On 2024-06-01 I noted an interesting idea: "Interesting use cases of computer vision tech and dentistry". This adds a dental domain to my exploration of medical imaging and computer vision, prompting me to consider which specific CV applications, datasets, and clinical settings might be relevant, and what performance metrics would matter.
I reviewed the paper “From Forks to Forceps: A New Framework for Instance Segmentation of Surgical Instruments”. It highlights challenges such as visual similarity, low inter‑class variance, thin instrument geometry (width‑to‑height ratio ≥ 2), and adverse imaging conditions (occlusions, specularities, smoke, blur).
- Method: metric‑learning classifier trained with Arc Loss to improve discrimination between visually similar tools.
- Decision: use instance segmentation rather than pure detection to handle occluded/overlapping instruments.