Why Machine Learning?

← Back to Electrochemical Imaging

Manual segmentation of large microscopy, spectroscopy, and tomography datasets is prohibitively slow and introduces inter-analyst variability. Rule-based image processing (thresholding, watershed) performs well only when imaging conditions are uniform, and it does not by itself exploit the temporal dependence between frames in a time-resolved sequence. Machine-learning approaches offer:

• Generalization: learned features adapt to varying contrast, noise, and morphology.

• Scalability: inference on thousands of images in minutes once a model is trained.

• Reproducibility: deterministic predictions from a fixed model.

• Transfer learning: foundation models trained on large generic datasets (SAM, NASA MicroNet) can be adapted to new domains with few or no labeled examples.

The primary challenge in applying ML to electrochemical imaging is the scarcity of labeled data: pixel-level annotation of microscopy, spectroscopy, and tomography images — and even more so of temporal image sequences, where every frame would need its own annotation — is extremely time-consuming. This motivates the synthetic-data generation approaches covered in Synthetic Data Generation.