A Semi-automatic Annotation Framework for Neutrophil Ultrastructure from TEM Images

TEM images of immune cells ultrastructure are critical for understanding immune responses, but manual annotation is time-consuming and subjective. To address this, we propose a semi-automatic pipeline integrating the YOLOv9 model with CVAT within a human-in-the-loop framework. Our approach leverages YOLOv9 to generate initial bounding box predictions, converts them to ellipses, and enables iterative refinement in CVAT, improving annotation accuracy over multiple cycles. On a test set of 26 high-resolution TEM images containing 6028 neutrophil ultrastructures, the pipeline increased the mean Average Precision at an Intersection over Union threshold of 0.5 from 0.53 to 0.688 after three iterations, detecting approximately 95% of the objects and reducing annotator workload by around 80%. The pipeline also demonstrated extensibility, detecting about 80% of ultrastructures in eosinophil TEM images, as validated by biomedical experts. To our knowledge, no dataset currently exists with all immune cell ultrastructures annotated, making our pipeline a valuable tool for generating high-quality datasets. This work accelerates TEM annotation, enhances dataset consistency, and supports broader immune cell research, with future extensions planned for other cell types like lymphocytes and monocytes.