White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach

White Matter (WM) lesions are diffuse white matter abnormalities, that appear as hyperintense (bright) regions in cranial Magnetic Resonance Imaging (MRI). WM lesions are often observed in older population and are important indicators of stroke, multiple sclerosis, dementia and other brain-related disorders. Manual detection of WM lesions is laborious and the currently adopted visual scoring approaches for lesion grading is very subjective. In this thesis, a new approach for automated WM Lesions Segmentation is presented. In the proposed approach, the presence of WM lesions is detected as outliers in the intensity distribution of the Fluid Attenuated Inversion Recovery (FLAIR) MR images using an Adaptive Outlier Detection technique

Quantitative 3D analysis of complex single border cell behaviors in coordinated collective cell migration

Understanding the mechanisms of collective cell migration is crucial for cancer metastasis, wound healing and many developmental processes. Imaging a migrating cluster in vivo is feasible, but the quantification of individual cell behaviours remains challenging. We have developed an image analysis toolkit, CCMToolKit, to quantify the Drosophila border cell system. In addition to chaotic motion, previous studies reported that the migrating cells are able to migrate in a highly coordinated pattern. We quantify the rotating and running migration modes in 3D while also observing a range of intermediate behaviours. Running mode is driven by cluster external protrusions. Rotating mode is associated with cluster internal cell extensions that could not be easily characterized. Although the cluster moves slower while rotating, individual cells retain their mobility and are in fact slightly more active than in running mode. We also show that individual cells may exchange positions during migration

Geometric constraints alter cell arrangements within curved epithelial tissues

Organ and tissue formation are complex three-dimensional processes involving cell division, growth, migration, and rearrangement, all of which occur within physically constrained regions. However, analyzing such processes in three dimensions in vivo is challenging. Here, we focus on the process of cellularization in the anterior pole of the early Drosophila embryo to explore how cells compete for space under geometric constraints. Using microfluidics combined with fluorescence microscopy, we extract quantitative information on the three-dimensional epithelial cell morphology. We observed a cellular membrane rearrangement in which cells exchange neighbors along the apical-basal axis. Such apical-to-basal neighbor exchanges were observed more frequently in the anterior pole than in the embryo trunk. Furthermore, cells within the anterior pole skewed toward the trunk along their long axis relative to the embryo surface, with maximum skew on the ventral side. We constructed a vertex model for cells in a curved environment. We could reproduce the observed cellular skew in both wild-type embryos and embryos with distorted morphology. Further, such modeling showed that cell rearrangements were more likely in ellipsoidal, compared with cylindrical, geometry. Overall, we demonstrate that geometric constraints can influence three-dimensional cell morphology and packing within epithelial tissues

Endocytic reawakening of motility in jammed epithelia.

Dynamics of epithelial monolayers has recently been interpreted in terms of a jamming or rigidity transition. How cells control such phase transitions is, however, unknown. Here we show that RAB5A, a key endocytic protein, is sufficient to induce large-scale, coordinated motility over tens of cells, and ballistic motion in otherwise kinetically arrested monolayers. This is linked to increased traction forces and to the extension of cell protrusions, which align with local velocity. Molecularly, impairing endocytosis, macropinocytosis or increasing fluid efflux abrogates RAB5A-induced collective motility. A simple model based on mechanical junctional tension and an active cell reorientation mechanism for the velocity of self-propelled cells identifies regimes of monolayer dynamics that explain endocytic reawakening of locomotion in terms of a combination of large-scale directed migration and local unjamming. These changes in multicellular dynamics enable collectives to migrate under physical constraints and may be exploited by tumours for interstitial dissemination

Automatic white matter lesions detection and segmentation of brain magnetic resonance images

White matter lesions (WML) are frequently associated with neuronal degeneration in ageing and can be an important indicator of stroke, multiple sclerosis, dementia and other brain-related disorders. WML can be readily detected on Magnetic Resonance Imaging (MRI), but manual delineation of lesions by neuroradiologists is a time consuming and laborious task. Furthermore, MRI intensity scales are not standardised and do not have tissue-specific interpretation, leading to WML quantification inaccuracies and difficulties in interpreting their pathological relevance. Numerous studies have shown tremendous advances in WML segmentation, but flow artefact, image noise, incomplete skull stripping and inaccurate WML classification continue to yield False Positives (FP) that have limited the reliability and clinical utility of these approaches. The present study proposed a new MRI intensity standardisation and clustered texture feature method based on the K-means clustering algorithm. Enhanced clustered texture features and histogram features were constructed based on the proposed standardisation method to significantly reduce FP through a Random Forest algorithm. Subsequently, a local outlier identification method further refined the boundary of WML for the final segmentation. The method was validated with a test set of 32 scans (279 images), with a significant correlation coefficient (R=0.99574, p-value < 0.001) between the proposed method and manual delineation by a neuroradiologist. Furthermore, comparison against three state-of-the-art methods for the 32 scans demonstrated that the proposed method outperformed five of seven well-known evaluation metrics. This improved specificity in WML segmentation may thus improve the quantification of clinical WML burden to assess for correlations between WML load and distribution with neurodenegerative disease

Automatic white matter lesion detection and segmentation on Magnetic Resonance Imaging: A review of past and current state-of-the-art

White matter lesion (WML) is an abnormal tissue occurring in white matter. It indicated the damage of the myelin sheath that used to surround the axon of a neurone. This resulting neurological and vascular disorder occur in the patient, also commonly developed in the healthy brain of elderly. Magnetic Resonance Imaging is a non-invasive medical equipment preferred choice by the clinician to diagnose and observed the injury of brain tissue. However, WML quantitative assessment and analyse on the large volume of MR imaging is a challenge. In this paper, we provide an intensive review of the past and recent WML delineation and detection methods. This review included visual scoring assessment, a common preprocessing step for WML segmentation, false positive elimination, and the latest automatic WML segmentation approaches will be presented

Gaussian mixture model - Expectation maximization algorithm for brain images

Segmentation of human brain can be performed with the aid of mathematical algorithm as well as computer-based system to assist radiologists and medical related profession to monitor the condition of one's brain comprehensively. Due to the complex structure of the human brain, one cannot simply analyze them just by looking at the MRI images. This research examines the brain segmentation and the validation of the segmentation using ground truth data for seven subjects. The segmentation of brain regions such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) can be accomplished by using Gaussian Mixture Model (GMM) and Expectation-Maximization (EM) Algorithm. The results of segmentation are shown by the Gaussian distribution graph that indicates the volume of brain regions. The segmentation results are validated by the value of Dice index, Jaccard index, and positive predictive value (PPV). It is found that all seven subjects have high value for every index as the values ranging from more than 0.6 to almost approaching 1. For all subjects, the lowest percentage for Dice is 77.82% while the highest is 84.28%, the lowest percentage for Jaccard is 63.70% while the highest is 72.84%, and the lowest percentage for PPV is 94.44% while the highest is 98.75%. In conclusion, the index values for all subjects are acceptable and this means the segmentation by using GMM and EM Algorithm is accurate after going through the process of validation of segmentation

Automatic DNA replication tract measurement to assess replication and repair dynamics at the single-molecule level

DNA fibre assay has a potential application in genomic medicine, cancer and stem cell research at the single-molecule level. A major challenge for the clinical and research implementation of DNA fibre assays is the slow speed in which manual analysis takes place as it limits the clinical actionability. While automatic detection of DNA fibres speeds up this process considerably, current publicly available software have limited features in terms of their user interface for manual correction of results, which in turn limit their accuracy and ability to account for atypical structures that may be important in diagnosis or investigative studies. We recognize that core improvements can be made to the GUI to allow for direct interaction with automatic results to preserve accuracy as well as enhance the versatility of automatic DNA fibre detection for use in variety of situations.Ministry of Education (MOE)Ministry of Health (MOH)National Research Foundation (NRF)Published versionThis work was supported by National Research Foundation Singapore, Clinician Scientist Award [NMRC/CSA-INV/0017/2017, MOH-000654] and administered by the Singapore Ministry of Health’s National Medical Research Council; and the Ministry of Education, Singapore, Academic Research Fund Tier 1 [2019-T1-001-018]; the National Cancer Centre Research Fund Terry Fox Grant [NCCRF-YR2018-NOV-1]; and the Nanyang Technological University Start-Up Grant (to J.N.). This work was jointly supported by BII and IMCB, BMRC, A*STAR research funding, and A*STAR BMRC ATR Grant