Adapting Open-Weight MLLMs to Generate Point Prompts for Electron Microscopy Segmentation
Promptable models such as microSAM segment electron microscopy (EM) images from point prompts, but automation requires generating prompts without user input. We ask whether open-weight multimodal large language models (MLLMs) can generate them from natural-language requests by returning coordinates to a frozen segmenter. To that end, we convert masks from three mitochondria datasets into training examples, pairing images and instructions with centroid coordinates, then train LoRA adapters while freezing the MLLM backbone and microSAM. We find that Qwen3-VL reaches segmentation AP$_{50}$ $0.736$ after supervised fine-tuning and reward optimization, up from $0.247$ without adaptation, while...
| Authors | Samia Mohinta, Albert Cardona |
|---|---|
| Primary category | cs.CV |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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