With rapid developments in convolutional neural networks for image processing, deep learning methods based on pixel classification have been extensively applied in medical image segmentation. One popular strategy for such tasks is the encoder-decoder-based U-Net architecture and its variants. Most segmentation methods based on fully convolutional networks will cause the loss of spatial and contextual information due to continuous pooling operations or strided convolution when decreasing image resolution, and make less use of contextual information and global information under different receptive fields. To overcome this shortcoming, this paper proposes a novel structure called RAAU-Net. In our proposed RAAU-Net structure, which is a modified U-shaped architecture, we aim to capture high-level information while preserving spatial information and focusing on the regions of interest. RAAU-Net comprises three main components: a feature encoder module that utilizes a pre-trained ResNet-18 model as a fixed feature extractor, a multi-receptive field extraction module that we developed, and a feature decoder module. We have tested our method on several 2D medical image segmentation tasks such as retinal nerve, breast tumor, skin lesion, lung, gland, and polyp segmentation. All the indexes of the model reached the best in the dataset of skin lesions, in which Accuracy, Precision, IoU, Recall, and Dice Score were 3.26%, 5.42%, 9.92%, 6.52%, and 5.95% higher than UNet.

中文翻译：

---

基于U型结构的二维医学图像分割

随着图像处理领域的卷积神经网络的快速发展，基于像素分类的深度学习方法在医学图像分割中得到了广泛的应用。此类任务的一种流行策略是基于编码器-解码器的 U-Net 架构及其变体。大多数基于全卷积网络的分割方法在降低图像分辨率时会因连续池化操作或跨步卷积而导致空间和上下文信息的丢失，并且较少利用不同感受野下的上下文信息和全局信息。为了克服这个缺点，本文提出了一种称为 RAAU-Net 的新颖结构。在我们提出的 RAAU-Net 结构中，它是一种改进的 U 形架构，我们的目标是捕获高级信息，同时保留空间信息并关注感兴趣的区域。RAAU-Net 包含三个主要组件：利用预训练的 ResNet-18 模型作为固定特征提取器的特征编码器模块、我们开发的多感受野提取模块和特征解码器模块。我们已经在多个 2D 医学图像分割任务上测试了我们的方法，例如视网膜神经、乳腺肿瘤、皮肤病变、肺、腺体和息肉分割。该模型各项指标均达到皮损数据集中最好，其中Accuracy、Precision、IoU、Recall、Dice Score比UNet分别提高了3.26%、5.42%、9.92%、6.52%、5.95%。