The group’s vision and aim are development of novel clinical data analysis algorithms to extract meaningful information which enables detection, characterization, and quantification of clinically significant information. Our group aims to:
Traumatic brain injury (TBI) is a leading cause of death and disability. At the time of the traumatic impact, primary injury is enforced by the transfer of kinetic forces causing neurological damage. Secondary alterations of the TBI comprises of consecutive pathological processes initiated at the moment-of-injury and progressive tissue damage in the form of reduced cerebral blood flow, cerebral edema, blood-brain barrier damage, cell apoptosis and necrosis, and neuroinflammation, all of which form a very complex environment with a multitude of contrasts on MRI scans.
Areas of ongoing secondary injury are referred to as the traumatic penumbra—perilesional areas that are dynamic and constantly changing with time around the necrotic core and have the potential to recover. In the absence of timely intervention, the affected brain tissue, which could have recuperated would become an irreversible injury. Hence, accurate assessment of lesion area and its sub-regions, including penumbra’s existence is crucial for early intervention. In this study, we have developed a deep-learning framework to handle multi-contrast MR for TBI lesion and sub-region segmentation to track the pathophysiological changes in TBI for early intervention.
Brain aging is different from chronological ageing and indicates individuals brain health. Brain age is a dynamic factor and accelerated brain ageing can be reversed using a targeted therapy. Owing to current paucity of AI-augmented advanced brain-structural changes quantifying platform, radiologists has since been using visual inspection during their routine practice. This is a subjective way of extracting information of brain atrophy/neurodegeneration and poses challenge even to the neuroradiologists.
An AI-augmented brain volumetry-based brain age detection platform (so-called ‘SaMD’) is being developed using normative population data in Singapore or ‘SG-brain’ template. This template will help to diagnose accelerated brain aging and facilitates differentiation from normal brain ageing. Many brain ageing studies were available for the Caucasian population. Owing to the difference in normative aging between the Caucasian and the Singapore population, there is a need to develop a normative atlas to detect accelerated brain aging in Singapore. It will serve as a local reference for normative brain volumetry leading to early detection, early intervention, and effective management.
Schizophrenia is a major psychiatric disorder that imposes enormous clinical burden on patients and their caregivers. Determining classification biomarkers can complement clinical measures and improve understanding of the neural basis underlying schizophrenia. Using neuroanatomical features, several machine learning-based investigations have attempted to classify schizophrenia from healthy controls, but the range of neuroanatomical measures employed have been limited in range to date. In this study, we sought to classify schizophrenia and healthy control cohorts using a diverse set of neuroanatomical measures (cortical and subcortical volumes, cortical areas and thickness, cortical mean curvature) and adopted Ensemble methods for better performance. Additionally, we correlated such neuroanatomical features with Quality of Life (QoL) assessment scores within the schizophrenia cohort. With Ensemble methods and diverse neuroanatomical measures, we achieved classification accuracies ranging from 83 to 87%, sensitivities and specificities varying between 90–98% and 65–70% respectively. In addition to lower QoL scores within schizophrenia cohort, significant correlations were found between specific neuroanatomical measures and psychological health, social relationship subscale domains of QoL. Our results suggest the utility of inclusion of subcortical and cortical measures and Ensemble methods to achieve better classification performance and their potential impact of parsing out neurobiological correlates of quality of life in schizophrenia.
In this study, we are working on
With the increasing prevalence of obesity and its tremendous health effects, it is important to invest time and effort into research of obesity and its associated diseases to develop better care and prevention. Worldwide, there are many clinical trials like – GENYAL (prevention of obesity in childhood); SWITCH (Community based obesity prevention trial); Clinical trial exploring exercise intervention on obese women; SAMS (Singapore adult Metabolism Study); GUSTO (Growing towards healthy outcome in Singapore); etc. and many countries like Singapore have declared war on obesity. Understanding the phenotypes and genotypes of obesity is crucial for profiling and management of the condition. Quantification of obesity in-vivo, helps monitoring the changes due to interventions, visualization of fat compartments, inter-ethnicity differences, inter – and intra-subject changes etc. which are part of the large cohort studies. In large cohort studies there is data deluge, and we need a comprehensive tool which includes (i) Accurate, rapid, and repeatable segmentation module (ii) Module for visualization of data and statistics and (iii) A correction tool to correct for errors if any.
Figure 6. End to End Human Metabolic Fat Analysis Framework.
In this study, we propose “CAFT: Comprehensive Abdominal fat segmentation tool” which comprises of a deep learning framework “Global aggregation UNet with self-attention (GA-UNet)” to accurately segment all the three compartments SSAT, DSAT and VAT simultaneously using a single network architecture. Dashboard for data presentation and analytics – which allows automatic lumbar based quantification and analysis; 3D visualization; Percentage and volumetric analysis of whole abdomen or lumbar based fat and an Interactive correction tool to edit the contours (between background & outer abdominal boundary; between SSAT & DSAT – correction of Fascia Superficialis and inner abdominal boundary which separates SAT and VAT) to correct the errors in segmentation.
The proposed study aims to speed up segmentation and improve interobserver agreement in CMR imaging analysis. Automated analysis via machine learning show potential and could remove this intra- and inter-observer variation. We seek to investigate an AI algorithm for LV segmentation and function analysis to achieve robust automated results in other aspects of CMR analysis such as infarct size/T1/T2/LGE analysis. To reduce the time spent by radiologists to process data and minimize intra- and inter-observer variability, we propose a fully automated multi-scan CMR image analysis pipeline. The refined and iterated pipeline proposed aims to have equivalent precision (scan-rescan reproducibility) to a human expert with global standardization using multicentre, international, de-identified trial data from IMMACULATE/PITRI/Platelet-STEMI cohort to train further iterations. The development of this fully automated multi-scan CMR image analysis pipeline has immense translational potential and will enhance the research ecosystem in the field of ML and AI. Future directions include its application to the Singapore Biobank to develop normal and disease measurement ranges for Asian cohort, licensing to CVI42 and other image analysis vendors, provision of research CMR Core lab service with superior efficiency to international trial groups.
Background: Cardiac Magnetic Resonance (CMR) sequences are commonly used to obtain a complete description of the function and structure of the heart, provided that accurate measurements are extracted from images. New methods of extraction of information are being developed, among them, deep neural networks are powerful tools that showed the ability to perform fast and accurate segmentation. To reduce the time spent by radiologists to process data and minimize intra- and inter-observer variability, we propose a fully automatic multi-scan CMR image analysis pipeline.
Methods: Sequence specific U-Net 2D models were trained to perform the segmentation of the left ventricle (LV), right ventricle (RV) and aorta in cine short-axis, late gadolinium enhancement, sequences depending on the need. The models were trained and tested on a set of data manually segmented by experts using semi-automatic and manual tools. A set of parameters were computed from the resulting segmentations, such as the left ventricle ejection fraction (EF), scar percentage. The Dice similarity coefficient, Hausdorff distance, mean surface distance, and Pearson correlation coefficient R were used to assess and compare the results of the U-Net-based pipeline with intra-observer variability.
Fat is not created equal. Just as there are different types of dietary fat, the human body also has different types of fat cells. Fat or adipose tissue is composed of white and brown fat – white fat stores excess energy while brown fat dissipates excess energy or fat storage as heat through a process called thermogenesis.
Brown fat or brown adipose tissue (BAT) was traditionally believed to be present abundantly only in infants and to progressively regress following infancy. Recently, studies have shown that BAT persists into adulthood. However, it’s noted that less brown fat can be found in obese individuals. BAT has since received a great deal of attention as an appealing target to combat obesity and metabolic diseases – such as diabetes – due to its ability to use body fat stores as fuel.
Research suggests that BAT is a major contributor to the regulation of energy metabolism in the body. Brown fat, when activated, increases thermogenesis by stimulating fatty acid oxidation and thereby increasing energy dissipation. BAT also possesses a great capacity for glucose uptake from the circulation, thereby keeping blood sugar levels balanced and improving insulin sensitivity – both of which are beneficial to an individual’s health. These properties suggest that brown fat might have an important role in whole-body metabolism.
The most commonly used method for estimating activity and quantity of brown fat in humans has been positron emission tomography (PET) combined with computed tomography (CT), which involves significant doses of radiation. Therefore, most previous studies on brown fat were done in patients requiring PET/CT scans for medical indications, limiting its applicability to the general population and to its use in children.
We investigated the influence of brown fat on adiposity in 198 young Asian preschool children (at 4.5 years of age) from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) study using magnetic resonance imaging (MRI), an alternative technique to identifying brown fat without radiation. Our study is the first to examine brown fat in relation to metabolic health in a cohort of children from a general population.
The current available applied methods to identify brown fat in humans are PET/CT and infrared thermography. These techniques generally detect activated brown fat, but do not detect inactive or weakly activated brown fat. Our findings confirm that identification and quantification of brown fat is possible with MRI and strengthen the proposition of MRI as an alternative imaging technique to characterise brown fat without any form of activation. This paves a new and effective way for future studies in identifying brown fat in healthy populations, children and in longitudinal studies without applying methods involving radiation. Figure 8 explain the brown adipose tissue quantification & population template generation framework.
Figure 8. Brown adipose tissue quantification & population template generation.
Bhanu Prakash K.N. obtained Ph.D. degree from Indian Institute of Science, Bangalore in 2002. He was a Senior grade lecturer in Visvesvaraya Technological University before joining Biomedical Imaging Lab in Institute of Infocomm Research (Agency for Science, Technology and Research – A*STAR, Singapore) in 2003 and later moved to Bioinformatics Institute in 2004 as Research Scientist. He then moved to Singapore Bioimaging Consortium (SBIC, A*STAR) in 2005 and was part of the Biomedical Imaging Lab. Later in 2012, he moved to Laboratory of metabolic imaging and was promoted as Group Leader of Signal & Image processing Group in 2006. He has more than 40 publications in peer-reviewed journals and 13 patents granted (4 patents have been licensed to a spin-off company and 2 patents to a start-up) and about 70 abstracts along with a few media releases.His main interests are in Biomedical signal & Image analysis, Pattern Recognition, AI, Clinical decision support systems, Differential diagnosis, Radiomics and Population based studies in Neuro and precision medicine. He is actively involved with RadiologyAsia and SGCR-WIRES activities and a member of ISMRM and IEEE. He has delivered many lectures and seminars in Singapore and overseas. In addition to his scientific contributions, he has organized and chair-ed several symposiums and seminars over the years. He serves as a reviewer to Neurotrauma, IJCARS, Scientific Reports, MDPI, Journal of Electronics, MAGMA and many other journals. He is the guest editor for “Novel Technologies on Image and Signal Processing” a special issue by MDPI Electronics and a member of Editorial board for Artificial Intelligence in Medical Imaging (AIMI).
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