Undergraduate Course: Biomedical Image Analysis 4 (IBMS10010)
Course Outline
| School | Deanery of Biomedical Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 10 (Year 4 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course aims to give students the ability to apply and improve strategies and pipelines for biomedical image analysis. It will provide students with the opportunity to understand and use an array of algorithms for image processing and analysis and prepare them to be able to develop ad hoc strategies for specific problems they may encounter in real-life situations. |
| Course description |
This course gives students the ability to apply and improve strategies and pipelines for biomedical image analysis. The course will build and expand on knowledge of image and data analysis gathered throughout previous years. From this basis, it will provide students with the opportunity to understand and use an array of algorithms for image processing and analysis and prepare them to be able to develop ad hoc strategies for specific problems they may encounter in real-life situations. This course aims to equip students with skills that will apply to analysis of a wide range of types of biological images, from 2D and 3D photomicrographs, medical images such as MRI scans, as well as time-lapses, e.g. from calcium imaging or behavioural experiments. The course will expose students to common, yet often complex, problems in image analysis, and suggest tools and algorithms for solving them; these include, for instance, image segmentation, particles and object tracking, contour tracing, object labelling and classification, image registration in 2D and 3D. Python will be used throughout this course as it offers a vast array of open-source libraries dedicated to image analysis.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2021/22, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
196 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Develop piece of relevant software and accompanying documentation (group; 40%)«br /»
Report on developing the software (individual; 60%)«br /»
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| Feedback |
To allow students to have feedback on their project, they will produce a formative project plan early in the course, including which outputs they aim to fulfil, and how.
Students will be encouraged to discuss their progress throughout the course in dedicated sessions. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Describe and critically discuss strategies and algorithms for analysis of biomedical images.
- Implement these strategies using Python.
- Work in a team to critically address a real-life problem involving image analysis by developing and applying an appropriate analysis pipeline.
- Professionally document their analysis, presenting their pipeline and the result of their analysis, in the context of the biomedical problem it is aiming to solve.
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Additional Information
| Graduate Attributes and Skills |
This course develops the following graduate skills and attributes:
General professional skills: Time management, project management, independence, curiosity, self-motivation, ability to complete projects, critical thinking, dedication, analytical reasoning, scientific creativity.
Bioinformatics Skills: Understanding and skills in image processing, analysis and interpretation. Using and developing software in Python. |
| Keywords | Image analysis,image processing,algorithms |
Contacts
| Course organiser | Dr Nicola Romano
Tel:
Email: nromano@exseed.ed.ac.uk |
Course secretary | Miss Natasha Goldie
Tel:
Email: natasha.goldie@ed.ac.uk |
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