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

用于肺部疾病评估的呼吸成像数据的定量图像分析。

• 批准号: MR/S004092/1
• 负责人: Bartlomiej Papiez
• 金额: $ 33.96万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FS004092%2F1
• 关键词: Towards; quantitative; image; analysis; respiratory

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.

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。

项目成果

Head and Neck Tumor Segmentation and Outcome Prediction - Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings

头颈肿瘤分割和结果预测 - 第二次挑战赛，HECKTOR 2021，与 MICCAI 2021 同期举行，法国斯特拉斯堡，2021 年 9 月 27 日，会议记录

• DOI: 10.1007/978-3-030-98253-9_24
• 发表时间: 2022
• 作者: Juanco-Müller Á

Medical Image Understanding and Analysis - 25th Annual Conference, MIUA 2021, Oxford, United Kingdom, July 12-14, 2021, Proceedings

医学图像理解与分析 - 第 25 届年会，MIUA 2021，英国牛津，2021 年 7 月 12-14 日，会议记录

• DOI: 10.1007/978-3-030-80432-9_17
• 发表时间: 2021
• 作者: Bagur A

MOCOnet: Robust Motion Correction of Cardiovascular Magnetic Resonance T1 Mapping Using Convolutional Neural Networks.

• DOI: 10.3389/fcvm.2021.768245
• 发表时间: 2021
• 期刊: Frontiers in cardiovascular medicine
• 影响因子: 3.6
• 作者: Gonzales RA;Zhang Q;Papież BW;Werys K;Lukaschuk E;Popescu IA;Burrage MK;Shanmuganathan M;Ferreira VM;Piechnik SK
• 通讯作者: Piechnik SK

Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis - First International Workshop, DATRA 2018 and Third International Workshop, PIPPI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings

数据驱动的治疗反应评估和早产儿、围产期和儿科图像分析 - 第一届国际研讨会，DATRA 2018 和第三届国际研讨会，PIPPI 2018，与 MICCAI 2018 联合举行，西班牙格拉纳达，2018 年 9 月 16 日，会议记录

• DOI: 10.1007/978-3-030-00807-9_8
• 发表时间: 2018
• 作者: Namburete A