Browsing by Research Output - Author "Abaxi, Sai Mu Dalike"

Absolute quantification of biological samples entails determining expression levels in precise numerical copies, offering enhanced accuracy and superior performance for rare templates. However, existing methodologies suffer from significant limitations: flow cytometers are both costly and intricate, while fluorescence imaging relying on software tools or manual counting is time-consuming and prone to inaccuracies. In this study, we have devised a comprehensive deep-learning-enabled pipeline that enables the automated segmentation and classification of GFP (green fluorescent protein)-labeled microreactors, facilitating real-time absolute quantification. Our findings demonstrate the efficacy of this technique in accurately predicting the sizes and occupancy status of microreactors using standard laboratory fluorescence microscopes, thereby providing precise measurements of template concentrations. Notably, our approach exhibits an analysis speed of quantifying over 2,000 microreactors (across 10 images) within remarkably 2.5 seconds, and a dynamic range spanning from 56.52 to 1569.43 copies per micron-liter. Furthermore, our Deep-dGFP algorithm showcases remarkable generalization capabilities, as it can be directly applied to various GFP-labeling scenarios, including droplet-based, microwell-based, and agarose-based biological applications. To the best of our knowledge, this represents the first successful implementation of an all-in-one image analysis algorithm in droplet digital PCR (polymerase chain reaction), microwell digital PCR, droplet single-cell sequencing, agarose digital PCR, and bacterial quantification, without necessitating any transfer learning steps, modifications, or retraining procedures. We firmly believe that our Deep-dGFP technique will be readily embraced by biomedical laboratories and holds potential for further development in related clinical applications.

Abstract: An automated and lightweight method to accurately segment optical coherence tomography (OCT) images can bring a plethora of benefits, such as the production of objective diagnostic indicators at a fast rate and the implementation in imaging devices with ease. Due to the unique imaging principle, OCT images differ from natural images as they feature layered structures stretching along the image width, instead of completely closed regions. Conventional convolutional neural networks designed for natural images are usually sub-optimal for segmenting OCT images. Therefore, it is imperative to develop a segmentation network with a strong awareness of the structural features in OCT images for more efficient predictions. In this work, we introduce a novel lightweight deformable OCT segmentation network (LiDeOCTNet) to enable a flexible and scalable feature receptive field for an accurate segmentation of the irregular structures in OCT images. When compared with the classic UNet, LiDeOCTNet achieved better performance in segmenting both retinal and endoscopic OCT images. In comparison to the state-of-the-art networks, LiDeOCTNet offered competitive results with a far more lightweight network. The simplistic design of our network may lead to a feasible OCT-aware framework to achieve reliable segmentation of OCT images in real time.

Optical Coherence Tomography (OCT) is an emerging technology that provides three-dimensional images of the microanatomy of biological tissue in-vivo and at micrometer-scale resolution. OCT imaging has been widely used to diagnose and manage various medical diseases, such as macular degeneration, glaucoma, and coronary artery disease. Despite its wide range of applications, the segmentation of OCT images remains difficult due to the complexity of tissue structures and the presence of artifacts. In recent years, different approaches have been used for OCT image segmentation, such as intensity-based, region-based, and deep learning-based methods. This paper reviews the major advances in state-of-the-art OCT image segmentation techniques. It provides an overview of the advantages and limitations of each method and presents the most relevant research works related to OCT image segmentation. It also provides an overview of existing datasets and discusses potential clinical applications. Additionally, this review gives an in-depth analysis of machine learning and deep learning approaches for OCT image segmentation. It outlines challenges and opportunities for further research in this field.