Purpose

Using computer-assisted means to process a large amount of heart image data in order to speed up the diagnosis efficiency and accuracy of medical doctors has become a research worthy of investigation.

Method

Based on the U-Net model, this paper proposes a multi-input fusion network (MIFNet) model based on multi-scale input and feature fusion, which automatically extracts and fuses features of different input scales to realize the detection of Cardiac Magnetic Resonance Images (CMRI). The MIFNet model is trained and verified on the public data set, and then compared with the segmentation models, namely the Fully Convolutional Network (FCN) and DeepLab v1.

Results

MIFNet model segmentation of CMRI significantly improved the segmentation accuracy, and the Dice value reached 97.238%. Compared with FCN and DeepLab v1, the average Hausdorff distance (HD) was reduced by 16.425%. The capacity parameter of FCN is 124.86% of MIFNet, DeepLab v1 is 103.22% of MIFNet.

Conclusion

Our proposed MIFNet model reduces the amount of parameters and improves the training speed while ensuring the simultaneous segmentation of overlapping targets. It can help clinicians to more quickly check the patient's CMRI focus area, and thereby improving the efficiency of diagnosis.

中文翻译：

---

基于多输入融合网络的心脏磁共振图像自动分割

目的

利用计算机辅助手段对大量心脏图像数据进行处理，以提高医生的诊断效率和准确性，已成为一项值得研究的研究。

方法

基于U-Net模型，本文提出了一种基于多尺度输入和特征融合的多输入融合网络（MIFNet）模型，自动提取和融合不同输入尺度的特征，实现心脏磁共振图像的检测(CMRI)。MIFNet 模型在公共数据集上进行训练和验证，然后与分割模型，即全卷积网络 (FCN) 和 DeepLab v1.0 进行比较。

结果

CMRI的MIFNet模型分割显着提高了分割精度，Dice值达到了97.238%。与FCN和DeepLab v1相比，平均Hausdorff距离（HD）降低了16.425%。FCN 的容量参数是 MIFNet 的 124.86%，DeepLab v1 是 MIFNet 的 103.22%。

结论

我们提出的 MIFNet 模型减少了参数量并提高了训练速度，同时确保了重叠目标的同时分割。它可以帮助临床医生更快速地检查患者的CMRI焦点区域，从而提高诊断效率。