摘要Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical dis-ease treatment.Their applications span from high-throughput drug screening to the modeling of complex diseases,with some even achieving clinical translation.Changes in the overall size,shape,boundary,and other morphological features of organ-oids provide a noninvasive method for assessing organoid drug sensitivity.However,the precise segmentation of organoids in bright-field microscopy images is made difficult by the complexity of the organoid morphology and interference,including overlapping organoids,bubbles,dust particles,and cell fragments.This paper introduces the precision organoid segmentation technique(POST),which is a deep-learning algorithm for segmenting challenging organoids under simple bright-field imag-ing conditions.Unlike existing methods,POST accurately segments each organoid and eliminates various artifacts encoun-tered during organoid culturing and imaging.Furthermore,it is sensitive to and aligns with measurements of organoid activ-ity in drug sensitivity experiments.POST is expected to be a valuable tool for drug screening using organoids owing to its capability of automatically and rapidly eliminating interfering substances and thereby streamlining the organoid analysis and drug screening process.