Artificial Intelligence with Human In The Loop for Automated Medical Image Contouring in Precision Oncology

正电子发射断层扫描(PET)以放射性摄取的形式提供局部体内生物和生物化学的定量图像，而计算机断层扫描(CT)提供详细的解剖图像。PET-CT检查在临床肿瘤学中对大多数癌症的诊断、分期和重新分期、监测治疗反应和计划放射治疗是至关重要的。用PET-CT成像的临床前模型也形成了发现新的癌症治疗方法的基本工具。为了对PET-CT图像进行定量评估，需要绘制感兴趣区域的轮廓/分割。这可以是临床研究中癌症病变的轮廓，放射治疗计划中的危险器官轮廓，以及临床前模型中生物分布研究的全身器官轮廓。自动分割PET-CT图像的能力是一个悬而未决的问题。在临床前和临床模型中开发的图像分析方法是可以互换的；两个领域之间的知识转移是共同的。人工识别和分割感兴趣的PET-CT体积的方法很容易出错。绘制全身临床前模型的轮廓可能需要几个小时。使用临床前模型开发分割算法的优势是可以获得基本的解剖结构。该项目将建立在内部开发的PET-CT成像自动分割工具的基础上，该工具利用深度学习(DL)人工智能(AI)。学生将在不同的临床前CT图像中评估模型的准确性，这些图像显示解剖和放射性药物摄取的变化；探索使用人类环路互动来提高准确性，并开发将开发的模型的知识转移到临床数据集的方法。

Positron Emission Tomography (PET) yields quantitative images of regional in-vivo biology and biochemistry in the form of radioactive uptake, whilst Computed Tomography (CT) provides detailed anatomical images of anatomy. PET-CT investigations are of paramount importance in clinical oncology for diagnosing, staging and re-staging most cancers, monitoring response to therapy and planning radiotherapy treatment. Preclinical models imaged with PET-CT also form essential tools for the discovery of novel cancer therapeutics. To make quantitative assessments of PET-CT images contouring/segmentation of regions of interest are required. This can be contouring of cancerous lesions in clinical studies organs at risk during radiotherapy treatment planning and whole body organ wise contouring for biodistribution studies in the pre-clinical model. The ability to automatically segment PET-CT images is an open problem. Methods for image analysis developed in pre-clinical and clinical models are interchangeable; with knowledge transfer between the two domains common place. Manual approaches to identify and segment PET-CT volumes of interest are error prone. The contouring of a whole body pre-clinical model can take several hours. The advantage of using a pre-clinical model to develop segmentation algorithms is the access to the ground truth anatomy. This project will build on an in-house developed automated segmentation tool for PET-CT imaging which utilizes deep learning (DL) artificial intelligence (AI). The student will assess the accuracy of the model in varying pre-clinical CT images which demonstrate variations in anatomy and radiopharmaceutical uptake; explore the use of human in the loop interactions to enhance accuracy and develop methods for knowledge transfer of the developed model to clinical datasets.