心脏图像分析

(1)心脏病是全球范围内患病率和致死率最高的疾病之一，其临床诊断依赖于对心脏结构参数和局部运动行为的精确量化分析。然而在实际临床实践中，从心脏影像（心脏核磁共振/心超）获取这些参数需要大量的人工标注和观察，极大降低了医师对心脏病的诊断效率。 本研究旨在通过机器学习特别是深度学习的方法，开发出稳定、可靠的自动参数量化与运动评估方法，辅助医师进行诊断。

(2)相关研究老师：薛武峰

(3)相关文章：

[1]Wufeng Xue, Gary Brahm, Sachin Pandey, Stephanie Leung, and Shuo Li. Full left ventricle quantification via deep multitask relationships learning. Medical Image Analysis, 43:54–65, Jan. 2018.

[2]Wufeng Xue, Ali Islam, Mousumi Bhaduri, and Shuo Li. Direct multitype cardiac indices estimation via joint representation and regression learning. IEEE Transactions on Medical Imaging, 36(10):2057–2067, 2017.

[3]Wufeng Xue, Ilanit Ben Nachum, Sachin Pandey, James Warrington, Stephanie Leung, and Shuo Li. Direct estimation of regional wall thicknesses via residual recurrent neural network. In IPMI, 2017. (Oral)

[4]Wufeng Xue, Andrea Lum, Ashley Mercado, Mark Landis, James Warringto, and Shuo Li. Full quantification of left ventricle via deep multitask learning network respecting intra-and inter-task relatedness. MICCAI, 2017. (Oral)