Chen Jiadong, Shen Xiaoyan, Zhao Yu, Qian Wei, Ma He, Sang Liang
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
School of Life and Health Technology, Dongguan University of Technology, Dongguan, China.
Quant Imaging Med Surg. 2024 Feb 1;14(2):2034-2048. doi: 10.21037/qims-23-947. Epub 2024 Jan 22.
In recent years, computer-aided diagnosis (CAD) systems have played an important role in breast cancer screening and diagnosis. The image segmentation task is the key step in a CAD system for the rapid identification of lesions. Therefore, an efficient breast image segmentation network is necessary for improving the diagnostic accuracy in breast cancer screening. However, due to the characteristics of blurred boundaries, low contrast, and speckle noise in breast ultrasound images, breast lesion segmentation is challenging. In addition, many of the proposed breast tumor segmentation networks are too complex to be applied in practice.
We developed the attention gate and dilation U-shaped network (GDUNet), a lightweight, breast lesion segmentation model. This model improves the inverted bottleneck, integrating it with tokenized multilayer perceptron (MLP) to construct the encoder. Additionally, we introduce the lightweight attention gate (AG) within the skip connection, which effectively filters noise in low-level semantic information across spatial and channel dimensions, thus attenuating irrelevant features. To further improve performance, we innovated the AG dilation (AGDT) block and embedded it between the encoder and decoder in order to capture critical multiscale contextual information.
We conducted experiments on two breast cancer datasets. The experiment's results show that compared to UNet, GDUNet could reduce the number of parameters by 10 times and the computational complexity by 58 times while providing a double of the inference speed. Moreover, the GDUNet achieved a better segmentation performance than did the state-of-the-art medical image segmentation architecture.
Our proposed GDUNet method can achieve advanced segmentation performance on different breast ultrasound image datasets with high efficiency.
近年来，计算机辅助诊断（CAD）系统在乳腺癌筛查和诊断中发挥了重要作用。图像分割任务是CAD系统中快速识别病变的关键步骤。因此，一个高效的乳腺图像分割网络对于提高乳腺癌筛查的诊断准确性是必要的。然而，由于乳腺超声图像存在边界模糊、对比度低和斑点噪声等特点，乳腺病变分割具有挑战性。此外，许多已提出的乳腺肿瘤分割网络过于复杂，难以在实际中应用。
我们开发了注意力门控与扩张U型网络（GDUNet），这是一种轻量级的乳腺病变分割模型。该模型改进了倒置瓶颈结构，将其与令牌化多层感知器（MLP）集成以构建编码器。此外，我们在跳跃连接中引入了轻量级注意力门控（AG），它能在空间和通道维度上有效过滤低层次语义信息中的噪声，从而减弱无关特征。为进一步提高性能，我们创新了AG扩张（AGDT）模块，并将其嵌入到编码器和解码器之间，以捕获关键的多尺度上下文信息。
我们在两个乳腺癌数据集上进行了实验。实验结果表明，与U-Net相比，GDUNet可将参数数量减少10倍，计算复杂度降低58倍，同时推理速度提高一倍。此外，GDUNet的分割性能优于当前最先进的医学图像分割架构。
我们提出的GDUNet方法能够高效地在不同的乳腺超声图像数据集上实现先进的分割性能。
