Automated image segmentation for detecting cell spreading for metastasizing assessments of cancer development
This work addresses a domain-specific problem for cancer research, but it is incremental as it applies existing methods to new data without significant advancements.
The authors tackled automated segmentation of cells in microscopic DIC images to assess cancer development, but found that detecting cell boundaries under realistic conditions remains challenging without concrete performance numbers.
The automated segmentation of cells in microscopic images is an open research problem that has important implications for studies of the developmental and cancer processes based on in vitro models. In this paper, we present the approach for segmentation of the DIC images of cultured cells using G-neighbor smoothing followed by Kauwahara filtering and local standard deviation approach for boundary detection. NIH FIJI/ImageJ tools are used to create the ground truth dataset. The results of this work indicate that detection of cell boundaries using segmentation approach even in the case of realistic measurement conditions is a challenging problem.