<p>Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease 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 organoids 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 imaging conditions. Unlike existing methods, POST accurately segments each organoid and eliminates various artifacts encountered during organoid culturing and imaging. Furthermore, it is sensitive to and aligns with measurements of organoid activity 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.</p>