Associating approximate paths and temporal sequences of noisy detections: Application to the recovery of spatio-temporal cancer cell trajectories.

In this paper we address the problem of recovering spatio-temporal trajectories of cancer cells in phase contrast video-microscopy where the user provides the paths on which the cells are moving. The paths are purely spatial, without temporal information. To recover the temporal information associated to a given path we propose an approach based on automatic cell detection and on a graph-based shortest path search.

Monitoring the cytoskeletal EGF response in live gastric carcinoma cells.

Altered cell motility is considered to be a key factor in determining tumor invasion and metastasis. Epidermal growth factor (EGF) signaling has been implicated in this process by affecting cytoskeletal organization and dynamics in multiple ways. To sort the temporal and spatial regulation of EGF-dependent cytoskeletal re-organization in relation to a cell's motile behavior time-lapse microscopy was performed on EGF-responsive gastric carcinoma-derived MKN1 cells co-expressing different fluorescently labeled cytoskeletal filaments and focal adhesion components in various combinations.

Learning cellular texture features in microscopic cancer cell images for automated cell-detection.

In this paper we present a new approach for automated cell detection in single frames of 2D microscopic phase contrast images of cancer cells which is based on learning cellular texture features. The main challenge addressed in this paper is to deal with clusters of cells where each cell has a rather complex appearance composed of sub-regions with different texture features. Our approach works on two different levels of abstraction.