Intelligent contour extraction approach for accurate segmentation of medical ultrasound images - Peng, Tao; Wu, Yiyun; Gu, Yidong; Xu, Daqiang; Wang, Caishan; Li, Quan; Cai, Jing (Frontiers in Physiology, 2023)
Accurate contour extraction in ultrasound images is a problematic issue owing to the missing or ambiguous outline between organs (i.e., prostate and kidney) and surrounding tissues, the appearance of shadow artifacts, and the large variability in the shape of organs. To address these issues, we devised a method that includes four stages. In the first stage, the data sequence is acquired using an improved adaptive selection principal curve method, in which a limited number of radiologistdefined data points are adopted as the prior. The second stage then uses an enhanced quantum evolution network to help acquire the optimal neural network. The third stage involves increasing the precision of the experimental outcomes after training the neural network, while using the data sequence as the input. In the final stage, the contour is smoothed using an explicable mathematical formula explained by the model parameters of the neural network. Our experiments showed that our approach outperformed other current methods, including hybrid and Transformer-based deeplearning methods, achieving an average Dice similarity coefficient, Jaccard similarity coefficient, and accuracy of 95.7 ± 2.4%, 94.6 ± 2.6%, and 95.3 ± 2.6%, respectively.