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3D imaging technology roadmap and software tool for X-ray histology

  • Full or part time
  • Application Deadline
    Friday, May 29, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Supervisor: Philipp Schneider, Philip Basford, Peter Lackie (Faculty of Medicine)

Project description

This PhD project offers a unique interdisciplinary life sciences experience, where you will work at the interface of medicine, biomedical imaging and industrial development, with the aim to help patients suffering from idiopathic pulmonary fibrosis, colorectal cancer and many other diseases that affect soft tissues in our body

You will work closely together with several Research Fellows, acting across μ-VIS X-ray Imaging Centre (, the Biomedical Imaging Unit (BIU) in Medicine (, and the Computational and Engineering Design Research Group in the Faculty of Engineering and Physical Sciences (FEPS). You will be actively involved in and contribute to our bi-weekly Correlative Imaging Forum ( between Engineering and Medicine, involving PhDs and PDRAs working on interdisciplinary projects, to foster and strengthen collaborations across traditional Faculty borders and academic disciplines.

Recently, IBM bought billions of medical images to transform image-based diagnostics. Google’s recent offshoot Deep Mind Health is also focusing on the analysis of medical data to assist clinicians in radiotherapy planning. These image-based diagnostic efforts are currently focused on areas where digitisation of medical imaging has been accomplished, such as in radiology. In histopathology, the image data from light microscopy is generally not digitised to date, yet the revolution towards digital histopathology has gained momentum and will soon give rise to novel image-based diagnostic opportunities.

We recently demonstrated for the first time that high-quality three-dimensional (3D) imaging can be performed on standard paraffin-embedded diagnostic soft tissue samples using micro-computed CT (μCT), which reveals diagnostically relevant tissue structures and cellular detail (doi: 10.1016/j.ajpath.2019.05.004). In collaboration with industry, we have developed a first-of-kind μCT system for clinical pathology and we are now demonstrating the diagnostic value of this clinical μCT system for so-called 3D X-ray histology or XRH (, based on correlative 2D microscopy and 3D μCT imaging.

The first objective of this PhD project is to test and benchmark emerging X-ray technologies when compared to the baseline performance of an existing (μCT) system for 3D X-ray histology, including a novel rotating X-ray source, an automated sample changing system, and a state-of-the art hybrid photon counting detector, which are expected to increase workflow automation and sample throughput.

The second objective of this PhD project is to develop an interactive software tool and user-friendly general user interface, for co-registration, morphing and fused visualisation of conventional 2D thin section-based histology and novel 3D µCT-based data. This will allow histopathologists and biomedical researchers to explore soft tissues interactively, such as lung, colon or liver, to understand and demonstrate the added benefit from combined 2D & 3D imaging of tissues in their native 3D state, beyond traditional 2D microscopy of histopathology in the hospital.

Entry Requirements
A good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: 29 May 2020.

Funding: full tuition fees for EU/UK students plus for UK students, a stipend of £15,285pa for up to 3.5 years.

How To Apply

Apply online, select the academic session 2020-21 “PhD Eng & Env (Full time)” as the programme, and enter Philip Schneider under the proposed supervisor.

Applications should include:
Curriculum Vitae
Two reference letters
Degree Transcripts to date
Apply online:

For further information please contact:

How good is research at University of Southampton in General Engineering?

FTE Category A staff submitted: 192.23

Research output data provided by the Research Excellence Framework (REF)

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