Hi mentors!!!
I’m Favour Achara, a final-year Computer Science student and I am very excited about Project #40: QC-Studio. Nice to meet you all!!!
I have a solid background in building AI tools for medical imaging. Recently, I developed a 3D Brain Tumor Segmentation API that handles the end-to-end pipeline of converting raw DICOM/NIfTI series into 3D GLB meshes for web visualization.
My experience aligns closely with several core aims of this project: I have deep experience with Nibabel and SimpleITK for handling NIfTI volumes. I’ve worked with marching_cubes and trimesh to generate decimated 3D meshes for browser performance. I believe will translate well to working with NiiVue. While my recent project used FastAPI, I am very comfortable with Python-based web frameworks and am eager to apply this to the Streamlit requirements. I am particularly interested in the enhancement goal of exploring LLM utility for image quality annotations and explanations.
I’ve already begun exploring the qc-studio repository design. Are there any specific good first issues/newcomer issues or a particular area of the NiiVue+Streamlit integration you’d recommend I look into first to better prepare my proposal?
Looking forward to contributing!