Affiliation:
1. School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macau 999078, China
Abstract
Deep learning technology has achieved breakthrough research results in the fields of medical computer vision and image processing. Generative adversarial networks (GANs) have demonstrated a capacity for image generation and expression ability. This paper proposes a new method called MWG-UNet (multiple tasking Wasserstein generative adversarial network U-shape network) as a lung field and heart segmentation model, which takes advantages of the attention mechanism to enhance the segmentation accuracy of the generator so as to improve the performance. In particular, the Dice similarity, precision, and F1 score of the proposed method outperform other models, reaching 95.28%, 96.41%, and 95.90%, respectively, and the specificity surpasses the sub-optimal models by 0.28%, 0.90%, 0.24%, and 0.90%. However, the value of the IoU is inferior to the optimal model by 0.69%. The results show the proposed method has considerable ability in lung field segmentation. Our multi-organ segmentation results for the heart achieve Dice similarity and IoU values of 71.16% and 74.56%. The segmentation results on lung fields achieve Dice similarity and IoU values of 85.18% and 81.36%.
Funder
Macau Science and Technology Development Fund
Reference37 articles.
1. Understanding cities with machine eyes: A review of deep computer vision in urban analytics;Ibrahim;Cities,2020
2. Machine learning for medical imaging;Erickson;Radiographics,2017
3. Krizhevsky, A., Sutskever, I., and Hinton, G. (2012, January 3–8). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Neural Information Processing Systems, Harrahs and Harveys, Lake Tahoe, CA, USA.
4. Lauw, H., Wong, R.W., Ntoulas, A., Lim, E.P., Ng, S.K., and Pan, S. (2020). Advances in Knowledge Discovery and Data Mining, Springer.
5. GANs for medical image analysis;Kazeminia;Artif. Intell. Med.,2020
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献