Lv Jinkai, Hu Yuyong, Fu Quanshui, Hu Yuqiang, Lv Lin, Yang Guoqing, Li Jinpeng, Zhao Yi
IEEE Trans Nanobioscience. 2023 Oct;22(4):828-835. doi: 10.1109/TNB.2023.3276473. Epub 2023 Oct 3.
Convolution-based methods are increasingly being used in medical image segmentation tasks and have shown good performance, but there are always problems in segmenting edge parts. These methods all have the following challenges: 1) Previous methods do not highlight the relationship between foreground and background in segmented regions, which is helpful for complex segmentation edges, 2) inductive bias of the convolutional layer leads to the fact that the extracted information is mainly the main part of the segmented area, and cannot effectively perceive complex edge changes and the aggregation of small and many segmented areas,3) different regions around the segmentation edge have different reference values for segmentation, and the ordering of these values is more important when the segmentation task is more complex. To address these challenges, we propose the CM-MLP framework on Multi-scale Feature Interaction (MFI) block and Axial Context Relation Encoder (ACRE) block for accurate segmentation of the edge of medical image. In the MFI block, we propose the Cascade Multi-scale MLP (Cascade MLP) to process all local information from the deeper layers of the network simultaneously, using Squeeze and Excitation in Space(SES) to process and redistribute the weights of all windows in Cascade MLP and utilize a cascade multi-scale mechanism to fuse discrete local information gradually. Then, multiple ACRE blocks cooperate with the deep supervision mechanism to gradually explore the boundary relationship between the foreground and the background, and gradually fine-tune the edges of the medical image. The segmentation accuracy (Dice) of our proposed CM-MLP framework reaches 96.98%, 96.67%, and 83.83% on three benchmark datasets: CVC-ClinicDB dataset, sub-Kvasir dataset, and our in-house dataset, respectively, which significantly outperform the state-of-the-art method. The source code and trained models will be available at https://github.com/ProgrammerHyy/CM-MLP.
基于卷积的方法越来越多地应用于医学图像分割任务中，并表现出良好的性能，但在分割边缘部分时总是存在问题。这些方法都面临以下挑战：1）先前的方法没有突出分割区域中前景与背景之间的关系，而这种关系对复杂的分割边缘很有帮助；2）卷积层的归纳偏差导致提取的信息主要是分割区域的主要部分，无法有效感知复杂的边缘变化以及小而多的分割区域的聚集情况；3）分割边缘周围的不同区域对于分割具有不同的参考值，并且当分割任务更复杂时，这些值的排序更为重要。为应对这些挑战，我们提出了基于多尺度特征交互（MFI）块和轴向上下文关系编码器（ACRE）块的CM-MLP框架，用于准确分割医学图像的边缘。在MFI块中，我们提出了级联多尺度MLP（Cascade MLP）来同时处理来自网络更深层的所有局部信息，使用空间挤压与激励（SES）来处理和重新分配Cascade MLP中所有窗口的权重，并利用级联多尺度机制逐步融合离散的局部信息。然后，多个ACRE块与深度监督机制协作，逐步探索前景与背景之间的边界关系，并逐步微调医学图像的边缘。我们提出的CM-MLP框架在三个基准数据集：CVC-ClinicDB数据集、sub-Kvasir数据集和我们的内部数据集上的分割准确率（Dice）分别达到了96.98%、96.67%和83.83%，显著优于当前的最优方法。源代码和训练好的模型将在https://github.com/ProgrammerHyy/CM-MLP上提供。
