PDF(Pubmed)
Abstract：
The abdomen houses multiple vital organs, which are associated with various diseases posing significant risks to human health. Early detection of abdominal organ conditions allows for timely intervention and treatment, preventing deterioration of patients\' health. Segmenting abdominal organs aids physicians in more accurately diagnosing organ lesions. However, the anatomical structures of abdominal organs are relatively complex, with organs overlapping each other, sharing similar features, thereby presenting challenges for segmentation tasks. In real medical scenarios, models must demonstrate real-time and low-latency features, necessitating an improvement in segmentation accuracy while minimizing the number of parameters. Researchers have developed various methods for abdominal organ segmentation, ranging from convolutional neural networks (CNNs) to Transformers. However, these methods often encounter difficulties in accurately identifying organ segmentation boundaries. MetaFormer abstracts the framework of Transformers, excluding the multi-head Self-Attention, offering a new perspective for solving computer vision problems and overcoming the limitations of Vision Transformers and CNN backbone networks. To further enhance segmentation effectiveness, we propose a U-shaped network, integrating SEFormer and depthwise cascaded upsampling (dCUP) as the encoder and decoder, respectively, into the UNet structure, named SEF-UNet. SEFormer combines Squeeze-and-Excitation modules with depthwise separable convolutions, instantiating the MetaFormer framework, enhancing the capture of local details and texture information, thereby improving edge segmentation accuracy. dCUP further integrates shallow and deep information layers during the upsampling process. Our model significantly improves segmentation accuracy while reducing the parameter count and exhibits superior performance in segmenting organ edges that overlap each other, thereby offering potential deployment in real medical scenarios.
摘要：
腹部有多个重要器官，与各种疾病有关，对人类健康构成重大风险。及早发现腹部器官状况,可以及时进行干预和治疗,防止患者健康恶化。分割腹部器官有助于医生更准确地诊断器官病变。然而,腹部器官的解剖结构相对复杂，器官相互重叠，共享类似的功能，从而为细分任务提出了挑战。在真实的医疗场景中，模型必须展示实时和低延迟功能，需要在最小化参数数量的同时提高分割精度。研究人员开发了各种腹部器官分割方法，从卷积神经网络(CNN)到变形金刚。然而,这些方法在准确识别器官分割边界时经常遇到困难。MetaFormer抽象了变形金刚的框架，不包括多头自我关注，为解决计算机视觉问题和克服视觉变形金刚和CNN骨干网络的局限性提供了新的视角。为了进一步提高分割效果，我们提出了一个U形网络，集成SEFormer和深度级联上采样（dCUP）作为编码器和解码器，分别,进入UNet结构，名为SEF-UNet。SEFormer将挤压和激励模块与深度可分离卷积相结合，实例化MetaFormer框架，增强局部细节和纹理信息的捕获，从而提高边缘分割精度。dCUP在上采样过程中进一步集成了浅层和深层信息层。我们的模型显着提高了分割精度，同时减少了参数计数，并在分割彼此重叠的器官边缘方面表现出卓越的性能，从而在真实的医疗场景中提供潜在的部署。
