Automated cell junction tracking with modified active contours guided by SIFT flow

AUTOMATED CELL JUNCTION TRACKING WITH MODIFIED ACTIVE CONTOURS GUIDED BY SIFT FLOW Chen-Yu Lee, Sukryool Kang, Andrew D. Chisholm † , and Pamela C. Cosman Department of Electrical and Computer Engineering Division of Biological Sciences, Section of Cell and Development Biology University of California at San Diego, La Jolla, CA, 92093-0407, USA ABSTRACT We present a novel algorithmic approach to track multiple cell junctions automatically in the developing epidermis of the C. elegans embryo. 3D cell boundaries are projected into 2D for segmentation using active contours with a non-intersection force, and subsequently tracked using SIFT (Scale-Invariant Feature Transform) flow. Our method achieves MAD (Mean Absolute Distance) less than 3 pixels between all tracked cell contours and ground truth data. Using this method we have generated a quantitative description of epidermal cell move- ments and shape changes during the process of epidermal en- closure. Fig. 1: Dataset snapshots. Columns 1 to 6 show Z-stack im- ages for slices 1,7,13,19,25, and 31. Last column shows max- imum projection images. Each row represents acquired data at one time point. Images are inverted for display. quires cell contour signals on the embryo surface. Active con- tours (also called snakes) [5] with a proposed non-intersection force can precisely segment all epidermal cells in 2D maxi- mum projection images. To handle large displacement of cell movement, we conduct experiments using conventional opti- cal flow and SIFT (Scale-Invariant Feature Transform) flow [6]. Experimental results show our modified active contours with SIFT flow can accurately track epithelial junctions. Our methods yield a quantitative description of the dynamics of epithelial shape changes during epidermal enclosure. Index Terms— C. elegans, cell tracking, SIFT flow 1. INTRODUCTION Quantitative analysis of cell shape in live samples is an im- portant goal in developmental biology. The nematode worm Caenorhabditis elegans is an excellent organism for analyz- ing fundamental aspects of development because it is simple, easy to grow in bulk populations, and convenient for genetic analysis. C. elegans has a fixed number of cells in early em- bryogenesis, all of which have been individually identified and characterized. We are interested in epidermal develop- ment as a model for epithelial morphogenesis. Tracking cells or subcellular structures in developing em- bryos is important to understand developmental processes. Computer aided tracking allows quantitative analysis of large numbers of cells or objects. Recently, several automated or semi-automated nuclei tracking algorithms [1][2][3][4] that allow quantitative analysis of nuclear positions in C elegans have been developed. However, nuclear positions do not pro- vide direct information on cell shape, size, or cellular con- tacts. A major remaining challenge is to segment and track cell surfaces or contacts in complex 4D data (Figure 1). The difficulty of tracking cells on surfaces lies in lack of image texture or color information. The recorded data only contains cell boundaries along the surface. In this paper, we present a new method to automatically segment and track epithelial junctions in 4D data that only re- U.S. Government work not protected by U.S. copyright 2. DATA ACQUISITION The C. elegans embryo consists of 24 epidermal cells on the surface. We use a Zeiss LSM700 to acquire Confocal Laser Scanning Microscope (CLSM) cell images over 20 minutes to capture the closing of the epidermal cells. Epidermal junc- tions visualized with DLG-1::GFP form lines at the subapical circumference of differentiated epidermal cells. By changing the focal length, our dataset contains 35 Z-stack image slices that cover the whole C. elegans embryo. Columns 1 to 6 in Figure 1 show the Z-stack snapshots for slices 1, 7, 13, 19, 25, and 31. However, Z-stack images are not informative for visu- alizing and tracking cell contours. We use a maximum projec- tion image as our input data. The pixel intensity value of each pixel is selected from the maximum pixel value through all Z-stack images. The maximum projection image can better visualize epidermal cells of the C. elegans embryo as shown in the last column of Figure 1.