Proceedings Article | 1 August 2023
KEYWORDS: Image segmentation, Image processing algorithms and systems, Feature extraction, Diagnostics, Echocardiography, Statistical analysis, Heart, Data modeling
Echocardiography segmentation is important to quantify cardiac function. Although the segmentation algorithms have achieved considerable performance in image segmentation, the significance of those state-of-the-art (SOTA) segmentation algorithms for clinical diagnosis is not clear. Therefore, this paper comprehensively discussed the significance of the SOTA segmentation algorithm for clinical diagnosis by using segmentation evaluation metrics and diagnostic evaluation metrics. Firstly, four different SOTA segmentation algorithms were used to segment the 2D echocardiography, and the segmentation results were evaluated by using the Dice similarity coefficient (DSC) and Hausdorff distance (HD) which were defined as the segmentation evaluation metrics). Secondly, on the basis of the segmented areas, four diagnose-related image features were extracted, including lower wall thickness (LWT), front wall thickness (FWT), left ventricular transverse diameter (LVTD), and left atrial thickness (LAT). Then, the correlation and difference of those features extracted from the segmented areas and ground truth were statistically analyzed, which were defined as diagnostic evaluation metrics. The results showed that SwinUNet model had the best segmentation results in terms of segmentation evaluation metrics. Among them, SwinUNet's mHD value reached (16.95±1.38)mm and its mDSC value reached (90.16±2.78)%. However, for diagnostic evaluation metrics, there was a significant difference in the LVTD (P<0.05) and LWT (P<0.05) extracted by SwinUNet segmented areas compared with the ground truth. In contrast, there was no significant difference in the LVTD (P=0.026) and FWT (P=0.912) extracted by TransUNet segmented areas. Similarly, LAT (P=0.959) and LWT (P=0.508) from ScaleFormer segmented areas also correlated with those from the ground truth. Four diagnose-related image features (LVTD, LAT, LWT, FWT) extracted from TransUNet segmented areas had the highest correlation with those from the ground truth, and their Pearson correlation coefficient reached 0.95, 0.86, 0.66 and 0.71, respectively. The experimental results showed that the segmentation algorithm with higher segmentation evaluation scores was not necessarily suitable for clinical diagnosis.
