Zhang Shuai, Niu Yanmin
School of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China.
Bioengineering (Basel). 2023 Jun 12;10(6):712. doi: 10.3390/bioengineering10060712.
In recent years, UNet and its improved variants have become the main methods for medical image segmentation. Although these models have achieved excellent results in segmentation accuracy, their large number of network parameters and high computational complexity make it difficult to achieve medical image segmentation in real-time therapy and diagnosis rapidly. To address this problem, we introduce a lightweight medical image segmentation network (LcmUNet) based on CNN and MLP. We designed LcmUNet's structure in terms of model performance, parameters, and computational complexity. The first three layers are convolutional layers, and the last two layers are MLP layers. In the convolution part, we propose an LDA module that combines asymmetric convolution, depth-wise separable convolution, and an attention mechanism to reduce the number of network parameters while maintaining a strong feature-extraction capability. In the MLP part, we propose an LMLP module that helps enhance contextual information while focusing on local information and improves segmentation accuracy while maintaining high inference speed. This network also covers skip connections between the encoder and decoder at various levels. Our network achieves real-time segmentation results accurately in extensive experiments. With only 1.49 million model parameters and without pre-training, LcmUNet demonstrated impressive performance on different datasets. On the ISIC2018 dataset, it achieved an IoU of 85.19%, 92.07% recall, and 92.99% precision. On the BUSI dataset, it achieved an IoU of 63.99%, 79.96% recall, and 76.69% precision. Lastly, on the Kvasir-SEG dataset, LcmUNet achieved an IoU of 81.89%, 88.93% recall, and 91.79% precision.
近年来，U-Net及其改进变体已成为医学图像分割的主要方法。尽管这些模型在分割精度方面取得了优异的成果，但其大量的网络参数和高计算复杂度使得难以在实时治疗和诊断中快速实现医学图像分割。为了解决这个问题，我们引入了一种基于卷积神经网络（CNN）和多层感知器（MLP）的轻量级医学图像分割网络（LcmUNet）。我们从模型性能、参数和计算复杂度方面设计了LcmUNet的结构。前三层是卷积层，后两层是MLP层。在卷积部分，我们提出了一种LDA模块，它结合了非对称卷积、深度可分离卷积和注意力机制，以减少网络参数数量，同时保持强大的特征提取能力。在MLP部分，我们提出了一种LMLP模块，它有助于在关注局部信息的同时增强上下文信息，并在保持高推理速度的同时提高分割精度。该网络还涵盖了编码器和解码器在各个层次之间的跳跃连接。我们的网络在大量实验中准确地实现了实时分割结果。LcmUNet只有149万个模型参数，且无需预训练，在不同数据集上都表现出了令人印象深刻的性能。在ISIC2018数据集上，它的交并比（IoU）为85.19%，召回率为92.07%，精确率为92.99%。在BUSI数据集上，它的IoU为63.99%，召回率为79.96%，精确率为76.69%。最后，在Kvasir-SEG数据集上，LcmUNet的IoU为81.89%，召回率为88.93%，精确率为91.79%。
