Digitization of natural history collections is a major challenge in archiving biodiversity. In recent years, several approaches have emerged, allowing either automated digitization, extended depth of field (EDOF) or multi-view imaging of insects. Here, we present DISC3D: a new digitization device for pinned insects and other small objects that combines all these aspects. A PC and a microcontroller board control the device. It features a sample holder on a motorized two-axis gimbal, allowing the specimens to be imaged from virtually any view. Ambient, mostly reflection-free illumination is ascertained by two LED-stripes circularly installed in two hemispherical white-coated domes (front-light and back-light). The device is equipped with an industrial camera and a compact macro lens, mounted on a motorized macro rail. EDOF images are calculated from an image stack using a novel calibrated scaling algorithm that meets the requirements of the pinhole camera model (a unique central perspective). The images can be used to generate a calibrated and real color texturized 3Dmodel by 'structure from motion' with a visibility consistent mesh generation. Such models are ideal for obtaining morphometric measurement data in 1D, 2D and 3D, thereby opening new opportunities for trait-based research in taxonomy, phylogeny, eco-physiology, and functional ecology.
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http://dx.doi.org/10.3897/zookeys.759.24584 | DOI Listing |
Sci Rep
January 2025
University Paris-Saclay, CEA, CNRS, Neurospin, Baobab UMR 9027, Gif-sur-Yvette, 91191, France.
Recent advances highlight the limitations of classification strategies in machine learning that rely on a single data source for understanding, diagnosing and predicting psychiatric syndromes. Moreover, approaches based solely on clinician labels often fail to capture the complexity and variability of these conditions. Recent research underlines the importance of considering multiple dimensions that span across different psychiatric syndromes.
View Article and Find Full Text PDFEndocrinol Metab (Seoul)
January 2025
Division of Endocrinology and Metabolism, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules.
Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer.
Brief Bioinform
November 2024
Department of Biology, University at Albany, SUNY, 1400 Washington Ave, Albany, NY 12222, United States.
The accuracy of assigning fluorophore identity and abundance, known as spectral unmixing, in biological fluorescence microscopy images remains a significant challenge due to the substantial overlap in emission spectra among fluorophores. In traditional laser scanning confocal spectral microscopy, fluorophore information is acquired by recording emission spectra with a single combination of discrete excitation wavelengths. However, organic fluorophores possess characteristic excitation spectra in addition to their unique emission spectral signatures.
View Article and Find Full Text PDFSensors (Basel)
December 2024
College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumqi 830017, China.
This paper addresses the challenges of low accuracy and long transfer learning time in small-sample bearing fault diagnosis, which are often caused by limited samples, high noise levels, and poor feature extraction. We propose a method that combines an improved capsule network with a Siamese neural network. Multi-view data partitioning is used to enrich data diversity, and Markov transformation converts one-dimensional vibration signals into two-dimensional images, enhancing the visualization of signal features.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Electrical Engineering, Center for Innovative Research on Aging Society (CIRAS), Advanced Institute of Manufacturing with High-Tech Innovations (AIM-HI), National Chung Cheng University, Chia-Yi 621, Taiwan.
In computer vision, accurately estimating a 3D human skeleton from a single RGB image remains a challenging task. Inspired by the advantages of multi-view approaches, we propose a method of predicting enhanced 2D skeletons (specifically, predicting the joints' relative depths) from multiple virtual viewpoints based on a single real-view image. By fusing these virtual-viewpoint skeletons, we can then estimate the final 3D human skeleton more accurately.
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