ALT Results for object segmentation based on foundation model (FM) features with a random forest, compared with supervised learning baselines and classical features. Experiments over five different datasets covering cell classification in spatial proteomics (CRC, HBM), high-content macroscopic imaging for animal phenotyping (Planri), nucleus classification in histopathology (PanNuke), and cell line classification in phase-contrast microscopy (LIVECell). The extra plot repots runtimes for model training and inference.

ALT valuation of different methods for pixel classification (top) and object classification (bottom) on the LIVECell dataset. Dark green bars show the F1-Score, which measures the classification / segmentation quality (higher is better), light green bars show the runtimes. Five different settings are compared for each task: Ilastik features + random forest (RF), microSAM embeddings + RF, SAM2 embeddings + RF, and uSAM, SAM2 + attentive probing (DeAP and ObAP). microSAM feature perform best for RF based methods, attentive probing outperforms RF based approaches, but at a much higher runtime.

ALT A cochlea imaged in light-sheet microscopy (right) with staining for spiral ganglion neurons (red) and inner hair cells (blue). You will develop AI-based methods to analyze these structures, for example via segmentation of the individual cells (right) that will support gene therapy development for hearing loss and a better overall understanding of the anatomy of hearing.