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Zhao B, Zhang K, Liu P, Chen Y. Large-scale time-lapse scanning electron microscopy image mosaic using a smooth stitching strategy. Microsc Res Tech 2023. [PMID: 37119500 DOI: 10.1002/jemt.24334] [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: 01/10/2023] [Revised: 03/23/2023] [Accepted: 04/15/2023] [Indexed: 05/01/2023]
Abstract
Due to the trade-off between the field of view and resolution of various microscopes, obtaining a wide-view panoramic image through high-resolution image tiles is frequently encountered and demanded in numerous applications. Here, we propose an automatic image mosaic strategy for sequential 2D time-lapse scanning electron microscopy (SEM) images. This method can accurately compute pairwise translations among serial image tiles with indeterminate overlapping areas. The detection and matching of feature points are limited by geographical coordinates, thus avoiding accidental mismatching. Moreover, the nonlinear deformation of the mosaic part is also taken into account. A smooth stitching field is utilized to gradually transform the perspective transformation in overlapping regions into the linear transformation in non-overlapping regions. Experimental results demonstrate that better image stitching accuracy can be achieved compared with some other image mosaic algorithms. Such a method has potential applications in high-resolution large-area analysis using serial microscopy images. RESEARCH HIGHLIGHTS: An automatic image mosaic strategy for processing sequential scanning electron microscopy images is proposed. A smooth stitching field is applied in the image mosaic. Improved stitching accuracy is achieved compared with other conventional mosaic methods.
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Affiliation(s)
- Binglu Zhao
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, China
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, China
| | - Kaidi Zhang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, China
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, China
| | - Peng Liu
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, China
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, China
| | - Yuhang Chen
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, China
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, China
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Hradecka L, Wiesner D, Sumbal J, Koledova ZS, Maska M. Segmentation and Tracking of Mammary Epithelial Organoids in Brightfield Microscopy. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:281-290. [PMID: 36170389 DOI: 10.1109/tmi.2022.3210714] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
We present an automated and deep-learning-based workflow to quantitatively analyze the spatiotemporal development of mammary epithelial organoids in two-dimensional time-lapse (2D+t) sequences acquired using a brightfield microscope at high resolution. It involves a convolutional neural network (U-Net), purposely trained using computer-generated bioimage data created by a conditional generative adversarial network (pix2pixHD), to infer semantic segmentation, adaptive morphological filtering to identify organoid instances, and a shape-similarity-constrained, instance-segmentation-correcting tracking procedure to reliably cherry-pick the organoid instances of interest in time. By validating it using real 2D+t sequences of mouse mammary epithelial organoids of morphologically different phenotypes, we clearly demonstrate that the workflow achieves reliable segmentation and tracking performance, providing a reproducible and laborless alternative to manual analyses of the acquired bioimage data.
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Deshpande NM, Gite S, Pradhan B, Kotecha K, Alamri A. Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:1970-2001. [PMID: 35135238 DOI: 10.3934/mbe.2022093] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The diagnosis of leukemia involves the detection of the abnormal characteristics of blood cells by a trained pathologist. Currently, this is done manually by observing the morphological characteristics of white blood cells in the microscopic images. Though there are some equipment- based and chemical-based tests available, the use and adaptation of the automated computer vision-based system is still an issue. There are certain software frameworks available in the literature; however, they are still not being adopted commercially. So there is a need for an automated and software- based framework for the detection of leukemia. In software-based detection, segmentation is the first critical stage that outputs the region of interest for further accurate diagnosis. Therefore, this paper explores an efficient and hybrid segmentation that proposes a more efficient and effective system for leukemia diagnosis. A very popular publicly available database, the acute lymphoblastic leukemia image database (ALL-IDB), is used in this research. First, the images are pre-processed and segmentation is done using Multilevel thresholding with Otsu and Kapur methods. To further optimize the segmentation performance, the Learning enthusiasm-based teaching-learning-based optimization (LebTLBO) algorithm is employed. Different metrics are used for measuring the system performance. A comparative analysis of the proposed methodology is done with existing benchmarks methods. The proposed approach has proven to be better than earlier techniques with measuring parameters of PSNR and Similarity index. The result shows a significant improvement in the performance measures with optimizing threshold algorithms and the LebTLBO technique.
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Affiliation(s)
- Nilkanth Mukund Deshpande
- Department of Electronics and Telecommunication, Lavale, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, Maharashtra, India
- Electronics and Telecommunication, Vilad Ghat, Dr. Vithalrao Vikhe Patil College of Engineering, Ahmednagar 414111, India
| | - Shilpa Gite
- Department of Computer Science, Lavale, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, Maharashtra, India
- Symbiosis Center for Applied Artificial Intelligence, Lavale, Symbiosis International (Deemed University), Pune 412115, Maharashtra, India
| | - Biswajeet Pradhan
- Centre for Advanced Modelling and Geospatial Information Systems, School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, NSW 2007, Sydney, Australia
- Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Malaysia
| | - Ketan Kotecha
- Department of Computer Science, Lavale, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, Maharashtra, India
- Symbiosis Center for Applied Artificial Intelligence, Lavale, Symbiosis International (Deemed University), Pune 412115, Maharashtra, India
| | - Abdullah Alamri
- Department of Geology and Geophysics, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
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Hajdowska K, Student S, Borys D. Graph based method for cell segmentation and detection in live-cell fluorescence microscope imaging. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103071] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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