Detection & Classification of Microcalcification Clusters in Mammograms

This project provides an annual stipend of £19,237.

Project advert

Manual identification of small changes in mammographic (breast X-ray) scans is a difficult process, and as such clinical experts are known to sometimes miss early signs of cancer development. Micro-calcifications (MC) are one type of mammographic abnormalities associated with early breast cancer symptoms, which appear as a cluster of small bright blobs. This PhD project aims to develop automated computational techniques for detecting and classifying such clusters (Xiao et al., 2021; Gomathi et al., 2023). It will focus on understanding the interface between medical image analysis and advanced machine learning (deep learning) in radiology. The successful PhD candidate will drive the development of data analysis and image processing techniques to fuse 2D and 3D information to classify MC clusters with confidence scores. The detection will provide a 3D representation of the MC clusters and will build on existing approaches (Brahimetaj et al., 2022). A 3D topological (Alam and Zwiggelaar, 2018), density distribution (He et al., 2015) and location (Andreadis et al., 2014) model will be developed for the classification of the clusters as benign or malignant.

As a PhD candidate at Manchester Metropolitan University, you’ll access state-of-the-art facilities, including the new £117M Dalton Building with advanced labs and collaborative spaces.

Project aims and objectives

We want to bring two novel components to this field of research, which are a) to use digital breast tomosynthesis and 2D mammography to provide a patient outcome prediction for MC clusters, and b) develop a pipeline to segment/model microcalcification clusters in 3D which will provide clinicians with a 3D perspective of different breast abnormalities.

Specific requirements of the candidate

Essential Criteria

• BSc/MSc in Mathematics, Statistics, Computer Science or related discipline (a minimum honours degree at UK first or upper second-class level).
• Experience in image analysis.
• Basic practical experience in implementing deep-learning and artificial intelligence methods.
• Proficiency in Python, ImageJ, TensorFlow/PyTorch.
• Ability to adapt statistical methodology for research studies.
• Should have or willing to work within a multidisciplinary environment.

Desirable Criteria

• Experience in analysing clinical research data/ Knowledge of health research.
• Familiarity with version control management tools (GitLab/ Bitbucket).
• Familiarity withITK and ParaView.
• Understanding of dataset design and management.
• Writing manuals and protocols. Previous publication is a plus (Please provide a Writing Samples if available).
• Working knowledge in Unix operating systems.

Candidates are strongly encouraged to specifically address the essential criteria outlined in the Person Specification in their statement of purpose letter. 

As a PhD student, you will be expected to actively participate within the programme of study, showing good time management and organisation. You are expected to work upon and further develop initial research questions, completing tasks required to gain a PhD such as attending meetings, regularly reviewing literature, completing pertinent studies, disseminating research works/outputs at appropriate forums and writing a thesis on the topic.

How to apply

Interested applicants should contact Dr. Nashid Alam for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Computing and Digital Technologies (or download the PGR application form).

You should also complete the PGR thesis proposal and a Narrative CV (supplementary information) form addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to [email protected].

Closing date: 14 October 2024. Expected start date: January 2025 for Home students and April 2025 for International students. 

Please note that Home fees are covered. Eligible International students will need to make up the difference in tuition fee funding. 

Please quote the reference: SciEng-2024-Mammograms