Degree type
PhD
Closing date
1 June 2024
Campus
Hobart
Citizenship requirement
Domestic
About the research project
The proposed Ph.D. project focuses on the development of a new innovative deep-learning algorithm for segmenting and analysing moving small objects in images. The algorithm will be applied to the field of biomedical engineering, specifically in the area of reproductive biomedicine. The objective is to improve the accuracy and efficiency of predicting and diagnosing infertility in cell culture observation. The algorithm will be based on a novel deep-learning model that can segment and track the motion of fertilized embryos in images collected from laboratory animals. The deep learning model will also be able to analyse the morphological characteristics of embryos and assess their fertilization potential and the causes of infertility.
The project will involve several key stages, including image acquisition, image pre-processing, model development, evaluation, fine-tuning, and implementation. Image acquisition will involve capturing high-resolution images of fertilized embryos using a microscope camera. Image pre-processing will involve enhancing the image quality and removing the noise and background. Model development will involve designing and implementing a new innovative deep-learning model that can segment and analyze moving small objects in images. The model will also use a classification network to predict the fertilization potential and the causes of infertility of embryos. Evaluation will involve testing the model on a large and diverse dataset of embryo images and comparing its performance with existing methods using various metrics such as accuracy, sensitivity, specificity, and F1-score. Fine-tuning will involve optimizing the model parameters and hyperparameters to improve its performance. Implementation will involve integrating the model into a cell culture observation system and deploying it in a real-world scenario.
The project demonstrates the innovation and potential of deep learning in the ICT field and in the biomedical field. This project shows how ICT can contribute to the advancement of healthcare solutions and improve medical outcomes. It also shows how deep learning can solve challenging and important problems in the field of reproductive biomedicine by providing a more accurate and efficient method for segmenting and analyzing moving small objects in images. The project also opens up opportunities for further research in this area and in other related areas.
Primary Supervisor
Meet Dr Mira ParkFunding
Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:
- a living allowance stipend of $32,192 per annum (2024 rate, indexed annually) for 3.5 years
- a relocation allowance of up to $2,000
- a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)
If successful, international applicants will receive a University of Tasmania Fees Offset for up to four years.
As part of the application process you may indicate if you do not wish to be considered for scholarship funding.
Other funding opportunities and fees
For further information regarding other scholarships on offer, and the various fees of undertaking a research degree, please visit our Scholarships and fees on research degrees page.
Eligibility
Applicants should review the Higher Degree by Research minimum entry requirements.
Ensure your eligibility for the scholarship round by referring to our Key Dates.
Additional eligibility criteria specific to this project/scholarship:
- Applicants must be able to undertake the project on-campus
Selection Criteria
The project is competitively assessed and awarded. Selection is based on academic merit and suitability to the project as determined by the College.
Additional essential selection criteria specific to this project:
- Research skills
Additional desirable selection criteria specific to this project:
- Python
Application process
- Select your project, and check that you meet the eligibility and selection criteria, including citizenship;
- Contact Dr Mira Park to discuss your suitability and the project's requirements; and
- In your application:
- Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
- Submit a signed supervisory support form, a CV including contact details of 2 referees and your project research proposal.
- Apply prior to 1 June 2024.
Full details of the application process can be found under the 'How to apply' section of the Research Degrees website.
Following the closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.
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