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

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.