收集的数据集上，Dice评分为90.62%,准确率为98.37%。同时,我们对该模型在CPU上的推理速度进行了比较，以验证其在资源受限环境中的效率。结果表明，SC-Unext模型在仅配备CPU的设备上实现了每个实例92.72ms的推理速度。模型的参数数量和计算资源消耗分别为1.46M和2.13GFlops，分别,与其他网络模型相比更低。由于其重量轻的性质，该模型对医学领域的各种实际应用具有重要价值。
