The Dynamic U-shaped Convolutional Network (DUCNet) achieved an average Dice coefficient of 80.68% for mouse cardiac slice image segmentation, outperforming the best existing algorithm by 1.7%.
A novel dynamic U-shaped convolutional network improves the automated segmentation and quantification of myocardial infarction in mouse cardiac slice images.
Absolute Event Rate: 80.68% vs 78.98%
Accurately segmenting and quantifying mouse cardiac slice images with myocardial infarction is of great significance in cardiovascular disease research. Manual methods are time-consuming, so automatic segmentation is highly sought after. However, due to the irregular U-shaped structure of infarcted and high-risk areas, the task is complex. This study proposes a dynamic U-shaped convolutional network that adapts to these irregular structures. We designed a dynamic convolution to focus on U-shaped local features, and developed a dual-stream fusion block to enhance performance. An attention gate mechanism suppresses irrelevant information, highlighting key features. We constructed a dataset of 243 mouse cardiac slice images with myocardial infarction. The proposed method outperforms other models, achieving an average Dice coefficient of 80.68%, 1.7% higher than the best existing algorithm. For infarct size segmentation, it reached 80.13%, surpassing the current optimal method by 2.43%. Additionally, our approach can quantify the ratio of infarcted to risk areas, aiding the assessment of myocardial injury severity. The dataset is available at https://github.com/wwz4416/mouse-cardiac-dataset .
Wang et al. (Thu,) conducted a other in Myocardial infarction (mouse model) (n=243). Dynamic U-shaped Convolutional Network (DUCNet) vs. Existing segmentation algorithms (e.g., DSCNet) was evaluated on Average Dice coefficient for cardiac slice segmentation. The Dynamic U-shaped Convolutional Network (DUCNet) achieved an average Dice coefficient of 80.68% for mouse cardiac slice image segmentation, outperforming the best existing algorithm by 1.7%.