Comparison of Cost Function Against Positional Analysis for Automated Tracking of Three-Cell Interactions

Localization and tracking of cells generates raw digital information from microscopy images, including images of stained nuclei and highly precise determination of central positions of cells, which can be analyzed for investigation of cell motility. In a previous study by this group, an algorithm termed automated contour-based tracking for in vitro environments (ACTIVE) was established for tracking large cell populations for long periods of time. For the two-cell interaction events on which ACTIVE was initially focused, error rate was reduced as much as 43% compared to a traditional positional analysis algorithm by Kilfoil and colleagues. In the present thesis, we investigated whether the ACTIVE algorithm could be improved when applied to a more complicated condition: three-cell interactions. To determine whether modification of the ACTIVE algorithms could allow ACTIVE to outperform the Kilfoil benchmark method when applied not only to two-cell interaction cases but also to three-cell interaction cases, two approaches were developed and studied: 1) optimization of the existing ACTIVE cost-function weighting factors by orthogonal design with addition of two new factors, velocity and directionality, and detection of ranges and effects for all factors, and 2) modification of the circumstances under which the Kilfoil approach and the cost function approach were executed. We found the position factor to be the most important and accurate among all the factors, and optimized all factors. What is more, the directionality was determined to be the second most significant factor of the cost function for correctly tracking cells. However, modification of neither the position nor directionality factor could achieve higher accuracy than the Kilfoil method. Having evaluated the new strategy that combines both the cost function and the Kilfoil method, we found that the new strategy did not result in higher accuracy for three-cell interactions, as compared to the pure Kilfoil benchmark method. The accuracy of the new strategy was 6% lower on average than the Kilfoil method. Although the results of the present work do not yet achieve a method for analysis of three-cell interactions that outperforms purely positional analysis, the work provides a method for optimization of the cost function and new understanding of characteristics of three-cell interactions that lead to reduced accuracy in the cost function and/or positional (Kilfoil) approaches

Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution.

The early detection of relapse following primary surgery for non-small-cell lung cancer and the characterization of emerging subclones, which seed metastatic sites, might offer new therapeutic approaches for limiting tumour recurrence. The ability to track the evolutionary dynamics of early-stage lung cancer non-invasively in circulating tumour DNA (ctDNA) has not yet been demonstrated. Here we use a tumour-specific phylogenetic approach to profile the ctDNA of the first 100 TRACERx (Tracking Non-Small-Cell Lung Cancer Evolution Through Therapy (Rx)) study participants, including one patient who was also recruited to the PEACE (Posthumous Evaluation of Advanced Cancer Environment) post-mortem study. We identify independent predictors of ctDNA release and analyse the tumour-volume detection limit. Through blinded profiling of postoperative plasma, we observe evidence of adjuvant chemotherapy resistance and identify patients who are very likely to experience recurrence of their lung cancer. Finally, we show that phylogenetic ctDNA profiling tracks the subclonal nature of lung cancer relapse and metastasis, providing a new approach for ctDNA-driven therapeutic studies

An investigation of automatic processing techniques for time-lapse microscope images

The analysis of time-lapse microscope images is a recent popular research topic. Processing techniques have been employed in such studies to extract important information about cells—e.g., cell number or alterations of cellular features—for various tasks. However, few studies provide acceptable results in practical applications because they cannot simultaneously solve the core challenges that are shared by most cell datasets: the image contrast is extremely low; the distribution of grey scale is non-uniform; images are noisy; the number of cells is large, etc. These factors also make manual processing an extremely laborious task. To improve the efficiency of related biological analyses and disease diagnoses. This thesis establishes a framework in these directions: a new segmentation method for cell images is designed as the foundation of an automatic approach for the measurement of cellular features. The newly proposed segmentation method achieves substantial improvements in the detection of cell filopodia. An automatic measuring mechanism for cell features is established in the designed framework. The measuring component enables the system to provide quantitative information about various cell features that are useful in biological research. A novel cell-tracking framework is constructed to monitor the alterations of cells with an accuracy of cell tracking above 90%. To address the issue of processing speed, two fast-processing techniques have been developed to complete edge detection and visual tracking. For edge detection, the new detector is a hybrid approach that is based on the Canny operator and fuzzy entropy theory. The method calculates the fuzzy entropy of gradients from an image to decide the threshold for the Canny operator. For visual tracking, a newly defined feature is employed in the fast-tracking mechanism to recognize different cell events with tracking accuracy: i.e., 97.66%, and processing speed, i.e., 0.578s/frame

Accomplishments and challenges in stem cell imaging in vivo.

Stem cell therapies have demonstrated promising preclinical results, but very few applications have reached the clinic owing to safety and efficacy concerns. Translation would benefit greatly if stem cell survival, distribution and function could be assessed in vivo post-transplantation, particularly in patients. Advances in molecular imaging have led to extraordinary progress, with several strategies being deployed to understand the fate of stem cells in vivo using magnetic resonance, scintigraphy, PET, ultrasound and optical imaging. Here, we review the recent advances, challenges and future perspectives and opportunities in stem cell tracking and functional assessment, as well as the advantages and challenges of each imaging approach

Fuzzy-based Propagation of Prior Knowledge to Improve Large-Scale Image Analysis Pipelines

Many automatically analyzable scientific questions are well-posed and offer a variety of information about the expected outcome a priori. Although often being neglected, this prior knowledge can be systematically exploited to make automated analysis operations sensitive to a desired phenomenon or to evaluate extracted content with respect to this prior knowledge. For instance, the performance of processing operators can be greatly enhanced by a more focused detection strategy and the direct information about the ambiguity inherent in the extracted data. We present a new concept for the estimation and propagation of uncertainty involved in image analysis operators. This allows using simple processing operators that are suitable for analyzing large-scale 3D+t microscopy images without compromising the result quality. On the foundation of fuzzy set theory, we transform available prior knowledge into a mathematical representation and extensively use it enhance the result quality of various processing operators. All presented concepts are illustrated on a typical bioimage analysis pipeline comprised of seed point detection, segmentation, multiview fusion and tracking. Furthermore, the functionality of the proposed approach is validated on a comprehensive simulated 3D+t benchmark data set that mimics embryonic development and on large-scale light-sheet microscopy data of a zebrafish embryo. The general concept introduced in this contribution represents a new approach to efficiently exploit prior knowledge to improve the result quality of image analysis pipelines. Especially, the automated analysis of terabyte-scale microscopy data will benefit from sophisticated and efficient algorithms that enable a quantitative and fast readout. The generality of the concept, however, makes it also applicable to practically any other field with processing strategies that are arranged as linear pipelines.Comment: 39 pages, 12 figure