Abstract
In medical imaging, accurate image segmentation is crucial for quantifying diseases, assessing prognosis, and evaluating treatment outcomes. However, existing methods lack an in-depth integration of global and local features, failing to pay special attention to abnormal regions and boundary details in medical images. To this end, we present a novel deep learning-based approach, MIPC-Net, for precise boundary segmentation in medical images. Our approach, inspired by radiologists' working patterns, features two distinct modules: (i) \textbf{Mutual Inclusion of Position and Channel Attention (MIPC) module}: To enhance the precision of boundary segmentation in medical images, we introduce the MIPC module, which enhances the focus on channel information when extracting position features and vice versa; (ii) \textbf{GL-MIPC-Residue}: To improve the restoration of medical images, we propose the GL-MIPC-Residue, a global residual connection that enhances the integration of the encoder and decoder by filtering out invalid information and restoring the most effective information lost during the feature extraction process. We evaluate the performance of the proposed model using metrics such as Dice coefficient (DSC) and Hausdorff Distance (HD) on three publicly accessible datasets: Synapse, ISIC2018-Task, and Segpc. Our ablation study shows that each module contributes to improving the quality of segmentation results. Furthermore, with the assistance of both modules, our approach outperforms state-of-the-art methods across all metrics on the benchmark datasets, notably achieving a 2.23mm reduction in HD on the Synapse dataset, strongly evidencing our model's enhanced capability for precise image boundary segmentation. Codes will be available at this https URL.
Abstract (translated)
在医学影像学中，准确的图像分割对于评估疾病、预测预后和评估治疗效果至关重要。然而，现有的方法缺乏对全局和局部特征的深入整合，未能特别关注医学图像中的异常区域和边界细节。为此，我们提出了一个基于深度学习的新的精确边界分割方法，称为MIPC-Net。 我们的方法受到放射科医生工作模式的启发，包括两个不同的模块：（i） mutual inclusion of position and channel attention (MIPC）模块：为了提高医学图像边界分割的精度，我们引入了MIPC模块，在提取位置特征时增强了通道信息的关注；反之亦然；（ii） GL-MIPC-Residue：为了提高医学图像的恢复效果，我们提出了GL-MIPC-Residue，一个全局残留连接，通过滤除无效信息并恢复在特征提取过程中丢失的有效信息来增强编码器和解码器的整合。 我们使用Synapse、ISIC2018-Task和Segpc三个公开可用的数据集对所提出的模型进行评估。我们的消融研究结果表明，每个模块都促进了分割结果的质量提升。此外，在基准数据集上，通过两个模块的辅助，我们的方法在所有指标上都超越了最先进的方法，特别是在Synapse数据集上实现了2.23mm的HD减少，充分证明了我们的模型在精确图像边界分割方面的增强能力。代码将在此链接处可用。
URL
https://arxiv.org/abs/2404.08201
