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 then follow comprising consecutive pathological processes initiated at the moment of injury and progressed 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. Therefore, the correct assessment of parts of the lesion area, including penumbra existence at an early stage and the development of means to identify it and monitor its progression is crucial.
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.
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.
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. In order 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 through the use of multicenter, 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 Corelab 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. In order 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), the right ventricle 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.
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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