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Choi YK, Feng L, Jeong WK, Kim J. Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity. Brain Inform 2024; 11:15. [PMID: 38833195 DOI: 10.1186/s40708-024-00228-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 05/08/2024] [Indexed: 06/06/2024] Open
Abstract
Mapping neural connections within the brain has been a fundamental goal in neuroscience to understand better its functions and changes that follow aging and diseases. Developments in imaging technology, such as microscopy and labeling tools, have allowed researchers to visualize this connectivity through high-resolution brain-wide imaging. With this, image processing and analysis have become more crucial. However, despite the wealth of neural images generated, access to an integrated image processing and analysis pipeline to process these data is challenging due to scattered information on available tools and methods. To map the neural connections, registration to atlases and feature extraction through segmentation and signal detection are necessary. In this review, our goal is to provide an updated overview of recent advances in these image-processing methods, with a particular focus on fluorescent images of the mouse brain. Our goal is to outline a pathway toward an integrated image-processing pipeline tailored for connecto-informatics. An integrated workflow of these image processing will facilitate researchers' approach to mapping brain connectivity to better understand complex brain networks and their underlying brain functions. By highlighting the image-processing tools available for fluroscent imaging of the mouse brain, this review will contribute to a deeper grasp of connecto-informatics, paving the way for better comprehension of brain connectivity and its implications.
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Affiliation(s)
- Yoon Kyoung Choi
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, South Korea
- Department of Computer Science and Engineering, Korea University, Seoul, South Korea
| | | | - Won-Ki Jeong
- Department of Computer Science and Engineering, Korea University, Seoul, South Korea
| | - Jinhyun Kim
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, South Korea.
- Department of Computer Science and Engineering, Korea University, Seoul, South Korea.
- KIST-SKKU Brain Research Center, SKKU Institute for Convergence, Sungkyunkwan University, Suwon, South Korea.
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2
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Guo X, Zhao F, Zhu J, Zhu D, Zhao Y, Fei P. Rapid 3D isotropic imaging of whole organ with double-ring light-sheet microscopy and self-learning side-lobe elimination. BIOMEDICAL OPTICS EXPRESS 2023; 14:6206-6221. [PMID: 38420327 PMCID: PMC10898557 DOI: 10.1364/boe.505217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/26/2023] [Accepted: 10/30/2023] [Indexed: 03/02/2024]
Abstract
Bessel-like plane illumination forms a new type of light-sheet microscopy with ultra-long optical sectioning distance that enables rapid 3D imaging of fine cellular structures across an entire large tissue. However, the side-lobe excitation of conventional Bessel light sheets severely impairs the quality of the reconstructed 3D image. Here, we propose a self-supervised deep learning (DL) approach that can completely eliminate the residual side lobes for a double-ring-modulated non-diffraction light-sheet microscope, thereby substantially improving the axial resolution of the 3D image. This lightweight DL model utilizes the own point spread function (PSF) of the microscope as prior information without the need for external high-resolution microscopy data. After a quick training process based on a small number of datasets, the grown-up model can restore sidelobe-free 3D images with near isotropic resolution for diverse samples. Using an advanced double-ring light-sheet microscope in conjunction with this efficient restoration approach, we demonstrate 5-minute rapid imaging of an entire mouse brain with a size of ∼12 mm × 8 mm × 6 mm and achieve uniform isotropic resolution of ∼4 µm (1.6-µm voxel) capable of discerning the single neurons and vessels across the whole brain.
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Affiliation(s)
- Xinyi Guo
- School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Fang Zhao
- School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Jingtan Zhu
- MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, 430074, Wuhan, China
| | - Dan Zhu
- MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, 430074, Wuhan, China
- Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yuxuan Zhao
- School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Peng Fei
- School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China
- MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, 430074, Wuhan, China
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Li Z, Shang Z, Liu J, Zhen H, Zhu E, Zhong S, Sturgess RN, Zhou Y, Hu X, Zhao X, Wu Y, Li P, Lin R, Ren J. D-LMBmap: a fully automated deep-learning pipeline for whole-brain profiling of neural circuitry. Nat Methods 2023; 20:1593-1604. [PMID: 37770711 PMCID: PMC10555838 DOI: 10.1038/s41592-023-01998-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 08/02/2023] [Indexed: 09/30/2023]
Abstract
Recent proliferation and integration of tissue-clearing methods and light-sheet fluorescence microscopy has created new opportunities to achieve mesoscale three-dimensional whole-brain connectivity mapping with exceptionally high throughput. With the rapid generation of large, high-quality imaging datasets, downstream analysis is becoming the major technical bottleneck for mesoscale connectomics. Current computational solutions are labor intensive with limited applications because of the exhaustive manual annotation and heavily customized training. Meanwhile, whole-brain data analysis always requires combining multiple packages and secondary development by users. To address these challenges, we developed D-LMBmap, an end-to-end package providing an integrated workflow containing three modules based on deep-learning algorithms for whole-brain connectivity mapping: axon segmentation, brain region segmentation and whole-brain registration. D-LMBmap does not require manual annotation for axon segmentation and achieves quantitative analysis of whole-brain projectome in a single workflow with superior accuracy for multiple cell types in all of the modalities tested.
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Affiliation(s)
- Zhongyu Li
- Division of Neurobiology, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Zengyi Shang
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Jingyi Liu
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Haotian Zhen
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Entao Zhu
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Shilin Zhong
- National Institute of Biological Sciences (NIBS), Beijing, China
| | - Robyn N Sturgess
- Division of Neurobiology, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Yitian Zhou
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Xuemeng Hu
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Xingyue Zhao
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Yi Wu
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Peiqi Li
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Rui Lin
- National Institute of Biological Sciences (NIBS), Beijing, China
| | - Jing Ren
- Division of Neurobiology, MRC Laboratory of Molecular Biology, Cambridge, UK.
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4
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Ushakov DS, Finke S. Tissue optical clearing and 3D imaging of virus infections. Adv Virus Res 2023; 116:89-121. [PMID: 37524483 DOI: 10.1016/bs.aivir.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
Imaging pathogens within 3D environment of biological tissues provides spatial information about their localization and interactions with the host. Technological advances in fluorescence microscopy and 3D image analysis now permit visualization and quantification of pathogens directly in large tissue volumes and in great detail. In recent years large volume imaging became an important tool in virology research helping to understand the properties of viruses and the host response to infection. In this chapter we give a review of fluorescence microscopy modalities and tissue optical clearing methods used for large volume tissue imaging. A summary of recent applications for virus research is provided with particular emphasis on studies using light sheet fluorescence microscopy. We describe the challenges and approaches for volumetric image analysis. Practical examples of volumetric imaging implemented in virology laboratories and addressing specialized research questions, such as virus tropism and immune host response are described. We conclude with an overview of the emerging technologies and their potential for virus research.
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Affiliation(s)
- Dmitry S Ushakov
- Institute for Molecular Virology and Cell Biology, Friedrich-Loeffler-Institut, Greifswald-Insel Riems, Germany.
| | - Stefan Finke
- Institute for Molecular Virology and Cell Biology, Friedrich-Loeffler-Institut, Greifswald-Insel Riems, Germany
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Sadeghi M, Ramos-Prats A, Neto P, Castaldi F, Crowley D, Matulewicz P, Paradiso E, Freysinger W, Ferraguti F, Goebel G. Localization and Registration of 2D Histological Mouse Brain Images in 3D Atlas Space. Neuroinformatics 2023; 21:615-630. [PMID: 37357231 PMCID: PMC10406728 DOI: 10.1007/s12021-023-09632-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/12/2023] [Indexed: 06/27/2023]
Abstract
To accurately explore the anatomical organization of neural circuits in the brain, it is crucial to map the experimental brain data onto a standardized system of coordinates. Studying 2D histological mouse brain slices remains the standard procedure in many laboratories. Mapping these 2D brain slices is challenging; due to deformations, artifacts, and tilted angles introduced during the standard preparation and slicing process. In addition, analysis of experimental mouse brain slices can be highly dependent on the level of expertise of the human operator. Here we propose a computational tool for Accurate Mouse Brain Image Analysis (AMBIA), to map 2D mouse brain slices on the 3D brain model with minimal human intervention. AMBIA has a modular design that comprises a localization module and a registration module. The localization module is a deep learning-based pipeline that localizes a single 2D slice in the 3D Allen Brain Atlas and generates a corresponding atlas plane. The registration module is built upon the Ardent python package that performs deformable 2D registration between the brain slice to its corresponding atlas. By comparing AMBIA's performance in localization and registration to human ratings, we demonstrate that it performs at a human expert level. AMBIA provides an intuitive and highly efficient way for accurate registration of experimental 2D mouse brain images to 3D digital mouse brain atlas. Our tool provides a graphical user interface and it is designed to be used by researchers with minimal programming knowledge.
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Affiliation(s)
- Maryam Sadeghi
- Department of Medical Statistics and Informatics, Medical University of Innsbruck, Innsbruck, Austria.
| | - Arnau Ramos-Prats
- Department of Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Pedro Neto
- Faculty of Engineering, University of Porto, Porto, Portugal
| | - Federico Castaldi
- Department of Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Devin Crowley
- Biomedical Engineering, Johns Hopkins University, Baltimore, United States
| | - Pawel Matulewicz
- Department of Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Enrica Paradiso
- KNAW, Netherlands Institute for Neuroscience, Amsterdam, Netherlands
| | | | - Francesco Ferraguti
- Department of Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Georg Goebel
- Department of Medical Statistics and Informatics, Medical University of Innsbruck, Innsbruck, Austria
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Gui P, He F, Ling BWK, Zhang D, Ge Z. Normal vibration distribution search-based differential evolution algorithm for multimodal biomedical image registration. Neural Comput Appl 2023; 35:1-23. [PMID: 37362574 PMCID: PMC10227826 DOI: 10.1007/s00521-023-08649-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 05/02/2023] [Indexed: 06/28/2023]
Abstract
In linear registration, a floating image is spatially aligned with a reference image after performing a series of linear metric transformations. Additionally, linear registration is mainly considered a preprocessing version of nonrigid registration. To better accomplish the task of finding the optimal transformation in pairwise intensity-based medical image registration, in this work, we present an optimization algorithm called the normal vibration distribution search-based differential evolution algorithm (NVSA), which is modified from the Bernstein search-based differential evolution (BSD) algorithm. We redesign the search pattern of the BSD algorithm and import several control parameters as part of the fine-tuning process to reduce the difficulty of the algorithm. In this study, 23 classic optimization functions and 16 real-world patients (resulting in 41 multimodal registration scenarios) are used in experiments performed to statistically investigate the problem solving ability of the NVSA. Nine metaheuristic algorithms are used in the conducted experiments. When compared to the commonly utilized registration methods, such as ANTS, Elastix, and FSL, our method achieves better registration performance on the RIRE dataset. Moreover, we prove that our method can perform well with or without its initial spatial transformation in terms of different evaluation indicators, demonstrating its versatility and robustness for various clinical needs and applications. This study establishes the idea that metaheuristic-based methods can better accomplish linear registration tasks than the frequently used approaches; the proposed method demonstrates promise that it can solve real-world clinical and service problems encountered during nonrigid registration as a preprocessing approach.The source code of the NVSA is publicly available at https://github.com/PengGui-N/NVSA.
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Affiliation(s)
- Peng Gui
- School of Computer Science, Wuhan University, Wuhan, 430072 People’s Republic of China
- AIM Lab, Faculty of IT, Monash University, Melbourne, VIC 3800 Australia
- Monash-Airdoc Research, Monash University, Melbourne, VIC 3800 Australia
| | - Fazhi He
- School of Computer Science, Wuhan University, Wuhan, 430072 People’s Republic of China
| | - Bingo Wing-Kuen Ling
- School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006 People’s Republic of China
| | - Dengyi Zhang
- School of Computer Science, Wuhan University, Wuhan, 430072 People’s Republic of China
| | - Zongyuan Ge
- AIM Lab, Faculty of IT, Monash University, Melbourne, VIC 3800 Australia
- Monash-Airdoc Research, Monash University, Melbourne, VIC 3800 Australia
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Keller D, Verasztó C, Markram H. Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and in situ RNA hybridization. Front Neuroanat 2023; 17:1118170. [PMID: 37007642 PMCID: PMC10055737 DOI: 10.3389/fnana.2023.1118170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 01/30/2023] [Indexed: 03/17/2023] Open
Abstract
Cells in the mammalian cerebral cortex exhibit layer-dependent patterns in their distribution. Classical methods of determining cell type distributions typically employ a painstaking process of large-scale sampling and characterization of cellular composition. We found that by combining in situ hybridization (ISH) images with cell-type-specific transcriptomes, position-dependent cortical composition in P56 mouse could be estimated in the somatosensory cortex. The method uses ISH images from the Allen Institute for Brain Science. There are two novel aspects of the methodology. First, it is not necessary to select a subset of genes that are particular for a cell type of interest, nor is it necessary to only use ISH images with low variability among samples. Second, the method also compensated for differences in soma size and incompleteness of the transcriptomes. The soma size compensation is particularly important in order to obtain quantitative estimates since relying on bulk expression alone would overestimate the contribution of larger cells. Predicted distributions of broader classes of cell types agreed with literature distributions. The primary result is that there is a high degree of substructure in the distribution of transcriptomic types beyond the resolution of layers. Furthermore, transcriptomic cell types each exhibited characteristic soma size distributions. Results suggest that the method could also be employed to assign transcriptomic cell types to well-aligned image sets in the entire brain.
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Lan YQ, Yu MB, Zhan ZY, Huang YR, Zhao LW, Quan YD, Li ZJ, Sun DF, Wu YL, Wu HY, Liu ZT, Wu KL. Use of a tissue clearing technique combined with retrograde trans-synaptic viral tracing to evaluate changes in mouse retinorecipient brain regions following optic nerve crush. Neural Regen Res 2023; 18:913-921. [DOI: 10.4103/1673-5374.353852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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9
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Cheng X, Zhang Y, Chen R, Qian S, Lv H, Liu X, Zeng S. Anatomical Evidence for Parasympathetic Innervation of the Renal Vasculature and Pelvis. J Am Soc Nephrol 2022; 33:2194-2210. [PMID: 36253054 PMCID: PMC9731635 DOI: 10.1681/asn.2021111518] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 08/08/2022] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND The kidneys critically contribute to body homeostasis under the control of the autonomic nerves, which enter the kidney along the renal vasculature. Although the renal sympathetic and sensory nerves have long been confirmed, no significant anatomic evidence exists for renal parasympathetic innervation. METHODS We identified cholinergic nerve varicosities associated with the renal vasculature and pelvis using various anatomic research methods, including a genetically modified mouse model and immunostaining. Single-cell RNA sequencing (scRNA-Seq) was used to analyze the expression of AChRs in the renal artery and its segmental branches. To assess the origins of parasympathetic projecting nerves of the kidney, we performed retrograde tracing using recombinant adeno-associated virus (AAV) and pseudorabies virus (PRV), followed by imaging of whole brains, spinal cords, and ganglia. RESULTS We found that cholinergic axons supply the main renal artery, segmental renal artery, and renal pelvis. On the renal artery, the newly discovered cholinergic nerve fibers are separated not only from the sympathetic nerves but also from the sensory nerves. We also found cholinergic ganglion cells within the renal nerve plexus. Moreover, the scRNA-Seq analysis suggested that acetylcholine receptors (AChRs) are expressed in the renal artery and its segmental branches. In addition, retrograde tracing suggested vagus afferents conduct the renal sensory pathway to the nucleus of the solitary tract (NTS), and vagus efferents project to the kidney. CONCLUSIONS Cholinergic nerves supply renal vasculature and renal pelvis, and a vagal brain-kidney axis is involved in renal innervation.
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Affiliation(s)
- Xiaofeng Cheng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
- Ministry of Education Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Yongsheng Zhang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
- Ministry of Education Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Ruixi Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
- Ministry of Education Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Shenghui Qian
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
- Ministry of Education Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Haijun Lv
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
- Ministry of Education Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Xiuli Liu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
- Ministry of Education Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
- Ministry of Education Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
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Sun R, Wu J, Miao Y, Ouyang L, Qu L. Progressive 3D biomedical image registration network based on deep self-calibration. Front Neuroinform 2022; 16:932879. [PMID: 36213548 PMCID: PMC9532554 DOI: 10.3389/fninf.2022.932879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 08/29/2022] [Indexed: 11/25/2022] Open
Abstract
Three dimensional deformable image registration (DIR) is a key enabling technique in building digital neuronal atlases of the brain, which can model the local non-linear deformation between a pair of biomedical images and align the anatomical structures of different samples into one spatial coordinate system. And thus, the DIR is always conducted following a preprocessing of global linear registration to remove the large global deformations. However, imperfect preprocessing may leave some large non-linear deformations that cannot be handled well by existing DIR methods. The recently proposed cascaded registration network gives a primary solution to deal with such large non-linear deformations, but still suffers from loss of image details caused by continuous interpolation (information loss problem). In this article, a progressive image registration strategy based on deep self-calibration is proposed to deal with the large non-linear deformations without causing information loss and introducing additional parameters. More importantly, we also propose a novel hierarchical registration strategy to quickly achieve accurate multi-scale progressive registration. In addition, our method can implicitly and reasonably implement dynamic dataset augmentation. We have evaluated the proposed method on both optical and MRI image datasets with obtaining promising results, which demonstrate the superior performance of the proposed method over several other state-of-the-art approaches for deformable image registration.
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Affiliation(s)
- Rui Sun
- Ministry of Education Key Laboratory of Intelligent Computing and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
| | - Jun Wu
- Ministry of Education Key Laboratory of Intelligent Computing and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
- *Correspondence: Jun Wu
| | - Yongchun Miao
- Ministry of Education Key Laboratory of Intelligent Computing and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
| | - Lei Ouyang
- Ministry of Education Key Laboratory of Intelligent Computing and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
| | - Lei Qu
- Ministry of Education Key Laboratory of Intelligent Computing and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China
- Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei, China
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
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11
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Handschuh S, Glösmann M. Mouse embryo phenotyping using X-ray microCT. Front Cell Dev Biol 2022; 10:949184. [PMID: 36187491 PMCID: PMC9523164 DOI: 10.3389/fcell.2022.949184] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 08/22/2022] [Indexed: 11/30/2022] Open
Abstract
Microscopic X-ray computed tomography (microCT) is a structural ex vivo imaging technique providing genuine isotropic 3D images from biological samples at micron resolution. MicroCT imaging is non-destructive and combines well with other modalities such as light and electron microscopy in correlative imaging workflows. Protocols for staining embryos with X-ray dense contrast agents enable the acquisition of high-contrast and high-resolution datasets of whole embryos and specific organ systems. High sample throughput is achieved with dedicated setups. Consequently, microCT has gained enormous importance for both qualitative and quantitative phenotyping of mouse development. We here summarize state-of-the-art protocols of sample preparation and imaging procedures, showcase contemporary applications, and discuss possible pitfalls and sources for artefacts. In addition, we give an outlook on phenotyping workflows using microscopic dual energy CT (microDECT) and tissue-specific contrast agents.
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12
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Richardson DS, Guan W, Matsumoto K, Pan C, Chung K, Ertürk A, Ueda HR, Lichtman JW. TISSUE CLEARING. NATURE REVIEWS. METHODS PRIMERS 2021; 1:84. [PMID: 35128463 PMCID: PMC8815095 DOI: 10.1038/s43586-021-00080-9] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/29/2021] [Indexed: 12/16/2022]
Abstract
Tissue clearing of gross anatomical samples was first described over a century ago and has only recently found widespread use in the field of microscopy. This renaissance has been driven by the application of modern knowledge of optical physics and chemical engineering to the development of robust and reproducible clearing techniques, the arrival of new microscopes that can image large samples at cellular resolution and computing infrastructure able to store and analyze large data volumes. Many biological relationships between structure and function require investigation in three dimensions and tissue clearing therefore has the potential to enable broad discoveries in the biological sciences. Unfortunately, the current literature is complex and could confuse researchers looking to begin a clearing project. The goal of this Primer is to outline a modular approach to tissue clearing that allows a novice researcher to develop a customized clearing pipeline tailored to their tissue of interest. Further, the Primer outlines the required imaging and computational infrastructure needed to perform tissue clearing at scale, gives an overview of current applications, discusses limitations and provides an outlook on future advances in the field.
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Affiliation(s)
- Douglas S. Richardson
- Harvard Center for Biological Imaging, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Webster Guan
- Department of Chemical Engineering, MIT, Cambridge, MA, USA
| | - Katsuhiko Matsumoto
- Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
| | - Chenchen Pan
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig Maximilians University of Munich, Munich, Germany
- Graduate School of Systemic Neurosciences (GSN), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Kwanghun Chung
- Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Broad Institute of Harvard University and MIT, Cambridge, MA, USA
- Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea
- Nano Biomedical Engineering (Nano BME) Graduate Program, Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea
| | - Ali Ertürk
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig Maximilians University of Munich, Munich, Germany
- Graduate School of Systemic Neurosciences (GSN), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Hiroki R. Ueda
- Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
| | - Jeff W. Lichtman
- Harvard Center for Biological Imaging, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
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Abstract
Tissue clearing increases the transparency of late developmental stages and enables deep imaging in fixed organisms. Successful implementation of these methodologies requires a good grasp of sample processing, imaging and the possibilities offered by image analysis. In this Primer, we highlight how tissue clearing can revolutionize the histological analysis of developmental processes and we advise on how to implement effective clearing protocols, imaging strategies and analysis methods for developmental biology.
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Affiliation(s)
| | - Nicolas Renier
- Sorbonne Université, Paris Brain Institute – ICM, INSERM, CNRS, AP-HP, Hôpital de la Pitié Salpêtrière, 75013 Paris, France
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14
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Zhang B, Pas KE, Ijaseun T, Cao H, Fei P, Lee J. Automatic Segmentation and Cardiac Mechanics Analysis of Evolving Zebrafish Using Deep Learning. Front Cardiovasc Med 2021; 8:675291. [PMID: 34179138 PMCID: PMC8221393 DOI: 10.3389/fcvm.2021.675291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 04/20/2021] [Indexed: 01/04/2023] Open
Abstract
Background: In the study of early cardiac development, it is essential to acquire accurate volume changes of the heart chambers. Although advanced imaging techniques, such as light-sheet fluorescent microscopy (LSFM), provide an accurate procedure for analyzing the heart structure, rapid, and robust segmentation is required to reduce laborious time and accurately quantify developmental cardiac mechanics. Methods: The traditional biomedical analysis involving segmentation of the intracardiac volume occurs manually, presenting bottlenecks due to enormous data volume at high axial resolution. Our advanced deep-learning techniques provide a robust method to segment the volume within a few minutes. Our U-net-based segmentation adopted manually segmented intracardiac volume changes as training data and automatically produced the other LSFM zebrafish cardiac motion images. Results: Three cardiac cycles from 2 to 5 days postfertilization (dpf) were successfully segmented by our U-net-based network providing volume changes over time. In addition to understanding each of the two chambers' cardiac function, the ventricle and atrium were separated by 3D erode morphology methods. Therefore, cardiac mechanical properties were measured rapidly and demonstrated incremental volume changes of both chambers separately. Interestingly, stroke volume (SV) remains similar in the atrium while that of the ventricle increases SV gradually. Conclusion: Our U-net-based segmentation provides a delicate method to segment the intricate inner volume of the zebrafish heart during development, thus providing an accurate, robust, and efficient algorithm to accelerate cardiac research by bypassing the labor-intensive task as well as improving the consistency in the results.
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Affiliation(s)
- Bohan Zhang
- Joint Department of Bioengineering, University of Texas (UT) Arlington/(UT) Southwestern, Arlington, TX, United States.,School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Kristofor E Pas
- Joint Department of Bioengineering, University of Texas (UT) Arlington/(UT) Southwestern, Arlington, TX, United States
| | - Toluwani Ijaseun
- Joint Department of Bioengineering, University of Texas (UT) Arlington/(UT) Southwestern, Arlington, TX, United States
| | - Hung Cao
- Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Peng Fei
- School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Juhyun Lee
- Joint Department of Bioengineering, University of Texas (UT) Arlington/(UT) Southwestern, Arlington, TX, United States.,Department of Medical Education, Texas Christian University (TCU) and University of North Texas Health Science Center (UNTHSC) School of Medicine, Fort Worth, TX, United States
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15
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Three-Dimensional X-ray Imaging of β-Galactosidase Reporter Activity by Micro-CT: Implication for Quantitative Analysis of Gene Expression. Brain Sci 2021; 11:brainsci11060746. [PMID: 34199780 PMCID: PMC8230009 DOI: 10.3390/brainsci11060746] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 05/24/2021] [Accepted: 05/27/2021] [Indexed: 02/07/2023] Open
Abstract
Acquisition of detailed anatomical and molecular knowledge from intact biological samples while preserving their native three-dimensional structure is still a challenging issue for imaging studies aiming to unravel a system's functions. Three-dimensional micro-CT X-ray imaging with a high spatial resolution in minimally perturbed naive non-transparent samples has recently gained increased popularity and broad application in biomedical research. Here, we describe a novel X-ray-based methodology for analysis of β-galactosidase (lacZ) reporter-driven gene expression in an intact murine brain ex vivo by micro-CT. The method relies on detection of bromine molecules in the product of the enzymatic β-galactosidase reaction. Enhancement of the X-ray signal is observed specifically in the regions of the murine brain where expression of the lacZ reporter gene is also detected histologically. We performed quantitative analysis of the expression levels of lacZ reporter activity by relative radiodensity estimation of the β-galactosidase/X-gal precipitate in situ. To demonstrate the feasibility of the method, we performed expression analysis of the Tsen54-lacZ reporter gene in the murine brain in a semi-quantitative manner. Human mutations in the Tsen54 gene cause pontocerebellar hypoplasia (PCH), a group of severe neurodegenerative disorders with both mental and motor deficits. Comparing relative levels of Tsen54 gene expression, we demonstrate that the highest Tsen54 expression is observed in anatomical brain substructures important for the normal motor and memory functions in mice.
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