Review
Multidisciplinary Sciences
Colin J. R. Sheppard
Summary: Structured illumination microscopy and image scanning microscopy are two rapidly growing microscopic techniques that can enhance transverse spatial resolution and/or improve axial imaging performance. This article reviews the history and principles of these techniques and compares the imaging properties of the two methods.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
(2021)
Article
Computer Science, Interdisciplinary Applications
Weixun Chen, Min Liu, Hao Du, Miroslav Radojevic, Yaonan Wang, Erik Meijering
Summary: This paper proposes a novel method called SPE-DNR that combines spherical-patches extraction and deep-learning for neuron reconstruction. Experimental results demonstrate that the method is competitive and robust.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Optics
Weisong Zhao, Shiqun Zhao, Zhenqian Han, Xiangyan Ding, Guangwei Hu, Liying Qu, Yuanyuan Huang, Xinwei Wang, Heng Mao, Yaming Jiu, Ying Hu, Jiubin Tan, Xumin Ding, Liangyi Chen, Changliang Guo, Haoyu Li
Summary: High-throughput super-resolution (SR) imaging is attractive for rapid and high-precision profiling in biomedical applications. The proposed SACD method reduces the number of frames required for SR imaging and enables four-dimensional imaging of live cells and events. SACD improves accessibility to SR imaging and facilitates high-throughput and low-cost biological studies.
Article
Multidisciplinary Sciences
Michael J. Wester, David J. Schodt, Hanieh Mazloom-Farsibaf, Mohamadreza Fazel, Sandeep Pallikkuth, Keith A. Lidke
Summary: In this study, a robust drift correction method is proposed for super-resolution methods based on single molecule localization, which combines 3D registration with a fast post-processing algorithm, ensuring stability and drift correction for an indefinite time period without specialized hardware.
SCIENTIFIC REPORTS
(2021)
Article
Multidisciplinary Sciences
Simao Coelho, Jongho Baek, James Walsh, J. Justin Gooding, Katharina Gaus
Summary: Two-photon direct laser writing enables nanometer-accuracy fabrication of three-dimensional structures, providing high-resolution imaging possibilities for optical microscopy.
NATURE COMMUNICATIONS
(2022)
Article
Chemistry, Physical
Yasashri Ranathunga, Temitayo Olowolafe, Emmanuel Orunesajo, Hackim Musah, Suk Kyoung Lee, Wen Li
Summary: We present a straightforward method for achieving three-dimensional ion momentum imaging. This method utilizes two complementary metal-oxide-semiconductor cameras along with a standard microchannel plates/phosphor screen imaging detector. The cameras are synchronized to measure the decay of luminescence generated by ion collisions and extract the time of flight. The time resolution achieved is better than 10 ns, primarily limited by camera jitters, but can reach better than 5 ns resolution when the jitter is minimized.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Optics
Lingjia Dai, Mingda Lu, Chao Wang, Sudhakar Prasad, Raymond Chan
Summary: Three-dimensional point source recovery from two-dimensional data is a challenging problem. This article proposes a convolution neural network-based approach to automatically localize space debris in full 3D space. The method is efficient and achieves higher precision compared to traditional model-based methods.
Article
Optics
Tatsuki Tahara, Takako Koujin, Atsushi Matsuda, Ayumi Ishii, Tomoyoshi Ito, Yasuyuki Ichihashi, Ryutaro Oi
Summary: Color fluorescence imaging of self-luminous objects and stained biological samples is achieved using incoherent digital holographic technique with multiplexing of multiple wavelengths, self-interference, and computational coherent superposition. This method allows for simultaneous color three-dimensional sensing of multiple self-luminous objects without mechanical scanning, and demonstrates improved point spread function in color fluorescence imaging through experimental verification of a Fresnel incoherent correlation holography system.
Article
Optics
Hao Wang, Waleed Tahir, Jiabei Zhu, Lei Tian
Summary: The novel algorithm developed is based on multiple-scattering beam propagation method combined with sparse regularization to reconstruct dense 3D particles of high refractive index contrast. A computationally efficient algorithm is devised to solve the inverse problem, significantly reducing the computation time and outperforming the single-scattering method in accuracy.
Article
Biochemical Research Methods
George Sirinakis, Edward S. Allgeyer, Jinmei Cheng, Daniel St Johnston
Summary: When using spinning disks combined with PAINT methods for super-resolution imaging, the geometry of the spinning disk has an impact on imaging performance. Disk geometries with higher light collection efficiency perform better for PAINT-based super-resolution imaging.
BIOMEDICAL OPTICS EXPRESS
(2022)
Article
Optics
Peter Kocsis, Igor Shevkunov, Vladimir Katkovnik, Heikki Rekola, Karen Egiazarian
Summary: The proposed method introduces a lensless single-shot phase retrieval approach that separates carrying and object wavefronts. By calibrating discrepancies between computational models and physical elements and implementing pixel super-resolution processing, it reconstructs the object wavefront with correction from the carrying wavefront, demonstrating robustness in simulations and experiments. In phase bio-imaging, it achieves high-quality imaging results and records dynamic scenes efficiently with the single-shot advantage.
Article
Optics
Dorian Bouchet, Jacob Seifert, Allard P. Mosk
Summary: This study presents a numerical framework to quantify the precision of estimating parameters from measured data using the Fisher information matrix as a benchmark for assessing CDI methods. By optimizing the Fisher information metric with deep learning libraries, the research identifies an optimal illumination scheme for minimizing estimation errors under specific experimental constraints, paving the way for efficient characterization of structured samples at the sub-wavelength scale.
Article
Optics
Benjamin Lochocki, Ksenia Abrashitova, Johannes F. de Boer, Lyubov Amitonova
Summary: Compressive imaging using sparsity constraints offers improved spatial resolution and overcomes traditional limits; the use of speckle illumination and single-pixel detection in experimental setups is crucial; oversampling may decrease the resolution and reconstruction quality of compressive imaging.
Article
Multidisciplinary Sciences
Siewert Hugelier, Wim Vandenberg, Tomas Lukes, Kristin S. Grussmayer, Paul H. C. Eilers, Peter Dedecker, Cyril Ruckebusch
Summary: In this research, the use of Whittaker smoothing is proposed to enhance SOFI signals by correcting photodestruction, particularly when it occurs rapidly. This method results in higher contrast images, strongly suppressed background, and more detailed visualization of cellular structures. Additionally, it is parameter-free, computationally efficient, and applicable to both two-dimensional and three-dimensional data.
SCIENTIFIC REPORTS
(2021)
Article
Optics
Kejun Wu, Qiong Liu, Kim-hui Yap, You Yang
Summary: This paper proposes an efficient VFMV compression scheme based on VMSR and ADPS. VMSR rearranges VFMV to enhance inter-view correlations, while ADPS efficiently compresses the rearranged VFMV by exploiting the enhanced correlations. The proposed scheme achieves state-of-the-art compression performance and provides forgery protection.
Article
Computer Science, Artificial Intelligence
Hong Liu, Dong Wei, Donghuan Lu, Xiaoying Tang, Liansheng Wang, Yefeng Zheng
Summary: This study proposes a framework based on hybrid 2D-3D convolutional neural networks for obtaining continuous 3D retinal layer surfaces from OCT volumes. The framework works well with both full and sparse annotations and utilizes alignment displacement vectors and layer segmentation to align the B-scans and segment the layers. Experimental results show that the framework outperforms state-of-the-art 2D deep learning methods in terms of layer segmentation accuracy and cross-B-scan 3D continuity.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Simon Oxenford, Ana Sofia Rios, Barbara Hollunder, Clemens Neudorfer, Alexandre Boutet, Gavin J. B. Elias, Jurgen Germann, Aaron Loh, Wissam Deeb, Bryan Salvato, Leonardo Almeida, Kelly D. Foote, Robert Amaral, Paul B. Rosenberg, David F. Tang-Wai, David A. Wolk, Anna D. Burke, Marwan N. Sabbagh, Stephen Salloway, M. Mallar Chakravarty, Gwenn S. Smith, Constantine G. Lyketsos, Michael S. Okun, William S., Zoltan Mari, Francisco A. Ponce, Andres Lozano, Wolf-Julian Neumann, Bassam Al-Fatly, Andreas Horn
Summary: Spatial normalization is a method to map subject brain images to an average template brain, allowing comparison of brain imaging results. We introduce a novel tool called WarpDrive, which enables manual refinements of image alignment after automated registration. The tool improves accuracy of data representation and aids in understanding patient outcomes.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Ricards Marcinkevics, Patricia Reis Wolfertstetter, Ugne Klimiene, Kieran Chin-Cheong, Alyssia Paschke, Julia Zerres, Markus Denzinger, David Niederberger, Sven Wellmann, Ece Ozkan, Christian Knorr, Julia E. Vogt
Summary: This study presents interpretable machine learning models for predicting the diagnosis, management, and severity of suspected appendicitis using ultrasound images. The proposed models utilize concept bottleneck models (CBM) that facilitate interpretation and intervention by clinicians, without compromising performance or requiring time-consuming image annotation.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Jian-Qing Zheng, Ziyang Wang, Baoru Huang, Ngee Han Lim, Bartlomiej W. Papiez
Summary: This article introduces a new method for medical image registration, which utilizes a separable motion backbone and a residual aligner module to better handle the discontinuous motion of multiple neighboring objects. The proposed method achieves excellent registration results on abdominal CT scans and lung CT scans.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangqiong Wu, Guanghua Tan, Hongxia Luo, Zhilun Chen, Bin Pu, Shengli Li, Kenli Li
Summary: This study develops a user-friendly framework for the automated diagnosis of thyroid nodules in ultrasound videos, simulating the diagnostic workflow of radiologists. By interpreting image characteristics and modeling temporal contextual information, the efficiency and generalizability of the diagnosis can be improved.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Riddhish Bhalodia, Shireen Elhabian, Jadie Adams, Wenzheng Tao, Ladislav Kavan, Ross Whitaker
Summary: This paper introduces DeepSSM, a deep learning-based framework for image-to-shape modeling. By learning the functional mapping from images to low-dimensional shape descriptors, DeepSSM can directly infer statistical representation of anatomy from 3D images. Compared to traditional methods, DeepSSM eliminates the need for heavy manual preprocessing and segmentation, and significantly improves computational time.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Florentin Liebmann, Marco von Atzigen, Dominik Stutz, Julian Wolf, Lukas Zingg, Daniel Suter, Nicola A. Cavalcanti, Laura Leoty, Hooman Esfandiari, Jess G. Snedeker, Martin R. Oswald, Marc Pollefeys, Mazda Farshad, Philipp Furnstahl
Summary: This study presents a marker-less approach for automatic registration and real-time navigation of lumbar spinal fusion surgery using a deep neural network, avoiding radiation exposure and surgical errors. The method was validated on an ex-vivo surgery and a public dataset.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Piyush Tiwary, Kinjawl Bhattacharyya, A. P. Prathosh
Summary: Domain shift refers to the change of distributional characteristics between training and testing datasets, leading to performance drop. For medical image tasks, domain shift can be caused by changes in imaging modalities, devices, and staining mechanisms. Existing approaches based on generative models suffer from training difficulties and lack of diversity. In this paper, the authors propose the use of energy-based models (EBMs) for unpaired image-to-image translation in medical images. The proposed method, called Cycle Consistent Twin EBMs (CCT-EBM), employs a pair of EBMs in the latent space of an Auto-Encoder to ensure translation symmetry and coupling between domains.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Yutong Xie, Jianpeng Zhang, Lingqiao Liu, Hu Wang, Yiwen Ye, Johan Verjans, Yong Xia
Summary: This paper proposes a hybrid pre-training paradigm that combines self-supervised learning and supervised learning to improve the representation quality for medical image segmentation tasks. It introduces a reference task in self-supervised learning and optimizes the model using a gradient matching method. The experimental results demonstrate the effectiveness of this approach on multiple medical image segmentation benchmarks.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Youyi Song, Jing Zou, Kup-Sze Choi, Baiying Lei, Jing Qin
Summary: Cell classification is crucial for intelligent cervical cancer screening, but the variation in cells' appearance and shape poses challenges. A new learning algorithm, worse-case boosting, is proposed to improve classification accuracy for under-represented data. Experimental results demonstrate the effectiveness of this algorithm in two publicly available datasets, achieving a 4% improvement in accuracy.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Sangjoon Park, Eun Sun Lee, Kyung Sook Shin, Jeong Eun Lee, Jong Chul Ye
Summary: The increasing demand for AI systems to monitor human errors and abnormalities in healthcare presents challenges. This study presents a model called Medical X-VL, which is tailored for the medical domain and outperformed current state-of-the-art models in two medical image datasets. The model enables various zero-shot tasks for monitoring AI in the medical domain.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Anna Klimovskaia Susmelj, Berkan Lafci, Firat Ozdemir, Neda Davoudi, Xose Luis Dean-Ben, Fernando Perez-Cruz, Daniel Razansky
Summary: Optoacoustic imaging is a technique that uses optical excitation and ultrasound detection for biological tissue imaging. The quality of the images depends on the extent of tomographic coverage provided by the ultrasound detector arrays. However, full coverage is not always possible due to experimental constraints. The proposed signal domain adaptation network aims to reduce limited-view artifacts in the images.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Srijay Deshpande, Muhammad Dawood, Fayyaz Minhas, Nasir Rajpoot
Summary: In this work, a novel framework called SynCLay is proposed for automated synthesis of histology images based on user-defined cellular layouts. The framework can generate realistic and high-quality histology images with different cellular arrangements, which is helpful for studying the role of cells in the tumor microenvironment. The framework integrates a nuclear segmentation and classification model to refine nuclear structures and generate nuclear masks. Evaluation using quantitative metrics and feedback from pathologists shows that the synthetic images generated by SynCLay have high realism scores and can accurately differentiate between benign and malignant tumors.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Ahmed H. Shahin, An Zhao, Alexander C. Whitehead, Daniel C. Alexander, Joseph Jacob, David Barber
Summary: Survival analysis is a valuable tool in healthcare for predicting the time to specific events. This paper introduces CenTime, a novel approach that directly estimates the time to event. The method performs well with censored data and can be easily integrated with deep learning models. Compared to standard methods, CenTime offers superior performance in predicting event time while maintaining comparable ranking performance.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Bingyuan Liu, Jose Dolz, Adrian Galdran, Riadh Kobbi, Ismail Ben Ayed
Summary: Most segmentation losses, such as CE and Dice, are variants of the Cross-Entropy or Dice losses. This work provides a theoretical analysis that shows a deeper connection between CE and Dice than previously thought. From a constrained-optimization perspective, both CE and Dice decompose into similar ground-truth matching terms and region-size penalty terms. The analysis uncovers hidden region-size biases: Dice has an intrinsic bias towards extremely imbalanced solutions, while CE implicitly encourages the ground-truth region proportions. Based on this analysis, a principled and simple solution is proposed to explicitly control the region-size bias.
MEDICAL IMAGE ANALYSIS
(2024)