A dynamic-shape-prior guided snake model with application in visually tracking dense cell populations

This paper proposes a dynamic-shape-prior guided snake (DSP G-snake) model that is designed to improve the overall stability of the point-based snake model. The dynamic shape prior is first proposed for snakes, that efficiently unifies different types of high-level priors into a new force term. To be specific, a global-topology regularity is first introduced that settles the inherent self-intersection problem with snakes. The problem that a snake’s snaxels tend to unevenly distribute along the contour is also handled, leading to good parameterization. Unlike existing methods that employ learning templates or commonly enforce hard priors, the dynamic-template scheme strongly respects the deformation flexibility of the model, while retaining a decent global topology for the snake. It is verified by experiments that the proposed algorithm can effectively prevent snakes from selfcrossing, or automatically untie an already self-intersected contour. In addition, the proposed model is combined with existing forces and applied to the very challenging task of tracking dense biological cell populations. The DSP G-snake model has enabled an improvement of up to 30% in tracking accuracy with respect to regular model-based approaches. Through experiments on real cellular datasets, with highly dense populations and relatively large displacements, it is confirmed that the proposed approach has enabled superior performance, in comparison to modern active-contour competitors as well as the state-of-the-art cell tracking frameworks

Automatic biological object segmentation and tracking in unconstrained microscopic video conditions

Cell and small biological organism tracking research is of fundamental importance for the analysis of dynamic behaviour for assisting the development of many biomedical image related applications. With the rapid development of digitised imaging systems, the immense collections of experimental (microscopic) videos make it nearly impossible to manually analyse the obtained data. Therefore, recent research has drawn attention to building automatic tracking systems to track the movement of cells and small biological organism models using videos taken by microscopes. Although general object tracking (such as traffic cars and pedestrians) has been studied for decades, existing general object tracking systems cannot directly be applied to cell and small biological organism tracking, due to the differences in the imaging devices and conditions of the targets. This research therefore investigates the novel application of computer vision techniques to reliably, accurately and effectively track the movement of cells and small biological organisms automatically. Due to difficulties in generating video segmentation ground-truth, there is a general lack of segmentation datasets with annotated ground-truth (particularly for biomedical images). This work proposes an efficient and scalable crowdsourced approach to generate video segmentation ground-truth and develops a tracking ground-truth generation system. To illustrate the proposed approach, an annotated zebrafish larvae video segmentation dataset and three tracking datasets have been generated and made freely available online. Automatic cell tracking techniques require accurate cell image segmentation; however, current general object segmentation techniques are susceptible to errors due to the poor microscopic imaging conditions, which include low contrast typical of cell microscopic images. This work proposes a novel image pre-processing technique to enhance low greyscale image contrast for improved cell image segmentation accuracy. An adaptive, shifted bi-Gaussian mixture model is matched to the original cell image intensity histogram for greater differentiation between the cell foreground and image background, while maintaining the original intensity histogram shape. Small biological organism videos taken by microscope imaging devices under realistic experimental conditions have more complex video backgrounds than cell videos. This work first investigates single zebrafish larvae tracking using dense SIFT flow and downsampling techniques. Many existing multiple small organism tracking systems require very strict video imaging conditions, which typically result in unreliable tracking results for realistic experimental conditions. Thus, this research further investigates the adaptation of advanced segmentation techniques to improve the performance of small organism segmentation under complex imaging conditions. Finally, this work improves the multiple object association method based on the segmentation module for the proposed system, to address object misdetection and overlapping problems. This system is then evaluated on zebrafish videos, Artemia franciscana videos and Daphnia magna videos, under a wide variety of (complex) video conditions, including shadowing, labels, and background artefacts (such as water bubbles of different sizes). The tracking accuracy of the proposed system outperforms three existing tracking systems. Thus, the work in this thesis has contributions in automatic cell and biological organism tracking, where the investigation studied the region-based segmentation dataset construction generalised for biological organisms, intensity contrast enhancement for micrographs, segmentation improvement by removing imaging constraints and the final tracking accuracy enhancement

Nanoparticles in medicine: Automating the analysis process of high-throughput microscopy data.

Automated tracking of cells across timelapse microscopy image sequences typically employs complex segmentation routines and/or bio-staining of the tracking objective. Often accurate identification of a cell's morphology is not of interest and the accurate segmentation of cells in pursuit of non-morphological parameters is complex and time consuming. This thesis explores the potential of internalized quantum dot nanoparticles as alternative, bio- and photo-stable optical markers for tracking the motions of cells through time. CdTe/ZnS core-shell quantum dots act as nodes in moving light display networks within A549, epithelial, lung cancer cells over a 40 hour time period. These quantum dot fluorescence sources are identified and interpreted using simplistic algorithms to find consistent, non-subjective centroids that represent cell centre locations. The presented tracking protocols yield an approximate 91% success rate over 24 hours and 78% over the full 40 hours. The nanoparticle moving light displays also provide simultaneous collection of cell motility data, resolution of mitotic traversal dynamics and identification of familial relationships enabling the construction of multi-parameter lineage trees. This principle is then developed further through inclusion of 3 different coloured quantum dots to create cell specific colour barcodes and reduce the number of time points necessary to successfully track cells through time. The tracking software and identification of parameters without detailed morphological knowledge is also demonstrated through automated extraction of DOX accumulation profiles and Cobalt agglomeration accruement statistics from two separate toxicology assays without the need for cell segmentation