Wavelet Energy Guided Level Set Based Active Contour - A Novel Method To Segment Highly Similar Intensity Regions

Segmentasi imej adalah salah satu peringkat permulaan yang paling penting dalam sistem pengesanan berbantukan komputer yang mempermudahkan pengesanan, pengecaman dan pengukuran objek selanjutnya. Image segmentation is one of the most important preliminary stages in computer-aided diagnosis system that facilitates further object identification, recognition, and quantification

Hippocampus Segmentation Using Locally Integrated Prior-Based Level Set Guided By Assembled And Weighted Coherent Point Drift Registration

Segmentasi hipokampus daripada struktur-struktur subkortikal otak bersebelahan merupakan satu tugas yang sangat mencabar, terutamanya akibat sempadan pemisahan struktur-struktur ini adalah lemah atau kurang jelas, seterusnya menyebabkan pendekatan berasaskan sempadan tidak berkesan untuk segmentasi hipokampus yang betul. Disamping itu, kedudukan hipokampus yang hampir dengan amygdala menyukarkan lagi isu segmentasi. Walau bagaimanapun, trend terkini telah beralih dari bergantung semata-mata kepada ciri-ciri imej kepada penggunaan model-model terdahulu dalam segmentasi. Secara amnya, model-model terdahulu dibina menggunakan segmentasi berasaskan atlas. Walau bagaimanapun, pendekatan ini sangat data intensif kerana ia menggunakan kaedah berasaskan volumetri untuk pembinaan model terdahulu. Oleh yang demikian, tesis ini mencadangkan satu pendekatan pembinaan model terdahulu yang bukan sahaja mampu mewakilkan maklumat bentuk dan lokasi ruang secara berkesan, malah mempunyai keperluan data intensif yang lebih rendah berbanding pendekatan berasaskan atlas. Secara terperinci, satu kaedah pendaftaran set titik yang novel dicadangkan dan disahkan bagi pembinaan model terdahulu. Hippocampus segmentation from neighbouring brain subcortical structures is a very challenging task mainly because boundaries separating these structures are weak or unclear, rendering conventional edge-based approaches ineffective for proper hippocampus segmentation. Besides that, close proximity of the hippocampus with the amygdala further complicates the segmentation issue. Recent trends, however have shifted from sole reliance on image features to utilization of prior models in the segmentation. Predominantly, the prior models are constructed using atlas-based segmentation. This approach however, is highly data intensive due to the volumetric-based methods used for prior model construction. Consequently, this thesis proposes a prior model construction method that not only effectively represents shape and spatial location information, but also requires lower data intensiveness compared to atlas-based approaches. Specifically, a novel point set registration method is proposed and validated for prior model construction

Reduced cerebral vascular fractal dimension among asymptomatic individuals as a potential biomarker for cerebral small vessel disease

Cerebral small vessel disease is a neurological disease frequently found in the elderly and detected on neuroimaging, often as an incidental finding. White matter hyperintensity is one of the most commonly reported neuroimaging markers of CSVD and is linked with an increased risk of future stroke and vascular dementia. Recent attention has focused on the search of CSVD biomarkers. The objective of this study is to explore the potential of fractal dimension as a vascular neuroimaging marker in asymptomatic CSVD with low WMH burden. Df is an index that measures the complexity of a self-similar and irregular structure such as circle of Willis and its tributaries. This exploratory cross-sectional study involved 22 neurologically asymptomatic adult subjects (42 ± 12 years old; 68% female) with low to moderate 10-year cardiovascular disease risk prediction score (QRISK2 score) who underwent magnetic resonance imaging/angiography (MRI/MRA) brain scan. Based on the MRI findings, subjects were divided into two groups: subjects with low WMH burden and no WMH burden, (WMH+; n = 8) and (WMH−; n = 14) respectively. Maximum intensity projection image was constructed from the 3D time-of-flight (TOF) MRA. The complexity of the CoW and its tributaries observed in the MIP image was characterised using Df. The Df of the CoW and its tributaries, i.e., Df (w) was significantly lower in the WMH+ group (1.5172 ± 0.0248) as compared to WMH− (1.5653 ± 0.0304, p = 0.001). There was a significant inverse relationship between the QRISK2 risk score and Df (w), (rs = −.656, p = 0.001). Df (w) is a promising, non-invasive vascular neuroimaging marker for asymptomatic CSVD with WMH. Further study with multi-centre and long-term follow-up is warranted to explore its potential as a biomarker in CSVD and correlation with clinical sequalae of CSVD

Dynamic Phenotypic Switching and Group Behavior Help Non-Small Cell Lung Cancer Cells Evade Chemotherapy

Drug resistance, a major challenge in cancer therapy, is typically attributed to mutations and genetic heterogeneity. Emerging evidence suggests that dynamic cellular interactions and group behavior also contribute to drug resistance. However, the underlying mechanisms remain poorly understood. Here, we present a new mathematical approach with game theoretical underpinnings that we developed to model real-time growth data of non-small cell lung cancer (NSCLC) cells and discern patterns in response to treatment with cisplatin. We show that the cisplatin-sensitive and cisplatin-tolerant NSCLC cells, when co-cultured in the absence or presence of the drug, display dynamic group behavior strategies. Tolerant cells exhibit a \u27persister-like\u27 behavior and are attenuated by sensitive cells; they also appear to \u27educate\u27 sensitive cells to evade chemotherapy. Further, tolerant cells can switch phenotypes to become sensitive, especially at low cisplatin concentrations. Finally, switching treatment from continuous to an intermittent regimen can attenuate the emergence of tolerant cells, suggesting that intermittent chemotherapy may improve outcomes in lung cancer

Monitoring and Determining Mitochondrial Network Parameters in Live Lung Cancer Cells

Mitochondria are dynamic organelles that constantly fuse and divide, forming dynamic tubular networks. Abnormalities in mitochondrial dynamics and morphology are linked to diverse pathological states, including cancer. Thus, alterations in mitochondrial parameters could indicate early events of disease manifestation or progression. However, finding reliable and quantitative tools for monitoring mitochondria and determining the network parameters, particularly in live cells, has proven challenging. Here, we present a 2D confocal imaging-based approach that combines automatic mitochondrial morphology and dynamics analysis with fractal analysis in live small cell lung cancer (SCLC) cells. We chose SCLC cells as a test case since they typically have very little cytoplasm, but an abundance of smaller mitochondria compared to many of the commonly used cell types. The 2D confocal images provide a robust approach to quantitatively measure mitochondrial dynamics and morphology in live cells. Furthermore, we performed 3D reconstruction of electron microscopic images and show that the 3D reconstruction of the electron microscopic images complements this approach to yield better resolution. The data also suggest that the parameters of mitochondrial dynamics and fractal dimensions are sensitive indicators of cellular response to subtle perturbations, and hence, may serve as potential markers of drug response in lung cancer

Monitoring and Determining Mitochondrial Network Parameters in Live Lung Cancer Cells.

Mitochondria are dynamic organelles that constantly fuse and divide, forming dynamic tubular networks. Abnormalities in mitochondrial dynamics and morphology are linked to diverse pathological states, including cancer. Thus, alterations in mitochondrial parameters could indicate early events of disease manifestation or progression. However, finding reliable and quantitative tools for monitoring mitochondria and determining the network parameters, particularly in live cells, has proven challenging. Here, we present a 2D confocal imaging-based approach that combines automatic mitochondrial morphology and dynamics analysis with fractal analysis in live small cell lung cancer (SCLC) cells. We chose SCLC cells as a test case since they typically have very little cytoplasm, but an abundance of smaller mitochondria compared to many of the commonly used cell types. The 2D confocal images provide a robust approach to quantitatively measure mitochondrial dynamics and morphology in live cells. Furthermore, we performed 3D reconstruction of electron microscopic images and show that the 3D reconstruction of the electron microscopic images complements this approach to yield better resolution. The data also suggest that the parameters of mitochondrial dynamics and fractal dimensions are sensitive indicators of cellular response to subtle perturbations, and hence, may serve as potential markers of drug response in lung cancer

Dynamic Phenotypic Switching and Group Behavior Help Non-Small Cell Lung Cancer Cells Evade Chemotherapy

Drug resistance, a major challenge in cancer therapy, is typically attributed to mutations and genetic heterogeneity. Emerging evidence suggests that dynamic cellular interactions and group behavior also contribute to drug resistance. However, the underlying mechanisms remain poorly understood. Here, we present a new mathematical approach with game theoretical underpinnings that we developed to model real-time growth data of non-small cell lung cancer (NSCLC) cells and discern patterns in response to treatment with cisplatin. We show that the cisplatin-sensitive and cisplatin-tolerant NSCLC cells, when co-cultured in the absence or presence of the drug, display dynamic group behavior strategies. Tolerant cells exhibit a ‘persister-like’ behavior and are attenuated by sensitive cells; they also appear to ‘educate’ sensitive cells to evade chemotherapy. Further, tolerant cells can switch phenotypes to become sensitive, especially at low cisplatin concentrations. Finally, switching treatment from continuous to an intermittent regimen can attenuate the emergence of tolerant cells, suggesting that intermittent chemotherapy may improve outcomes in lung cancer

Detecting global and local hippocampal shape changes in Alzheimer's disease using statistical shape models

The hippocampus is affected at an early stage in the development of Alzheimer's disease (AD). With the use of structural magnetic resonance (MR) imaging, we can investigate the effect of AD on the morphology of the hippocampus. The hippocampal shape variations among a population can be usually described using statistical shape models (SSMs). Conventional SSMs model the modes of variations among the population via principal component analysis (PCA). Although these modes are representative of variations within the training data, they are not necessarily discriminative on labeled data or relevant to the differences between the sub-populations. We use the shape descriptors from SSM as features to classify AD from normal control (NC) cases. In this study, a Hotelling's T-2 test is performed to select a subset of landmarks which are used in PCA. The resulting variation modes are used as predictors of AD from NC. The discrimination ability of these predictors is evaluated in terms of their classification performances with bagged support vector machines (SVMs). Restricting the model to landmarks with better separation between AD and NC increases the discrimination power of SSM. The predictors extracted on the subregions also showed stronger correlation with the memory-related measurements such as Logical Memory, Auditory Verbal Learning Test (AVLT) and the memory subscores of Alzheimer Disease Assessment Scale (ADAS). Crown Copyright (C) 2011 Published by Elsevier Inc. All rights reserved