LM-UNet: 横向MLP用于增强U-Net的医学图像分割

卷积神经网络(CNN)作为医学图像分割领域中U-Net基线网络的重要组成部分,其主要作用是处理局部特征信息之间的关系.而Transformer是一种能够有效强化特征信息之间的远距离依赖关系的视觉模型.目前的研究表明,结合Transformer和CNN可以在一定程度上提高医学图像分割的准确性.但是,由于医学图像的标注数据较少,而且训练Transformer模型需要大量数据,这使得Transformer模型面临耗时长和参数量大的挑战.基于这些考虑,本文在UNeXt模型的基础上,结合多尺度混合MLP和CNN,提出了一种新型的基于混合MLP的医学图像分割模型——LM-UNet.这种模型能够有效地增强局部与全局信息之间的联系,并加强特征信息间的融合.在多个数据集上的实验表明, LM-UNet模型在皮肤数据集上的分割性能明显提升,平均Dice系数达到92.58%,平均IoU系数达到86.52%,分别比UNeXt模型提高了3%和3.5%.在软骨和乳腺数据集上的分割效果也有显著提升,平均Dice系数分别比UNeXt提高了2.5%和1.0%.因此, LM-UNet模型不仅提高了医学图像分割的准确性,还增...

LM-UNet: Lateral MLP Augmented U-Net for Medical Image Segmentation

Convolutional neural network (CNN), as an important part of U-Net baseline networks in the field of medical image segmentation, is mainly used to deal with the relationships among local feature information. Transformer is a visual model that can effectively strengthen the long-distance dependency among feature information. The previous study shows that Transformer can be combined with CNNs to improve the accuracy of medical image segmentation to a certain extent. However, labeled data in medical images are rarely available while a large amount of data is required to train the Transformer model, exposing the Transformer model to the challenges of high time consumption and a large number of parameters. Due to these considerations, this paper proposes a novel medical image segmentation model based on a hybrid multi-layer perception (MLP) network by combining the multi-scale hybrid MLP with a CNN based on the UNeXt model, namely, the LM-UNet model. This model can effectively enhance the connection between local and global information and strengthen the fusion between feature information. Experiments on multiple datasets reveal significantly improved segmentation performance of the LM-UNet model on the international skin imaging collaboration (ISIC) 2018 dataset manifested as an average Dice coefficient of 92.58% and an average intersection over union (IoU) coefficient of 86.52%, which are 3% and 3.5% higher than those of the UNeXt model, respectively. The segmentation effects of the proposed model on the osteoarthritis initiative-zuse institute Berlin two-dimensional (OAI-ZIB 2D) and the breast ultrasound image (BUSI) datasets are also substantially superior, represented as average Dice coefficients 2.5% and 1.0% higher than those of the UNeXt counterpart, respectively. In summary, the LM-UNet model not only improves the accuracy of medical image segmentation but also provides better generalization performance.