Synergistic Representation Learning for Pancreatic Image Analysis

Aim of the PhD Project:

In this project, we aim to develop novel machine learning approaches for segmentation and analysis of pancreatic images.
The project will enable robust and accurate characterisation of pancreatic volumes and shapes, providing quantitative imaging phenotypes for assessment of pancreatic anatomy and identification of pathological features.

Project Description:

Pancreatic cancer is the 6th most common cause of cancer deaths in the UK with a 5-year survival rate of only 5% [1]. However, if pancreatic cancer is diagnosed at an early stage when surgery is possible, the survival rate can go up to 20% [1], [2]. Early diagnosis of pancreatic cancer is challenging, mainly because symptoms only occur at a late stage and screening tools are still lacking. In this project, we investigate novel machine learning approaches for automated segmentation and analysis of pancreatic anatomy from medical images. It will provide an efficient tool for extracting quantitative image-based biomarkers and assisting clinicians in diagnosis and assessment of pancreatic diseases.

A number of methods have been proposed for pancreatic image segmentation in recent years. Some are atlas-based, relying on image registration for atlas propagation and then performing label fusion to create segmentation [3]. A disadvantage with atlas-based methods is that they are computationally expensive due to the cost of multiple image registrations. Most recent methods are deep learning-based, which train convolutional neural networks to learn the mapping from image to segmentation [4]-[10]. They are computationally faster due to the use of GPUs and the one-pass inference process.

State-of-the-art segmentation methods can achieve an average Dice overlap metric of 86.9% for normal pancreas [4]. However, for abnormal pancreas, the Dice metric can be as low as 38.4% [4]. This demonstrates the technical challenges in pancreatic image segmentation. The challenges are attributed to several factors. First, the pancreas is small compared to other abdominal organs, occupying only a small proportion of the 3D field-of-view. Neural networks are less sensitive to small objects due to the class imbalance problem. Second, the pancreas is highly variable in anatomical shape and appearance. Its anatomy is altered by ageing, which causes atrophy, lobulation and fatty degeneration. For pathological cases, the anatomy can also be significantly influenced by cysts and tumours. Third, the training of neural networks requires large datasets. Available training data with manual annotations are often limited in clinical scenarios.

To address these challenges, we propose a synergistic representation learning approach for pancreatic image segmentation to improve both the robustness and accuracy. The synergy will come from multiple aspects. 1) Synergy between scales: Multi-scale semantic information will be incorporated in a joint and coarse-to-fine fashion. 2) Synergy between image features and anatomical priors: Anatomical shape priors will be learnt to improve segmentation robustness. 3) Synergy between data: Fully-labelled (multi-organ annotation), partially-labelled (pancreas-only annotation) and unannotated data will be utilised for semi- and partially-supervised learning. 4) Synergy between modalities: Both CT and MR modalities will be explored for semantic feature learning. 5) Synergy between computer and human. Abnormal cases and hard examples will be detected for human to review and annotate to enable human-in-the-loop learning.

The output of the project will be an automated tool that can be applied to large-scale datasets for analysis of pancreatic imaging phenotypes. The expected candidate's background is engineering, computing or physical sciences.

Strains on the healthcare system in the UK create an acute need for finding more effective, efficient, safe, and accurate non-invasive imaging solutions for clinical decision-making, both in terms of diagnosis and prognosis, and to reduce unnecessary treatment procedures and associated costs. Medical imaging is currently undergoing a step-change facilitated through the advent of artificial intelligence (AI) techniques, in particular deep learning and statistical machine learning, the development of targeted molecular imaging probes and novel "push-button" imaging techniques. There is also the availability of low-cost imaging solutions, creating unique opportunities to improve sensitivity and specificity of treatment options leading to better patient outcome, improved clinical workflow and healthcare economics. However, a skills gap exists between these disciplines which this CDT is aiming to fill.

Consistent with our vision for the CDT in Smart Medical Imaging to train the next generation of medical imaging scientists, we will engage with the key beneficiaries of the CDT: (1) PhD students & their supervisors; (2) patient groups & their carers; (3) clinicians & healthcare providers; (4) healthcare industries; and (5) the general public. We have identified the following areas of impact resulting from the operation of the CDT.

- Academic Impact: The proposed multidisciplinary training and skills development are designed to lead to an appreciation of clinical translation of technology and generating pathways to impact in the healthcare system. Impact will be measured in terms of our students' generation of knowledge, such as their research outputs, conference presentations, awards, software, patents, as well as successful career destinations to a wide range of sectors; as well as newly stimulated academic collaborations, and the positive effect these will have on their supervisors, their career progression and added value to their research group, and the universities as a whole in attracting new academic talent at all career levels.

- Economic Impact: Our students will have high employability in a wide range of sectors thanks to their broad interdisciplinary training, transferable skills sets and exposure to industry, international labs, and the hospital environment. Healthcare providers (e.g. the NHS) will gain access to new technologies that are more precise and cost-efficient, reducing patient treatment and monitoring costs. Relevant healthcare industries (from major companies to SMEs) will benefit and ultimately profit from collaborative research with high emphasis on clinical translation and validation, and from a unique cohort of newly skilled and multidisciplinary researchers who value and understand the role of industry in developing and applying novel imaging technologies to the entire patient pathway.

- Societal Impact: Patients and their professional carers will be the ultimate beneficiaries of the new imaging technologies created by our students, and by the emerging cohort of graduated medical imaging scientists and engineers who will have a strong emphasis on patient healthcare. This will have significant societal impact in terms of health and quality of life. Clinicians will benefit from new technologies aimed at enabling more robust, accurate, and precise diagnoses, treatment and follow-up monitoring. The general public will benefit from learning about new, cutting-edge medical imaging technology, and new talent will be drawn into STEM(M) professions as a consequence, further filling the current skills gap between healthcare provision and engineering.

We have developed detailed pathways to impact activities, coordinated by a dedicated Impact & Engagement Manager, that include impact training provision, translational activities with clinicians and patient groups, industry cooperation and entrepreneurship training, international collaboration and networks, and engagement with the General Public.