Segmentation of haematopoeitic cells in bone marrow using circle detection and splitting techniques

pre-printBone marrow evaluation is indicated when peripheral blood abnormalities are not explained by clinical, physical, or laboratory findings. In this paper, we propose a novel method for segmentation of haematopoietic cells in the bone marrow from scanned slide images. Segmentation of clumped cells is a challenging problem for this application. We first use color information and morphology to eliminate red blood cells and the background. Clumped haematopoietic cells are then segmented using circle detection and a splitting algorithm based on the detected circle centers. The Hough Transform is used for circle detection and to find the number and positions of circle centers in each region. The splitting algorithm is based on detecting the maximum curvature points, and partitioning them based on information obtained from the centers of the circles in each region. The performance of the segmentation algorithm for haematopoietic cells is evaluated by comparing our proposed method with a hematologist's visual segmentation in a set of 3748 cells

Rouleaux red blood cells splitting in microscopic thin blood smear images via local maxima, circles drawing, and mapping with original RBCs.

Splitting the rouleaux RBCs from single RBCs and its further subdivision is a challenging area in computer-assisted diagnosis of blood. This phenomenon is applied in complete blood count, anemia, leukemia, and malaria tests. Several automated techniques are reported in the state of art for this task but face either under or over splitting problems. The current research presents a novel approach to split Rouleaux red blood cells (chains of RBCs) precisely, which are frequently observed in the thin blood smear images. Accordingly, this research address the rouleaux splitting problem in a realistic, efficient and automated way by considering the distance transform and local maxima of the rouleaux RBCs. Rouleaux RBCs are splitted by taking their local maxima as the centres to draw circles by mid-point circle algorithm. The resulting circles are further mapped with single RBC in Rouleaux to preserve its original shape. The results of the proposed approach on standard data set are presented and analyzed statistically by achieving an average recall of 0.059, an average precision of 0.067 and F-measure 0.063 are achieved through ground truth with visual inspection

Quantifying expression variability in single-cell RNA sequencing data

Transcriptional noise is an intrinsic feature of cell populations and plays a driving role in mammalian development, tissue homoeostasis and immune function. While expression heterogeneity, a phenotypic readout of transcriptional noise, has been broadly studied in prokaryotic model systems or by profiling individual genes, few whole-transcriptome studies in mammalian systems have been reported. The development of single-cell RNA sequencing technologies introduced powerful tools to investigate transcriptional differences between individual cells, therefore allowing the in-depth characterisation of expression variability. In this thesis, I computationally analysed single-cell RNA sequencing data to understand transcriptional variability and expanded a statistical model to avoid confounding effects when quantifying such variability. First, I profiled individual transcriptomes of CD4$^+$ T cells, identifying a global decrease in transcriptional variability upon immune activation. By extending this analysis across two sub-species of mice, I identified an evolutionarily conserved set of immune response genes for which transcriptional variability increases during ageing. I used a Bayesian modelling framework to quantify mean expression and transcriptional variability but due to a strong confounding effect between these two parameters, variability analysis was restricted to genes that are similarly expressed across the tested conditions. To address this problem, I extended the computational framework allowing the parallel assessment of changes in mean expression and variability. Within this Bayesian framework, I introduced a joint prior linking mean expression and variability parameters, which allowed a residual over-dispersion to be measured for each gene. This measure allowed me to statistically assess changes in variability even for genes with differences in mean expression between conditions. Finally, I applied the model to identify temporal changes in variability over the time-course of spermatogenesis. This unidirectional differentiation process involves several complex steps before mature sperm form from spermatogonial stem cells. When profiling changes in variability across this developmental time-course, peaks in variability are caused by rapid changes in gene expression along the differentiation trajectory. This thesis provides a deeper understanding of technical and biological factors that drive transcriptional variability and offers a basis for future research to characterise its role in health and disease.Funding was provided via the EMBL international PhD programm