Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images

To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies.These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.Recent advances in cell imaging technologies include accurate stage controllers, improved optics, increased camera resolution, and, perhaps most importantly, fluorescent staining of specific cellular components. Together these advances enable automated image acquisition of small subcellular objects with the goal of providing insight into phenotypes and cellular functions [1-4]. With increased imaging throughput and large-scale data acquisition, the challenge of image interpretation and information extraction has also shifted from visual inspection or interactive analysis to more automated methods [5,6].Accurate and automated subcellular object segmentation is essential for a variety of applications. For example, interpreting complex cellular phe