Background

Automated segmentation methods for cardiac magnetic resonance imaging (MRI) offer valuable assistance in evaluating cardiac function for clinical diagnosis. Nevertheless, prevailing techniques encounter challenges in dealing with characteristics like indistinct image boundaries and uneven resolution in cardiac MRI scans. Consequently, these methods often encounter problems related to uncertainty within the same class of structures and ambiguity when distinguishing between different classes.

Method

ology: In our paper, enhancements are made to the U-Net model to address these challenges. Initially, an enhanced residual block is incorporated into the U-Net architecture to increase network depth and capture richer feature information. Subsequently, by integrating the Convolutional Block Attention Module (CBAM) mechanism, the network focuses more intensely on specific feature layers and spatial regions. This leads to the suppression of non-target region features, consequently enhancing segmentation accuracy.

Results

This study examined an enhanced model's performance using a collection of cardiac MRI data from the Straits Affiliated Hospital of Huaqiao University. The dataset encompassed 6680 images for training and 2225 images for testing. Model evaluation was conducted using the Dice coefficient and accuracy metrics, yielding values of 0.9292 and 0.8911, respectively. The outcomes indicate that the enhanced model effectively enhances the precision and accuracy of cardiac magnetic resonance imaging segmentation.

Conclusion

The method put forth in this study brings about a substantial enhancement in segmentation accuracy, resulting in a segmentation outcome that aligns closely with the reference ground truth labels. In comparison to alternative algorithms, this approach demonstrates elevated accuracy across distinct regions, thereby yielding segmentation results of heightened precision.

中文翻译：

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基于心脏磁共振成像的改进U-Net模型和卷积块注意力模块的图像分割

背景

心脏磁共振成像（MRI）的自动分割方法为临床诊断评估心脏功能提供了宝贵的帮助。然而，流行的技术在处理心脏 MRI 扫描中图像边界模糊和分辨率不均匀等特征时遇到了挑战。因此，这些方法经常遇到与同一类结构内的不确定性和区分不同类时的模糊性相关的问题。

方法

ology：在我们的论文中，对 U-Net 模型进行了增强以应对这些挑战。最初，U-Net 架构中融入了增强型残差块，以增加网络深度并捕获更丰富的特征信息。随后，通过集成卷积块注意力模块（CBAM）机制，网络更加关注特定的特征层和空间区域。这导致非目标区域特征的抑制，从而提高分割精度。

结果

本研究使用华侨大学海峡附属医院收集的心脏 MRI 数据检查了增强模型的性能。该数据集包含 6680 张用于训练的图像和 2225 张用于测试的图像。使用 Dice 系数和准确度指标进行模型评估，得出的值分别为 0.9292 和 0.8911。结果表明，增强模型有效提高了心脏磁共振成像分割的精度和准确度。

结论

本研究中提出的方法大大提高了分割精度，从而产生与参考地面真实标签紧密一致的分割结果。与其他算法相比，这种方法证明了不同区域的准确性更高，从而产生了更高精度的分割结果。