Narrow Results

This interview with the executive director of Institute of Bioengineering and Nanobiotechnology summarises Professor Yings views on the bioengineering field and the institutes’ goals.

SINGAPORE – Biofourmis to Run New Study on How Singapore’s Lifestyle Affects Heart Health SingHEART Vitals Study with Support from IMDA.

SINGAPORE – Smart Nation Boost: Singapore Engineers Develop Remote Vital Signs Monitoring System.

ESMO ASIA 2016 CONGRESS, SINGAPORE – Routine Blood Test Predicts How Long Cancer Patients Will Survive.

ESMO ASIA 2016 CONGRESS, SINGAPORE – Cancer Costs Leaving Patients in Debt.

ESMO ASIA 2016 CONGRESS, SINGAPORE – Asian Head and Neck Cancer Patients Live Longer with Immunotherapy than Mixed Race Group.

JAPAN – Avastin® Received Orphan Drug Designation for the Treatment of Malignant Pleural Mesothelioma.

UNITED STATES – Recovery from Brain Injury and Better Sleep Go Hand in Hand.

UNITED STATES – Erasing the Line between Imaging and Analysing.

Prevention is the most effective way to reduce dental caries. In order to provide a simple way to achieve oral healthcare direction in daily life, dual Channel, portable dental Imaging system that combine white light with autofluorescence techniques was established, and then, a group of volunteers were recruited, 7200 tooth pictures of different dental caries stage and dental plaque were taken and collected. In this work, a customized Convolutional Neural Networks (CNNs) have been designed to classify dental image with early stage caries and dental plaque. Eighty percentage ($n=6000$) of the pictures taken were used to supervised training of the CNNs based on the experienced dentists’ advice and the rest 20% ($n=1200$) were used to a test dataset to test the trained CNNs. The accuracy, sensitivity and specificity were calculated to evaluate performance of the CNNs. The accuracy for the early stage caries and dental plaque were 95.3% and 95.9%, respectively. These results shown that the designed image system combined the customized CNNs that could automatically and efficiently find early caries and dental plaque on occlusal, lingual and buccal surfaces. Therefore, this will provide a novel approach to dental caries prevention for everyone in daily life.

To accurately guide surgical instruments during ophthalmic procedures, some necessary intraoperative depth perception is required, which standard surgical microscopes supply limitedly. Intraoperative optical coherence tomography (iOCT), combining optical coherence tomography (OCT) technology and surgical microscope, enables noninvasive, real-time and high-resolution cross-sectional imaging. Currently, though iOCT enables structural imaging, little research has been done on intraoperative angiography. In this work, we presented a swept-source intraoperative OCT angiography (SS-iOCTA) system based on a standard surgical microscope, which provides both structural and angiographic images. The feasibility of the proposed SS-iOCTA was confirmed through deep anterior lamellar keratoplasty (DALK) of ex vivo porcine eyes and blood perfusion imaging of in vivo rat cortex. High-resolution intraoperative feedback, including sub-surface structure and angiogram of biological tissue, can be visualized simultaneously with the SS-iOCTA system, which expand the surgeon’s capabilities and could be widely used in clinical surgery.

Image reconstruction in fluorescence molecular tomography involves seeking stable and meaningful solutions via the inversion of a highly under-determined and severely ill-posed linear mapping. An attractive scheme consists of minimizing a convex objective function that includes a quadratic error term added to a convex and nonsmooth sparsity-promoting regularizer. Choosing ${\ell }_1$-norm as a particular case of a vast class of nonsmooth convex regularizers, our paper proposes a low per-iteration complexity gradient-based first-order optimization algorithm for the ${\ell }_1$-regularized least squares inverse problem of image reconstruction. Our algorithm relies on a combination of two ideas applied to the nonsmooth convex objective function: Moreau–Yosida regularization and inertial dynamics-based acceleration. We also incorporate into our algorithm a gradient-based adaptive restart strategy to further enhance the practical performance. Extensive numerical experiments illustrate that in several representative test cases (covering different depths of small fluorescent inclusions, different noise levels and different separation distances between small fluorescent inclusions), our algorithm can significantly outperform three state-of-the-art algorithms in terms of CPU time taken by reconstruction, despite almost the same reconstructed images produced by each of the four algorithms.

Microwave-induced thermoacoustic imaging (MTAI) has emerged as a potential biomedical imaging modality with over 20-year growth. MTAI typically employs pulsed microwave as the pumping source, and detects the microwave-induced ultrasound wave via acoustic transducers. Therefore, it features high acoustic resolution, rich electromagnetic contrast, and large imaging depth. Benefiting from these unique advantages, MTAI has been extensively applied to various fields including pathology, biology, material and medicine. Till now, MTAI has been deployed for a wide range of biomedical applications, including cancer diagnosis, joint evaluation, brain investigation and endoscopy. This paper provides a comprehensive review on (1) essential physics (endogenous/exogenous contrast mechanisms, penetration depth and resolution), (2) hardware configurations and software implementations (excitation source, antenna, ultrasound detector and image recovery algorithm), (3) animal studies and clinical applications, and (4) future directions.

Laser speckle contrast imaging (LSCI) is a noninvasive, label-free technique that allows real-time investigation of the microcirculation situation of biological tissue. High-quality microvascular segmentation is critical for analyzing and evaluating vascular morphology and blood flow dynamics. However, achieving high-quality vessel segmentation has always been a challenge due to the cost and complexity of label data acquisition and the irregular vascular morphology. In addition, supervised learning methods heavily rely on high-quality labels for accurate segmentation results, which often necessitate extensive labeling efforts. Here, we propose a novel approach LSWDP for high-performance real-time vessel segmentation that utilizes low-quality pseudo-labels for nonmatched training without relying on a substantial number of intricate labels and image pairing. Furthermore, we demonstrate that our method is more robust and effective in mitigating performance degradation than traditional segmentation approaches on diverse style data sets, even when confronted with unfamiliar data. Importantly, the dice similarity coefficient exceeded 85% in a rat experiment. Our study has the potential to efficiently segment and evaluate blood vessels in both normal and disease situations. This would greatly benefit future research in life and medicine.

In this paper, we explore the transformative role of artificial intelligence (AI) in the analysis and transformation of biomedical images in the field of cancer immunotherapy. This review highlights the potential of AI to be deployed in the clinic as part of value-added healthcare, streamlining image analysis, facilitating the prediction of immunotherapy response, and integrating multi-modal data to offer a holistic view of patient conditions. We discuss the challenges facing clinical implementation, including data privacy, model interpretability, and the need for standardized reporting, which must be addressed to ensure patient safety.