Biomed Opt Express
School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
Published: June 2024
Optical microscopy has witnessed notable advancements but has also become more costly and complex. Conventional wide field microscopy (WFM) has low resolution and shallow depth-of-field (DOF), which limits its applications in practical biological experiments. Recently, confocal and light sheet microscopy become major workhorses for biology that incorporate high-precision scanning to perform imaging within an extended DOF but at the sacrifice of expense, complexity, and imaging speed. Here, we propose deep focus microscopy, an efficient framework optimized both in hardware and algorithm to address the tradeoff between resolution and DOF. Our deep focus microscopy achieves large-DOF and high-resolution projection imaging by integrating a deep focus network (DFnet) into light field microscopy (LFM) setups. Based on our constructed dataset, deep focus microscopy features a significantly enhanced spatial resolution of ∼260 nm, an extended DOF of over 30 µm, and broad generalization across diverse sample structures. It also reduces the computational costs by four orders of magnitude compared to conventional LFM technologies. We demonstrate the excellent performance of deep focus microscopy , including long-term observations of cell division and migrasome formation in zebrafish embryos and mouse livers at high resolution without background contamination.
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http://dx.doi.org/10.1364/BOE.523312 | DOI Listing |
Curr Drug Discov Technol
December 2024
Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Delhi Pharmaceutical Sciences and Research University, PushpViharSector-3, M-B Road, New Delhi, 110017, India.
Background: Computer-Aided Drug Design (CADD) approaches are essential in the drug discovery and development process. Both academic institutions and pharmaceutical and biotechnology corporations utilize them to enhance the efficacy of bioactive compounds.
Objective: This study aims to entice researchers by investigating the benefits of Computer-Aided Drug and Design (CADD) and its fundamental principles.
Sci Rep
January 2025
College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong, 030800, China.
To address the challenges of unbalanced class labels with varying maturity levels of tomato fruits and low recognition accuracy for both fruits and stems in intelligent harvesting, we propose the YOLOX-SE-GIoU model for identifying tomato fruit maturity and stems. The SE focus module was incorporated into YOLOX to improve the identification accuracy, addressing the imbalance in the number of tomato fruits and stems. Additionally, we optimized the loss function to GIoU loss to minimize discrepancies across different scales of fruits and stems.
View Article and Find Full Text PDFJ Am Acad Orthop Surg Glob Res Rev
January 2025
From the Department of Anatomy, School of Medicine, Marmara University, Basibuyuk Yolu, Maltepe, Istanbul, Turkey (Dr. Ismailoglu, Dr. Sehirli, and Dr. Ayingen); the Department of Anatomy, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Atasehir, Istanbul, Turkey (Dr. Bayramoglu and Dr. Savasan); and the Department of Orthopedic Surgery, Faculty of Medicine, Acibadem Mehmet Ali Aydinlar University, Atasehir, Istanbul, Turkey (Dr. Kocaoglu).
Purpose: The surgical approach for midfoot injuries classically requires dual dorsal incision and identification of the neurovascular structures that are susceptible to injury during the surgery. The aim of this study was to map the topographic anatomy of the dorsum of the foot along with tarsal joints for the dorsal approach of midfoot surgery that would facilitate the surgery and minimize the risk of neurovascular injuries for surgeons who specially focus on foot and ankle injuries.
Methods: The dorsum of the foot was evaluated in 12 feet injected with latex containing a red colorant to visualize the arterial vessels.
Curr Med Imaging
January 2025
School of Life Sciences, Tiangong University, Tianjin 300387, China.
Objective: The objective of this research is to enhance pneumonia detection in chest X-rays by leveraging a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with modified Swin Transformer blocks. This study aims to significantly improve diagnostic accuracy, reduce misclassifications, and provide a robust, deployable solution for underdeveloped regions where access to conventional diagnostics and treatment is limited.
Methods: The study developed a hybrid model architecture integrating CNNs with modified Swin Transformer blocks to work seamlessly within the same model.
BMC Cancer
January 2025
Department of Data Science, Faculty of Interdisciplinary Science and Technology, Tarbiat Modares University, Tehran, Iran.
Background: Melanoma is a highly aggressive skin cancer, where early and accurate diagnosis is crucial to improve patient outcomes. Dermoscopy, a non-invasive imaging technique, aids in melanoma detection but can be limited by subjective interpretation. Recently, machine learning and deep learning techniques have shown promise in enhancing diagnostic precision by automating the analysis of dermoscopy images.
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