基于DH-Swin Unet的医学图像分割算法

骨关节疾病自古以来是人类最高发的疾病之一, 随着老龄化的不断加快, 这类疾病日趋广泛, 关节外科医师面临着巨大挑战. 对人体关节的图像分割方法研究可以帮助医生进行临床诊断和治疗, 然而, 由于存在噪声、模糊、对比度低等问题, 医学图像的特征提取比普通图像更具挑战性, 而且目前大多数分割模型在编码器和解码器之间都采用了普通的跳跃连接, 没有注重解决跳跃连接过程中的信息间隙和损失问题. 为解决这些问题, 提出一种基于DH-Swin Unet的医学图像分割算法, 该模型在Swin-Unet模型的基础上, 在跳跃连接中引入密集连接的Swin Transformer块, 并加入混合注意力机制, 来强化网络的特征信息传递. 通过在某三甲医院提供的真实临床数据对所提方法的性能进行评价, 结果表明, 所提出的方法取得了DSC为86.79%、HD为32.05 mm的分割结果, 在关节疾病的临床诊断中具有一定的实用价值.

Medical Image Segmentation Method Based on DH-Swin Unet

Bone and joint diseases have been one of the most prevalent diseases in human history. With the acceleration of aging, these diseases have become increasingly widespread, posing great challenges to orthopedic surgeons. Research on image segmentation for human joints can assist doctors in clinical diagnosis and treatment. However, due to the presence of noise, blurring, and low contrast, medical image feature extraction is more difficult than ordinary images. In addition, most segmentation models only use simple skip connections between the encoder and decoder, without addressing the issues of information gaps and losses during the skip connection process. To this end, this study proposes the DH-Swin Unet algorithm for medical image segmentation. On the basis of the Swin-Unet model, the densely connected Swin Transformer block and hybrid attention mechanism are introduced into skip connection to enhance the feature information transmission. The real clinical data provided by a hospital ranking top three are used to evaluate the performance of the proposed method. The results show that the DSC and HD of the model reach 86.79% and 32.05 mm respectively, and the model has certain practical value in the clinical diagnosis of joint diseases.