Abstract
In medical research, organoids, which exhibit structural and functional resemblances to authentic organs, offer a substantial avenue for delving into aspects encompassing physiology, pathophysiology, diseases, and pharmaceutical screening. Critical insights into drug responsiveness are often gleaned from these organoids' dimensional and configurational disparities. However, conventional detection methodologies reliant upon fluorescent labeling engender potential hazards to organoid integrity, thereby impinging upon their intrinsic dynamic attributes. Traditional bounding-box detection methodologies fall short in encapsulating intricate morphological particulars, and certain deep-learning approaches grapple with the intricate task of capturing multi-scale data, particularly when tasked with discerning organoid structures characterized by marked shape and size heterogeneities. In a bid to surmount these constraints, our study introduces CAMPEOD, an innovative framework that synergistically amalgamates multi-scale attributes derived from organoid specimens, employing cross-attention mechanisms. This novel approach effectively obviates superfluous background interference and image noise, thereby endowing an automated, finely-tuned dissection of organoid samples. Significantly, this segmentation process ensures congruence with authentic organoid quantities and morphological characteristics. By facilitating comprehensive scrutiny of microscopy images of organoid samples on a large scale, CAMPEOD assumes considerable implications for the realm of pharmaceutical screening and ailment emulation.