Longitudinal Prostate MR Image Registration for Patients in Active Surveillance

1)简要说明研究的背景，包括潜在的影响前列腺癌是西方世界许多地区最常见的男性癌症之一。主动监测作为最常见的观察策略之一，被用来帮助监测低风险前列腺癌患者的肿瘤进展情况，并决定接受治疗的时间点。挑战来自于接受标准、决策、确定随访的时间间隔和患者的短期和长期结果的不确定性。近年来，随着越来越多的患者被纳入主动监测项目，深度学习技术被认为是帮助医生应对这些挑战的手段。这既可以将医生从繁重的医学图像解释工作中解脱出来，也可以帮助他们做出更好的决策。2)目的和目标-具体目标是：1.对主动监测项目进行回顾性研究，建立基于深度学习的模型，帮助医生观察肿瘤的进展情况，并做出治疗策略或决策。探索本课题中的一种基本方法&纵向前列腺图像配准方法。该项目推动的探索包括但不限于，例如，研究患者间/患者内和单/多模式磁共振图像配准的分布对配准的影响。3)研究方法的新颖性该方法专注于以弱监督方式进行深度学习的纵向磁共振图像配准，该方法可以在一系列后续访问中学习图像的形态变化。这些形态变化可以被量化并用作区分肿瘤进展状态的一个因素，这可以用于其他下游任务。4)与EPSRC的战略和研究领域保持一致医疗技术：人工智能技术/医学成像5)任何公司或合作者都不参与。

1) Brief description of the context of the research including potential impactProstate cancer is one of the most diagnosed cancer in male in many parts of the western world. Asone of the most common observation strategies, active surveillance is used to help monitor the lowrisky prostate cancer patients on the progression of the tumor and to decide the time point to receivethe treatment. Challenges come from the uncertainties on accept criterion, decision making, determinethe time interval for follow-up visits and the short- and long-term outcomes of the patients. With theincreasing number of patients who were added into active surveillance project in recent years, deeplearning technologies are considered to use for helping the doctors face these challenges. Which mayboth relief the doctors form laborious medical image interpreting works and help make betterdecisions.2) Aims and Objectives-The specific objectives are to:1. Perform a retrospective study on an active surveillance project to build a deep learning basedmodel for helping the doctors observe the progression condition of the tumors and make treatmentstrategies or decisions.2. Exploring the method of longitudinal prostate image registration, which is a principle method inthis project. The project drives the explorations including but not limited to, for example, theresearch on the influence of distribution of the inter-/intra-patient and single/multi-modality MRimage registration.3) Novelty of Research MethodologyThe method is concentrating on the registration of the longitudinal magnetic resonance images withdeep learning in a weakly supervised manner, which could learn the morphological changes of theimages in a sequence of follow-up visits. The morphological changes could be quantified and be usedas a factor for discriminate the progression status of the tumor, which could be used in other downstreamingtasks.4) Alignment to EPSRC's strategies and research areasHealthcare Technologies: Artificial Intelligence Technologies / Medical Imaging5) Any companies or collaborators involvedNone.