Towards quantitative image analysis of respiratory imaging data for pulmonary disease assessment.

拟议的研究包括新一代的计算算法和癌症图像分析的应用研究，这不仅会影响医学成像领域，而且主要有助于对癌症，治疗计划和反应评估的重要理解。虽然这里的主要重点是肺肿瘤，但正在探索的许多概念具有超越的适用性（例如肝脏疾病）。开发的计算算法将能够自动学习，建立在现有数据的基础上，但弱监督（来自我们的先验知识或必要时的人类互动）也将可用于提供生物医学上合理的结果。因此，预计新一代算法也可以应用于其他工程和数学领域，例如在计算建模中使用成像。

The proposed research encompasses new generation of computational algorithms and applied research in cancer image analysis that will impact not only the medical imaging field, but primarily contribute toward a significant understanding of cancer, treatment planning and response assessment. While the main focus here is on lung tumours, many of the concepts being explored have applicability beyond (e.g. liver diseases). The developed computational algorithms will be capable of learning automatically, building on available data, but weak-supervision (either coming from our a priori knowledge or human interaction when necessary) will be also available to provide biomedically plausible outcomes. Therefore, it is expected that the new generation algorithms can also be applied in other fields of engineering and mathematics that make use of imaging for instance in computational modelling.