Concavity Point and Skeleton Analysis Algorithm for Detection and Quantization in Heavily Clumped Red Blood Cells

In practice, most hospitals use light microscope to examine the smeared blood for blood quantification. This visual quantification is subjective, laborious and time-consuming. Although automating the process is a good solution, the available techniques are unable to count or ignore the clumpy red blood cells (RBC). Moreover, clumping cell can affect the whole counting process of RBC as well as their accuracy. This paper proposes a new quantization process called concavity point and skeleton analysis (CP-SA) for heavily clump RBC. The proposed methodology is based on induction approach, enhanced lime blood cell by using gamma correction to get the appropriate edges. Then, splitting the clump and single cells by calculating each object area in pixel. Later, the quantification of clumpy cells with the proposed CP-SA method is done. This algorithm has been tested on 556 clump RBC taken from thin blood smear images under light microscope. All dataset images are captured from Hematology Unit, UKM Medical Centre in Kuala Lumpur. On all tested images, the cells of interest are successfully detected and counted from those clump cells. A comparative study and analysis to evaluate the performance of the proposed algorithm in three levels of clump have been conducted. The first level was with two clumps, second level with three clumps and third level with four clumps. The counting number of clump cells has been analyzed using quantitative analysis, resulting in much better results compared to other recent algorithms. The comparison shows that the proposed method gives better precision result at all levels with respect to ground truth: two clump cells (92%), three clump cells (96%) and four clump cells (90%). The results prove that this study has successfully developed a new method to count heavily clump RBC more accurately in microscopic images. In addition, this can be considered as a low-cost solution for quantification in massive examination

Характеристика та аналіз Ki-67-імунореактивності в астроцитомах головного мозку

Метою даної роботи було визначення діагностичної значущості рівня експресії маркеру проліферації Ki-67 в клітинах астроцитом. При розподілі випадків (n=45) за ступенем злоякісності було відмічено подібний та відносно низький рівень експресії у гліомах, що відносяться до I та II рівнів злоякісності за ВООЗ, значимо нижчий ніж у групах ІІІ та ІV рівнів. Була виявлена значна варіабельність результатів, отриманих при дослідженні одного матеріалу різними методиками. Background. A significant problem of brain tumors diagnostics is the subjective histological criteria. A promising area of research is estimation of mitotic activity level, but counting mitotic figures can not be accurate in slides stained by hematoxylin and eosin and therefore immunohistochemical detection of expression Ki-67 is widely used. Despite considerable experience in using proliferative index, determined by using Ki-67-immunoreactivity, the technique requires further research for standardization and optimization. Objective. To determine the diagnostic value of the Ki-67 expression in astrocytomas. Methods. The study included 45 astrocytomas, which were received by biopsy or operation. Imunohistochemical determining of Ki-67-labeling was used for calculating of proliferation index. Statistical analysis was performed by nonparametric tests. Results. It was observed similar and relatively low levels of Ki-67 expression in astrocytoma Grade I and II (WHO), significantly lower than in groups III and IV levels. It was revealed significant differences between the results obtained by different researchers. Conclusion. The level of Ki-67 expression correlates with Grade of astrocytomas (p <0,05). Great overlaps of Ki-67 expression (between I and II, III and IV level) does not allow its use in the differentiation of Grades. Despite the strong statistically significant relationship (p <0,05) between the results obtained by different researchers, a significant difference between them requires consideration when comparing with other data

Characterization and analysis of Ki-67-immunoreactivity in brain astrocytoma

Background. A significant problem of brain tumors diagnostics is the subjective histological criteria. A promising area of research is estimation of mitotic activity level, but counting mitotic figures can not be accurate in slides stained by hematoxylin and eosin and therefore immunohistochemical detection of expression Ki-67 is widely used. Despite considerable experience in using proliferative index, determined by using Ki-67-immunoreactivity, the technique requires further research for standardization and optimization. Objective. To determine the diagnostic value of the Ki-67 expression in astrocytomas. Methods. The study included 45 astrocytomas, which were received by biopsy or operation. Imunohistochemical determining of Ki-67-labeling was used for calculating of proliferation index. Statistical analysis was performed by nonparametric tests. Results. It was observed similar and relatively low levels of Ki-67 expression in astrocytoma Grade I and II (WHO), significantly lower than in groups III and IV levels. It was revealed significant differences between the results obtained by different researchers. Conclusion. The level of Ki-67 expression correlates with Grade of astrocytomas (p <0,05). Great overlaps of Ki-67 expression (between I and II, III and IV level) does not allow its use in the differentiation of Grades. Despite the strong statistically significant relationship (p <0,05) between the results obtained by different researchers, a significant difference between them requires consideration when comparing with other data. Citation: Shpon‘ka IS, Shynkarenko TV, Poslavska OV. [Characterization and analysis of Ki-67-immunoreactivity in brain astrocytoma]. Morphologia. 2016;10(1):96-101. Ukrainian

Label-Free Optical Imaging and Quantitative Algorithms to Assess Living Biological Systems.

Non-perturbing tools that provide reliable, information-rich assessments of living biological systems can inform clinical practice and improve patient health. In this dissertation, we developed label-free nonlinear optical molecular imaging to provide spatially-resolved, non-perturbing, quantitative functional assessments of 1) living cell lines, 2) primary human cells, and 3) tissue-engineered constructs manufactured with primary cells. Quantitative analytic methods were developed to account for the high inter-patient variability in primary human cells freshly harvested from distinct donors. The FDA strictly regulates the manufacture of tissue-engineered constructs, requiring assessment of product effectiveness and safety prior to release for patient treatment. We addressed this clinical need by developing quantitative methods to assess local tissue structure and biochemistry using label-free nonlinear optical molecular microscopy. Optical measures characterized morphologic and functional differences between controls and stressed constructs. Rigorous statistical analysis accounted for variability between patients. The technique reliably differentiated controls from stressed constructs from 10 batches/patients with P-value < 0.01. Further, the optical metrics strongly correlated with a standard WST-1 cell viability assay (P-values < 0.001 for 5 batches/patients). Unlike the standard methods, which are reliable but destructive, label-free optical assessments are both non-invasive and reliable. Thus, such optical measures could serve as reliable manufacturing release criteria for cell-based tissue-engineered constructs prior to human implantation. Label-free fluorescence lifetime imaging microscopy (FLIM) images consist of spatial and temporal information. The traditional method to analyze FLIM is iterative fitting, which is time-consuming and requires prior knowledge of the sample. Clinical practitioners require an analytical and simple-to-operate method to interpret FLIM images. Thus, extended phasor analysis algorithms were developed. The algorithms characterized tissue constituents with better differentiation (P-value < 0.001 for 5 batches/patients) than the standard fitting method (P-value = 0.048 for 5 batches/patients). In addition, time-gated FLIM with various gating schemes was analyzed with the developed phasor analysis algorithms to monitor intracellular lifetime variation. In summary, the developed algorithms could advance future FLIM applications in clinic.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108858/1/lengleng_1.pd

Automatic Segmentation of Cells of Different Types in Fluorescence Microscopy Images

Recognition of different cell compartments, types of cells, and their interactions is a critical aspect of quantitative cell biology. This provides a valuable insight for understanding cellular and subcellular interactions and mechanisms of biological processes, such as cancer cell dissemination, organ development and wound healing. Quantitative analysis of cell images is also the mainstay of numerous clinical diagnostic and grading procedures, for example in cancer, immunological, infectious, heart and lung disease. Computer automation of cellular biological samples quantification requires segmenting different cellular and sub-cellular structures in microscopy images. However, automating this problem has proven to be non-trivial, and requires solving multi-class image segmentation tasks that are challenging owing to the high similarity of objects from different classes and irregularly shaped structures. This thesis focuses on the development and application of probabilistic graphical models to multi-class cell segmentation. Graphical models can improve the segmentation accuracy by their ability to exploit prior knowledge and model inter-class dependencies. Directed acyclic graphs, such as trees have been widely used to model top-down statistical dependencies as a prior for improved image segmentation. However, using trees, a few inter-class constraints can be captured. To overcome this limitation, polytree graphical models are proposed in this thesis that capture label proximity relations more naturally compared to tree-based approaches. Polytrees can effectively impose the prior knowledge on the inclusion of different classes by capturing both same-level and across-level dependencies. A novel recursive mechanism based on two-pass message passing is developed to efficiently calculate closed form posteriors of graph nodes on polytrees. Furthermore, since an accurate and sufficiently large ground truth is not always available for training segmentation algorithms, a weakly supervised framework is developed to employ polytrees for multi-class segmentation that reduces the need for training with the aid of modeling the prior knowledge during segmentation. Generating a hierarchical graph for the superpixels in the image, labels of nodes are inferred through a novel efficient message-passing algorithm and the model parameters are optimized with Expectation Maximization (EM). Results of evaluation on the segmentation of simulated data and multiple publicly available fluorescence microscopy datasets indicate the outperformance of the proposed method compared to state-of-the-art. The proposed method has also been assessed in predicting the possible segmentation error and has been shown to outperform trees. This can pave the way to calculate uncertainty measures on the resulting segmentation and guide subsequent segmentation refinement, which can be useful in the development of an interactive segmentation framework

Ultrasound Imaging

This book provides an overview of ultrafast ultrasound imaging, 3D high-quality ultrasonic imaging, correction of phase aberrations in medical ultrasound images, etc. Several interesting medical and clinical applications areas are also discussed in the book, like the use of three dimensional ultrasound imaging in evaluation of Asherman's syndrome, the role of 3D ultrasound in assessment of endometrial receptivity and follicular vascularity to predict the quality oocyte, ultrasound imaging in vascular diseases and the fetal palate, clinical application of ultrasound molecular imaging, Doppler abdominal ultrasound in small animals and so on

Quantitative PET and SPECT

Since the introduction of personalized medicine, the primary focus of imaging has moved from detection and diagnosis to tissue characterization, the determination of prognosis, prediction of treatment efficacy, and measurement of treatment response. Precision (personalized) imaging heavily relies on the use of hybrid technologies and quantitative imaging biomarkers. The growing number of promising theragnostics require accurate quantification for pre- and post-treatment dosimetry. Furthermore, quantification is required in the pharmacokinetic analysis of new tracers and drugs and in the assessment of drug resistance. Positron Emission Tomography (PET) is, by nature, a quantitative imaging tool, relating the time–activity concentration in tissues and the basic functional parameters governing the biological processes being studied. Recent innovations in single photon emission computed tomography (SPECT) reconstruction techniques have allowed for SPECT to move from relative/semi-quantitative measures to absolute quantification. The strength of PET and SPECT is that they permit whole-body molecular imaging in a noninvasive way, evaluating multiple disease sites. Furthermore, serial scanning can be performed, allowing for the measurement of functional changes over time during therapeutic interventions. This Special Issue highlights the hot topics on quantitative PET and SPECT