Clinical Data Analytics & Radiomics

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:

• Develop new skills & knowledge in advanced multi-modal image (MR, CT, PET, Optical, Photoacoustic) data processing,
• Contribute to local research activities,
• Forge collaborations with clinical community and industries
• Develop new IP and publish in prestigious journals

Figure 1 – CDAR's capabilities

Computer-Aided Decision Support Systems – Preclinical/Clinical

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.

Figure 2 –

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.

Figure 3 – End-end DL framework for lesion segmentation on multi-contrast MR images

Comprehensive Abdominal fat Analysis tool for Geriatric cohort study - TTSH

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.

Figure 4 – CAFT: Comprehensive Abdominal Fat Segmentation Tool

Automated analysis of cardiac MRI for clinical trials IMMACULATE/PITRI/Platelet-STEMI (With National Heart Centre)

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.

Figure 5 – Automated analysis of cardiac MRI for clinical trials IMMACULATE/PITRI/Platelet-STEMI (With NHCS)

Moving Forward

Figure 6 – CDAR's plan moving forward

Collaborations

Figure 7 – Collaborations

• Bhanu Prakash KN, Arvind CS, Yeow LY, Chen WX, Lim WS, Tan CH. CAFT: A Deep Learning based Comprehensive Abdominal Fat Analysis Tool for Large Cohort Studies. MAGMA, 2021. 


• Geetha Soujanya Chilla, Yeow Ling Yun, Qian Hui Chew, Kang Sim, Bhanu Prakash KN Machine learning classification of schizophrenia patients and healthy controls using diverse neuroanatomical markers and Ensemble methods. Scientific Reports (Submitted).   


• Che Mohd Nassir CMN, Damodaran T, Yusof SR, Norazit A, Chilla G, Huen I, Bhanu Prakash KN, Mohamed Ibrahim N, Mustapha M. Aberrant Neurogliovascular Unit Dynamics in Cerebral Small Vessel Disease: A Rheological Clue to Vascular Parkinsonism. Pharmaceutics 2021, 13, 1207.


• Ren BX, Huen I, Wu ZJ, Wang H, Duan MY, Guenther I, Bhanu Prakash KN, Tang FR. Early Postnatal Irradiation-induced Age-dependent Changes in Adult Mouse Brain: MRI based Characterization.BMC Neuroscience, 22, Article number: 28 (2021). 


• Gawali M , Arvind CS, Suryavanshi S, Madaan H, Gaikwad A, Bhanu Prakash KN, Kulkarni V, Pant A.. Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare.arXiv:2012.12591, 2021. 


• Smarajit Chakraborty, Ong WK, Yau WWY, Zhou ZH, Bhanu Prakash KN, Toh SA, Han WP, Yen PM, Shigeki Sugii. CD10 marks non-canonical PPARγ-independent adipocyte maturation and browning potential of adipose-derived stem cells. Stem Cell Research & Therapy volume 12, Article number: 109 (2021) 


• Yuan C, Chakraborty S, Chitta KK, Subramanian S, Lim TE, Han W, Bhanu Prakash KN, Sugii S. Fast Adipogenesis Tracking System (FATS)—a robust, high-throughput, automation-ready adipogenesis quantification technique. Stem Cell Res Ther. 2019; 10: 38. Published online 2019 Jan 22. doi: 10.1186/s13287-019-1141-0  


• Lum FM, Zhang W, Lim KC, Malleret B, Teo TH, Koh JJ, Lee KJ, Chua TK, Kam YW, Yee WX, Huen I, Tan JJL, Amrun SN, Bhanu Prakash KN, Cozzone PJ, Renia L, Lee PTH, Ng LFP. Multimodal assessments of Zika virus immune pathophysiological responses in marmosets. Scientific Reports. Nov 2018(1):17125. doi: 10.1038/s41598-018-35481-6   


• Bhanu Prakash KN, Srour H, Velan SS, Chuang KH. A method for automatic segmentation of Brown Adipose Tissue.   Magnetic Resonance Materials in Physics, Biology and Medicine.  Apr; 29(2):287-99, 2016. DOI 10.1007/s10334-015-0517-0   


• Bhanu PK, Verma SK, Yaligar J, Goggi J, Gopalan V, Lee SS, Tian X, Sugii S, Leow MK, Bhakoo K, Velan SS. Segmentation and characterization of Interscapular Brown Adipose Tissue in rats by Multi-Parametric Magnetic Resonance Imaging.   Magnetic Resonance Materials in Physics, Biology and Medicine. Apr; 29(2):277-86, 2016. DOI 10.1007/s10334-015-0514-3. 


• Chaurasia B, Kaddai VA, Lancaster GI, Henstridge DC, Sriram S, Galam DL, Gopalan V, Bhanu Prakash KN, Velan SS, Bulchand S, Tsong TJ, Wang M, Siddique MM, Yuguang G, Sigmundsson K, Mellet NA, Weir JM, Meikle PJ, Bin M Yassin MS, Shabbir A, Shayman JA, Hirabayashi Y, Shiow ST, Sugii S, Summers SA. Adipocyte Ceramides Regulate Subcutaneous Adipose Browning, Inflammation, and Metabolism.   Cell Metabolism. 2016 Oct 21. pii: S1550-4131(16)30533-2. doi: 10.1016/j.cmet.2016.10.002. 


• View full list of publications and patents here