YOLORG: A multi-scale intestinal Organoid detection algorithm

Intestinal organoids have shown great research value in areas such as drug screening and disease modeling due to their unique physiological properties. However, the morphological diversity of organoids made the accurate acquisition of morphological information particularly critical and challenging. Although conventional fluorescent labeling assays could provide a certain degree of morphological information, the potential risk of compromising the integrity of organoids could not be ignored. Traditional bounding box detection methods were not capable of capturing details when dealing with the complex and variable morphology of intestinal organoids. Meanwhile, the huge size of the gut organoid image dataset, and the subjective and time-consuming manual classification, made it difficult to meet the research demand for high efficiency. Although some deep learning methods had made significant progress in the field of image processing, they still faced great challenges in dealing with complex structures such as organoids, which had significant shape and size heterogeneity. The paper proposes an intestinal organoid detection method YOLORG. YOLORG employed a multi-scale feature extraction module to fuse the multi-scale attributes of organoid specimens. This method effectively eliminated background interference and image noise, thus improving the accuracy and robustness of organoid detection.