Evaluation of segmentation techniques for cell tracking in confocal microscopy images

Abstract

In different biological studies, such as cell regeneration studies, cell tracking over time is required. Thus, in these studies, the evolution of an amputated limb of the crustacean Parhyale hawaiensis is tracked using 4D confocal microscopy images. However, given the high number of images, noise level and number of cells make the manual cell tracking process a complex, cumbersome and difficult task. The tracking process using image processing techniques generally includes three stages: image enhancement, segmentation and cell identification. A tool made for this purpose, as a plugin of the ImageJ program is TrackMate, commonly used by biologists, which includes for segmentation the Laplacian of Gaussian (LoG) and Difference of Gaussians (DoG) edge detectors. To provide even more powerful detectors, the filtering methods based on the second derivative of Deriche and Shen and Castan were implemented and included in TrackMate. These four methods were evaluated for cell detection in images of Parhyale hawaiensis, finding that the Deriche and, Shen and Castan filters detected an appreciable number of false positives, due to sensitivity to noise and because the same cell was counted multiple times. As for the LoG and DoG methods, they presented the best results, being very similar because the DoG is basically an approximation of the LoG, finding that the DoG method slightly outperformed the LoG.