SwinPA-Net: Swin Transformer-Based Multiscale Feature Pyramid Aggregation Network for Medical Image Segmentation
SwinPA-Net: Swin Transformer-Based Multiscale Feature Pyramid Aggregation Network for Medical Image Segmentation
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DOI：
10.1109/tnnls.2022.3204090
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发表时间：
2022-09-19
影响因子：
10.4
通讯作者：
Meijering, Erik
中科院分区：
文献类型：
--
作者：
Du, Hao;Wang, Jiazheng;Meijering, Erik
医学图像的精确分割是病理学研究和临床实践中的关键问题之一。然而，许多医学图像分割任务存在不同类型的病灶之间差异大、形状相似以及病灶与周围组织之间颜色等问题，严重影响了分割精度的提高。在这篇文章中，一种新的方法称为Swin金字塔聚合网络（SwinPA-Net），通过结合两个设计的模块与Swin Transformer学习更强大和鲁棒的功能。提出了两个模块，即密集乘性连接（DMC）模块和局部金字塔注意（LPA）模块，用于聚合医学图像的多尺度上下文信息。DMC模块通过稠密乘性特征融合级联多尺度语义特征信息，最大限度地减少浅层背景噪声的干扰，改善特征表达，解决病变大小和类型变化过大的问题。此外，LPA模块通过融合全局注意力和局部注意力来引导网络聚焦于感兴趣区域，这有助于解决类似问题。建议的网络进行评估的两个公共基准数据集的息肉分割任务和皮肤病变分割任务，以及腹腔镜图像分割任务的临床私人数据集。与现有的最先进的（SOTA）方法相比，SwinPA-Net实现了最先进的性能，并且在三个任务上的平均Dice得分分别超过第二好的方法1.68%，0.8%和1.2%。
The precise segmentation of medical images is one of the key challenges in pathology research and clinical practice. However, many medical image segmentation tasks have problems such as large differences between different types of lesions and similar shapes as well as colors between lesions and surrounding tissues, which seriously affects the improvement of segmentation accuracy. In this article, a novel method called Swin Pyramid Aggregation network (SwinPA-Net) is proposed by combining two designed modules with Swin Transformer to learn more powerful and robust features. The two modules, named dense multiplicative connection (DMC) module and local pyramid attention (LPA) module, are proposed to aggregate the multiscale context information of medical images. The DMC module cascades the multiscale semantic feature information through dense multiplicative feature fusion, which minimizes the interference of shallow background noise to improve the feature expression and solves the problem of excessive variation in lesion size and type. Moreover, the LPA module guides the network to focus on the region of interest by merging the global attention and the local attention, which helps to solve similar problems. The proposed network is evaluated on two public benchmark datasets for polyp segmentation task and skin lesion segmentation task as well as a clinical private dataset for laparoscopic image segmentation task. Compared with existing state-of-the-art (SOTA) methods, the SwinPA-Net achieves the most advanced performance and can outperform the second-best method on the mean Dice score by 1.68%, 0.8%, and 1.2% on the three tasks, respectively.