The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Zhejiang University of Technology, Hangzhou, China.
J Appl Clin Med Phys. 2024 Oct;25(10):e14483. doi: 10.1002/acm2.14483. Epub 2024 Aug 12.
In recent years, the use of deep learning for medical image segmentation has become a popular trend, but its development also faces some challenges. Firstly, due to the specialized nature of medical data, precise annotation is time-consuming and labor-intensive. Training neural networks effectively with limited labeled data is a significant challenge in medical image analysis. Secondly, convolutional neural networks commonly used for medical image segmentation research often focus on local features in images. However, the recognition of complex anatomical structures or irregular lesions often requires the assistance of both local and global information, which has led to a bottleneck in its development. Addressing these two issues, in this paper, we propose a novel network architecture.
We integrate a shift window mechanism to learn more comprehensive semantic information and employ a semi-supervised learning strategy by incorporating a flexible amount of unlabeled data. Specifically, a typical U-shaped encoder-decoder structure is applied to obtain rich feature maps. Each encoder is designed as a dual-branch structure, containing Swin modules equipped with windows of different size to capture features of multiple scales. To effectively utilize unlabeled data, a level set function is introduced to establish consistency between the function regression and pixel classification.
We conducted experiments on the COVID-19 CT dataset and DRIVE dataset and compared our approach with various semi-supervised and fully supervised learning models. On the COVID-19 CT dataset, we achieved a segmentation accuracy of up to 74.56%. Our segmentation accuracy on the DRIVE dataset was 79.79%.
The results demonstrate the outstanding performance of our method on several commonly used evaluation metrics. The high segmentation accuracy of our model demonstrates that utilizing Swin modules with different window sizes can enhance the feature extraction capability of the model, and the level set function can enable semi-supervised models to more effectively utilize unlabeled data. This provides meaningful insights for the application of deep learning in medical image segmentation. Our code will be released once the manuscript is accepted for publication.
近年来，深度学习在医学图像分割中的应用已成为一种趋势，但它的发展也面临一些挑战。首先，由于医学数据的专业性，精确的标注既费时又费力。在医学图像分析中，如何利用有限的带标注数据有效地训练神经网络是一个重大挑战。其次，常用于医学图像分割研究的卷积神经网络通常侧重于图像中的局部特征。然而，对于复杂的解剖结构或不规则病变的识别，往往需要局部和全局信息的共同辅助，这导致了其发展的瓶颈。针对这两个问题，本文提出了一种新的网络架构。
我们集成了一个滑动窗口机制来学习更全面的语义信息，并采用了一种半监督学习策略，结合了灵活数量的未标注数据。具体来说，我们应用了一个典型的 U 型编码器-解码器结构来获得丰富的特征图。每个编码器都设计为双分支结构，包含 Swin 模块，配备不同大小的窗口，以捕获多个尺度的特征。为了有效地利用未标注数据，引入了水平集函数来建立函数回归和像素分类之间的一致性。
我们在 COVID-19 CT 数据集和 DRIVE 数据集上进行了实验，并将我们的方法与各种半监督和全监督学习模型进行了比较。在 COVID-19 CT 数据集上，我们的分割精度高达 74.56%。在 DRIVE 数据集上，我们的分割精度为 79.79%。
结果表明，我们的方法在几个常用的评估指标上表现出色。我们的模型具有较高的分割精度，这表明使用不同大小窗口的 Swin 模块可以增强模型的特征提取能力，而水平集函数可以使半监督模型更有效地利用未标注数据。这为深度学习在医学图像分割中的应用提供了有意义的见解。我们的代码将在论文被接受后发布。
