Abstract:

Concerning the problems of the difference in the number of samples between classes and the diversity of image background caused by environmental factors in the screening method of adolescent scoliosis based on back images and deep learning， a scoliosis screening method was designed， including steps such as back image data enhancement， back region extraction， and scoliosis diagnosis. Firstly， an improved diffusion generation model based on double residual U-Net structure and Convolutional Self-Attention Mechanism （CSAM） was proposed to generate high-quality pseudo-samples for the minority class back images， so as to balance the class distribution. Secondly， a back region extraction model with multi-loss constraint balance was designed to identify and extract back region from the back image， so as to eliminate the influence of image background difference on the diagnosis model. Thirdly， based on the selective kernel feature extraction and Spatial Pyramid Pooling （SPP） technologies， a classification model was constructed to realize the early screening and severity diagnosis of scoliosis through the back region. Finally， by integrating the above methods， the computer software and mobile software were developed to facilitate the actual back image acquisition and scoliosis screening business. Experimental results show that on the self-made scoliosis dataset， the proposed method achieves 98.64% and 73.06% accuracy in scoliosis early screening and severity diagnosis tasks， respectively， which is 2.52 and 6.48 percentage points higher than that of ResNet101. It can be seen that the proposed method can complete the diagnosis of scoliosis conveniently and quickly， and has certain application scenarios in large-scale rapid screening of scoliosis.

Key words: scoliosis, deep learning, class imbalance, back region extraction, selective kernel

摘要：

针对基于背部图像和深度学习的青少年脊柱侧弯筛查方法存在的类间样本数量差异以及环境因素引起的图像背景多样性等问题，设计一种脊柱侧弯筛查方法，包括背部图像数据增强、背部区域提取和脊柱侧弯诊断等步骤。首先，提出一种基于双残差U-Net结构和卷积自注意力机制（CSAM）的改进的扩散生成模型，为少数类背部图像生成高质量的伪样本以平衡类别分布；其次，设计一种多损失约束平衡的背部区域提取模型来从背部图像中识别并提取背部区域，以消除图像背景差异对诊断模型的影响；继次，基于选择性核特征提取和空间金字塔池化（SPP）技术，构建一种分类模型，从而通过背部区域实现脊柱侧弯早筛及严重度诊断；最后，集成上述方法，开发电脑端和移动端软件，以便开展实际背部图像采集和脊柱侧弯筛查业务。实验结果表明，在自制的脊柱侧弯数据集上，所提方法在脊柱侧弯早筛及严重度诊断任务上分别达到了98.64%和73.06%的准确率，与ResNet101相比，分别提升了2.52和6.48个百分点。可见，所提方法能够方便且快速地完成脊柱侧弯诊断，在脊柱侧弯的大规模快速筛查中有一定的应用场景。

关键词: 脊柱侧弯, 深度学习, 类不平衡, 背部区域提取, 选择性核