Optical diffraction tomography enables label-free, 3D refractive index (RI) imaging of biological samples. We present a novel, cost-effective approach to ODT that employs a modular design incorporating a self-reference holographic capture module. This two-part system consists of an illumination module and a capture module that can be seamlessly integrated with any life-science microscope using an automated alignment protocol. The illumination module employs a galvo-scanner system, providing precise control over the angular illumination, while the capture module utilises the principle of self-reference off-axis holography. The design has a compact form factor, simple alignment, and reduced cost. Furthermore, our system offers the capability to switch between two imaging modalities, ODT and real-time synthetic aperture digital holographic microscopy (SA-DHM), a unique feature not found in other setups. Experimental results are provided using a kidney cancer cell line. Experimental results are provided using a kidney cancer cell line.
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http://dx.doi.org/10.1364/BOE.545296 | DOI Listing |
Biomed Opt Express
January 2025
Department of Electronic Engineering, Maynooth University, Maynooth, Co. Kildare, Ireland.
Optical diffraction tomography enables label-free, 3D refractive index (RI) imaging of biological samples. We present a novel, cost-effective approach to ODT that employs a modular design incorporating a self-reference holographic capture module. This two-part system consists of an illumination module and a capture module that can be seamlessly integrated with any life-science microscope using an automated alignment protocol.
View Article and Find Full Text PDFSci Rep
January 2025
Zhongyu (Fujian) Digital Technology Co., Ltd, Fuzhou, 350108, China.
Attention mechanisms have been introduced to exploit deep-level information for image restoration by capturing feature dependencies. However, existing attention mechanisms often have limited perceptual capabilities and are incompatible with low-power devices due to computational resource constraints. Therefore, we propose a feature enhanced cascading attention network (FECAN) that introduces a novel feature enhanced cascading attention (FECA) mechanism, consisting of enhanced shuffle attention (ESA) and multi-scale large separable kernel attention (MLSKA).
View Article and Find Full Text PDFSci Rep
January 2025
College of computer science and technology, China University of Petroleum (East China), No.66 Changjiang West Road, Huangdao, Qingdao, 266580, Shandong, China.
Addressing the issues of inadequate information exchange among subsequences in the operational time series of water injection pumps, leading to low accuracy and high false alarm rates in anomaly detection, this paper proposes a multidimensional time series anomaly detection method for water injection pump operations, leveraging Long Short-Term Memory Autoencoder augmented with Attention Mechanism (LSTMA-AE) and mechanistic constraints. The LSTMA-AE framework encompasses three primary modules: a Time Feature Extraction Module (Encoder), an Attention Layer, and a Data Reconstruction Module (Decoder). The Encoder captures temporal dependencies and features within the input sequences, mapping the input data into a higher-dimensional space.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Guizhou Province International Science and Technology Cooperation Base for Precision Imaging Diagnosis and Treatment, Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province, Department of Radiology, Guizhou Provincial People's Hospital, Guizhou 550002, China. Electronic address:
Background And Objective: Accurate segmentation of the prostate region in magnetic resonance imaging (MRI) is crucial for prostate-related diagnoses. Recent studies have incorporated Transformers into prostate region segmentation to better capture long-range global feature representations. However, due to the computational complexity of Transformers, these studies have been limited to processing single slices.
View Article and Find Full Text PDFPhys Med Biol
January 2025
School of Software Engineering, Xi'an Jiaotong University, Xi 'an Jiaotong University Innovation Port, Xi 'an, Shaanxi Province, Xi'an, Shaanxi, 710049, CHINA.
Deformable registration aims to achieve nonlinear alignment of image space by estimating a dense displacement field. It is commonly used as a preprocessing step in clinical and image analysis applications, such as surgical planning, diagnostic assistance, and surgical navigation. We aim to overcome these challenges: Deep learning-based registration methods often struggle with complex displacements and lack effective interaction between global and local feature information.
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