Computer-aided systems for automatic assessment of scoliosis and spinal lesions

Back pain is the most common cause of long-term disability world-wide with significant social and economic repercussions. The basis of most back pain diagnoses is that the pain arises from degenerative changes in a specific structure of the spine or deformation of the spine.
Grading of the degree of degeneration and segmentation of spinal units manually by radiologists is laborious and lacks reproducibility due to inter- and intra-observer variation. Therefore, it is crucial to develop techniques to localise and segment lumbar spinal discs automatically. Several so-called Computer Aided Detection (CADe) and Computer Aided Diagnosis (CADx) techniques have been developed but they rely on strong supervision for their development. Moreover, recognition is challenging due to the similar intensity with surrounding tissues and differences in size, shape, intensity of discs and vertebral bodies.
Scoliosis is a common disease affecting 2-3% of the population worldwide. The Cobb angle is the standard measurement for assessing scoliosis. Precise measurement of Cobb angles is crucial to make diagnosis and propose appropriate treatments. A high inter and intra-variability in Cobb angles measurements undermines the validity of this method.
The first part of the PhD comprises developing robust and precise methods for automatic assessment of scoliosis from X-ray and MRI images. The second part of the PhD will include developing methods to automatically detect, segment and assess spinal lesions and cancerous tissues.
One of the objectives of the first part of the PhD is to develop a robust strategy to automatically measure the Cobb angles for diagnosis of scoliosis on X-ray images. We proposed a computer-aided system to automatically detect the centroids of each vertebra and compute the Cobb angles to tackle manual measurement uncertainty. Our model automatically calculated the Cobb angles after detection and extraction of centroid coordinates. Based on preliminary results, we were able to demonstrate the merits of our approach in terms of accuracy, reproducibility, and flexibility. Our computer-aided method benefits from being intuitive and reliable. It offers benefits for application in clinical settings to measure Cobb angles automatically and accurately. This will have a direct effect in facilitating the assessment of scoliosis and its severity on X-ray images.
It will be necessary in future work to create a more robust curve estimation method and extend the method to 3D MRI data. Further work is required to improve the model's ability to generalise. We want to show that this methodology improves the assessment of the progress of scoliosis. From the patient's perspective, the reduced X-ray dosage requirement is compelling as repeated X-ray exposure is associated with greater chance of developing cancer. We also plan to develop techniques that can learn to segment abnormalities and predict radiological scores from weakly labelled training data. It will also assist in making decisions about treatment by providing quantitative parameters.
This project is carried at the Department of Engineering, jointly supervised by Prof. Andrew Zisserman, Dr. Amir Jamaludin and Dr. Timor Kadir. Industrial support will be provided by Thomas Nichols from Oxford BDI-Novartis.

The UK's world-leading position in biomedical research is critically dependent upon training scientists with the cutting-edge research skills and technological know-how needed to drive future scientific advances. Since 2009, the EPSRC and MRC CDT in Systems Approaches to Biomedical Science (SABS) has been working with its consortium of 22 industrial and institutional partners to meet this training need.

Over this period, our partners have identified a growing training need caused by the increasing reliance on computational approaches and research software. The new EPSRC CDT in Sustainable Approaches to Biomedical Science: Responsible and Reproducible Research - SABS:R^3 will address this need. By embedding a sustainable approach to software and computational model development into all aspects of the existing SABS training programme, we aim to foster a culture change in how the computational tools and research software that now underpin much of biomedical research are developed, and hence how quantitative and predictive translational biomedical research is undertaken.

As with all CDT Programmes, the future impact of SABS:R^3 will be through its alumni, and by the culture change that its training engenders. By these measures, our existing SABS CDT is already proving remarkably successful. Our alumni have gone on to a wide range of successful careers, 21 in academic research, 19 in industry (including 5 in SABS partner companies) and the other 10 working in organisations from the Office of National Statistics to the EPSRC. SABS' unique Open Innovation framework has facilitated new company connections and a high level of operational freedom, facilitating 14 multi-company, pre-competitive, collaborative doctoral research projects between 11 companies, each focused on a SABS student.

The impact of sustainable and open computational approaches on biomedical research is clear from existing SABS' student projects. Examples include SAbDab which resulted from the first-ever co-sponsored doctorate in SABS, by UCB and Roche. It was released as open source software, is embedded in the pipelines of several pharmaceutical companies (including UCB, Medimmune, GSK, and Lonza) and has resulted in 13 papers. The SABS student who developed SAbDab was initially seconded to MedImmune, sponsored by EPSRC IAA funding; he went on to work at Roche, and is now at BenevolentAI. Similarly, PanDDA, multi-dataset X-ray crystallographic software to detect ligand-bound states in protein complexes is in CCP4 and is an integral part of Diamond Light Source's XChem Pipeline. The SABS student who developed PanDDA was awarded an EMBO Fellowship.

Future SABS:R^3 students will undertake research supported by both our industrial partners and academic supervisors. These supervisors have a strong track record of high impact research through the release of open source software, computational tools, and databases, and through commercialisation and licensing of their research. All of this research has been undertaken in collaboration with industrial partners, with many examples of these tools now in routine use within partner companies.

The newly focused SABS:R^3 will permit new industrial collaborations. Six new partners have joined the consortium to support this new bid, ranging from major multinationals (e.g. Unilever) to SMEs (e.g. Lhasa). SABS:R^3 will continue to make all of its research and teaching resources publicly available and will continue to help to create other centres with similar aims. To promote a wider cultural change, the SABS:R^3 will also engage with the academic publishing industry (Elsevier, OUP, and Taylor & Francis). We will explore novel ways of disseminating the outputs of computational biomedical research, to engender trust in the released tools and software, facilitate more uptake and re-use.