I'm a high school student planning to study radiology, and my final school research project needs to be completed by December. I'm considering a project on automated detection and morphometric analysis of biological structures using computer image processing. The idea would be to use biological images with Python or ImageJ to automatically detect, count, and measure structures such as cells. I have no programming experience, so I'd like an honest assessment of how difficult this would be and whether AI tools could help me create a manageable version of the project.
3 Answers
AI can definitely help you get started by explaining code, suggesting methods, and helping troubleshoot errors, but you should treat it as a guide rather than letting it build everything blindly. A sensible plan would be to learn basic Python, choose a small set of images, create a simple thresholding or segmentation workflow, and evaluate how accurately it counts and measures the structures. Documenting the limitations and testing different methods could make the project strong even if the results are imperfect.
This is related to computer vision and machine learning, not just ordinary beginner programming. More advanced approaches can involve statistics, linear algebra, and calculus, so training a sophisticated model from scratch may be too ambitious. For a school project, you could narrow the scope and compare a few existing image-processing methods instead of trying to invent a complete medical system.
A basic version is realistic, but a fully reliable system would be quite advanced. If you use clear images and focus on simple tasks like separating cells from the background, counting them, and measuring their area, you could make progress with Python libraries or ImageJ. The project becomes much harder when cells overlap, images vary in quality, or you need very high accuracy.
That makes sense. I’d like to use AI for guidance while still understanding the code and methods well enough to explain them in my report.