New Trends in Image Analysis for Next-Generation Microscopy

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: 31 May 2025 | Viewed by 884

Special Issue Editors


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Guest Editor
Institute of Optics, CSIC—Instituto de Óptica Daza de Valdés, (IO-CSIC), 28006 Madrid, Spain
Interests: optics; biophotonics; visual science; adaptation; adaptive optics; microscopy; neural pathways

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Guest Editor
VISILAB Group (Vision and Artificial Intelligence Group), Universidad de Castilla-La Mancha, ETSII, 13071 Ciudad Real, Spain
Interests: artificial intelligence; microscopy; photonics; biomedical image processing; digital pathology
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute of Optics, Spanish National Research Council, Serrano 121, 28006 Madrid, Spain
Interests: image processing; image quality assessment; microscopy; phytoplankton identification; image fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advancements in image analysis are pivotal in fully leveraging the potential of next-generation microscopy, enabling the extraction of meaningful insights from complex and high-resolution datasets. Our main objective for this Special Issue is to foster innovation in image analysis to enhance data interpretation, application efficiency, and access to advanced microscopy tools. The integration of emerging technologies, such as artificial intelligence, has revolutionized microscopy by enabling the automated and precise analysis of single-molecule, single-cell, and live-cell data. Moreover, advancements in image enhancement and super-resolution techniques have allowed researchers to explore biological, clinical, and material systems in unparalleled detail. The development of 3D and 4D image reconstruction and visualization techniques is expanding the horizons of spatial and temporal analysis, while multimodal data integration and quantitative approaches are providing holistic perspectives on complex biological and physical phenomena.

To meet the demands of large-scale and high-throughput applications, cloud computing and high-performance computing are increasingly essential in processing and analyzing microscopy datasets. Additionally, the advent of open hardware and software solutions is democratizing access to advanced microscopy technologies, fostering innovation and collaboration across disciplines. Applications go far beyond clinical research, encompassing biological studies on cellular dynamics, materials science investigations into nanostructures, and environmental analyses of microbial ecosystems and pollutants.

This Special Issue will feature a selection of contributions presented at the recent SPAOM 2024 Conference. By showcasing the latest advancements in image analysis for next-generation microscopy, we aim to inspire innovative solutions and promote collaborations to facilitate discoveries across the diverse applications of microscopy. Papers are solicited from both the Conference and the broader community. We are looking for papers including, but not limited to, the following areas:

  • Machine learning and deep learning in microscopy;
  • Image analysis, image enhancement, and super-resolution techniques;
  • Single-molecule, single-cell, and live-cell image analysis;
  • Nonlinear imaging and advanced microscopy (SHG, THG, CARS, FlIM, or FRET);
  • 3D and 4D image reconstruction and visualization;
  • Automated quantitative analysis and high-content screening;
  • Multimodal integration and correlative imaging;
  • Cloud and high-performance computing;
  • Open hardware and software in microscopy.

Dr. María Viñas Peña
Dr. Gloria Bueno
Dr. Gabriel Cristobal
Guest Editors

Manuscript Submission Information

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Keywords

  • microscopy
  • image analysis
  • correlative imaging
  • deep learning
  • nanoscopy
  • super-resolution techniques
  • live cell analysis
  • 3D and 4D visualization
  • cloud computing
  • open hardware and software

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Published Papers (1 paper)

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7 pages, 4282 KiB  
Brief Report
Multiphoton Microscopy to Visualize Live Renal Nerves in Reanimated Kidney Blocks
by Joerg Reifart, Patrick T. Willey and Paul A. Iaizzo
J. Imaging 2025, 11(2), 56; https://doi.org/10.3390/jimaging11020056 - 13 Feb 2025
Viewed by 581
Abstract
Renal denervation to treat arterial hypertension is growing in adoption but still shows inconsistent results. Device improvement is difficult, as there is currently no way to study the immediate success of renal denervation devices in living tissue. In an effort to visualize live [...] Read more.
Renal denervation to treat arterial hypertension is growing in adoption but still shows inconsistent results. Device improvement is difficult, as there is currently no way to study the immediate success of renal denervation devices in living tissue. In an effort to visualize live renal nerves surrounding their arteries using multiphoton microscopy, kidney pairs were explanted from Yorkshire pigs. They were maintained viable with a pulsatile perfusion apparatus using Visible Kidney™ methodologies, in which blood is replaced by a modified, oxygenated, and warmed (37 °C) Krebs–Henseleit buffer. The block resection allows catheter placement for nerve ablation treatment. Subsequently, the kidney block was disconnected from the perfusion system and underwent multiphoton microscopy (Nikon A1R 1024 MP). A total of three renal blocks were imaged using this model. Using 780 nm excitation for autofluorescence, we were able to selectively image peri-arterial nerves (2.5–23 μm diameter) alongside arteriolar elastin fibers (1.96 ± 0.87 μm; range: 0.3–4.27) at 25× magnification at a pixel size of 1.02 µm). Autofluoresecence was not strong enough to identify nerves at 4× magnification. There was a high but variable signal-to-noise ratio of 52.3 (median, IQR 159). This model may be useful for improving future physician training and innovations in renal denervation technologies. Full article
(This article belongs to the Special Issue New Trends in Image Analysis for Next-Generation Microscopy)
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