Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: a tool for cell-based drug testing

This paper presents a segmentation and tracking method for quantitative analysis of cell dynamics from in vitro videomicroscopy data. The method is based on parametric active contours and includes several adaptations that address important difficulties of cellular imaging, particularly the presence of low-contrast boundary deformations known as pseudopods, and the occurence of multiple contacts between cells. First, we use an edge map based on the average intensity dispersion that takes advantage of relative background homogeneity to facilitate the detection of both pseudopods and interfaces between adjacent cells. Second, we introduce a repulsive interaction between contours that allows correct segmentation of objects in contact and overcomes the shortcomings of previously reported techniques to enforce contour separation. Our tracking technique was validated on a realistic data set by comparison with a manually defined ground-truth and was successfully applied to study the motility of amoebae in a biological research project.

[1]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[2]  Jean-Christophe Olivo-Marin,et al.  Active contours for the movement and motility analysis of biological objects , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[3]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Jean-Christophe Olivo-Marin,et al.  Active contours applied to the shape and motion analysis of amoeba , 2001, SPIE Optics + Photonics.

[5]  Frederic Fol Leymarie,et al.  Tracking Deformable Objects in the Plane Using an Active Contour Model , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  J. Olivo-Marin,et al.  The membrane-microfilament linker ezrin is involved in the formation of the immunological synapse and in T cell activation. , 2001, Immunity.

[7]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Anthony J. Yezzi,et al.  Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification , 2001, IEEE Trans. Image Process..

[9]  Johan Montagnat,et al.  Shape and Topology Constraints on Parametric Active Contours , 2001, Comput. Vis. Image Underst..

[10]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

[11]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[12]  Marina Chicurel,et al.  Cell Migration Research Is on the Move , 2002, Science.

[13]  J C Olivo,et al.  Virulence and functions of myosin II are inhibited by overexpression of light meromyosin in Entamoeba histolytica. , 1998, Molecular biology of the cell.

[14]  L M Loew,et al.  Electric field-directed fibroblast locomotion involves cell surface molecular reorganization and is calcium independent , 1994, The Journal of cell biology.

[15]  Jean-Christophe Olivo-Marin,et al.  EhPAK, a member of the p21-activated kinase family, is involved in the control of Entamoeba histolytica migration and phagocytosis , 2003, Journal of Cell Science.

[16]  Krishna R. Pattipati,et al.  Multiassignment for tracking a large number of overlapping objects , 1997, Optics & Photonics.

[17]  Junaed Sattar Snakes , Shapes and Gradient Vector Flow , 2022 .

[18]  D. Lauffenburger,et al.  Cell Migration: A Physically Integrated Molecular Process , 1996, Cell.

[19]  Tim D. Jones,et al.  An active contour model for measuring the area of leg ulcers , 2000, IEEE Transactions on Medical Imaging.

[20]  Demetri Terzopoulos,et al.  8 – Deformable Models , 2000 .