DistortCorrect Stitcher: Microscopy Image Stitching with Integrated Optical Distortion Correction
The rapidly advancing field of imaging-based spatial transcriptomics demands microscopy images with larger fields of view (FOV) and high spatial resolution to accurately and efficiently quantify RNA molecules at the single-cell level. This challenge requires advanced image processing algorithms to s...
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Cold Spring Harbor
Cold Spring Harbor Laboratory Press
03.11.2024
Cold Spring Harbor Laboratory |
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| Abstract | The rapidly advancing field of imaging-based spatial transcriptomics demands microscopy images with larger fields of view (FOV) and high spatial resolution to accurately and efficiently quantify RNA molecules at the single-cell level. This challenge requires advanced image processing algorithms to seamlessly stitch multiple microscopic images while overcoming the inherent FOV limitations of microscopes. However, image stitching accuracy is often compromised by optical distortions, which are typically uncorrected or require cumbersome hardware-based calibration. Here, we present DistortCorrect Stitcher, a powerful software package that simultaneously estimates and corrects optical distortion during the stitching process. By iteratively integrating image stitching with distortion correction, our method significantly enhances alignment accuracy without relying on external calibration, providing a robust solution for processing spatial transcriptomics data. DistortCorrect Stitcher maintains high stitching fidelity across large tissue areas, ensuring that the molecular data remain reliable and biologically meaningful. This integrated approach has broad implications for improving spatial biology analysis and enabling more accurate high-throughput assays, thereby accelerating advancements in the field.Competing Interest StatementThe authors have declared no competing interest. |
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| AbstractList | The rapidly advancing field of imaging-based spatial transcriptomics demands microscopy images with larger fields of view (FOV) and high spatial resolution to accurately and efficiently quantify RNA molecules at the single-cell level. This challenge requires advanced image processing algorithms to seamlessly stitch multiple microscopic images while overcoming the inherent FOV limitations of microscopes. However, image stitching accuracy is often compromised by optical distortions, which are typically uncorrected or require cumbersome hardware-based calibration. Here, we present DistortCorrect Stitcher, a powerful software package that simultaneously estimates and corrects optical distortion during the stitching process. By iteratively integrating image stitching with distortion correction, our method significantly enhances alignment accuracy without relying on external calibration, providing a robust solution for processing spatial transcriptomics data. DistortCorrect Stitcher maintains high stitching fidelity across large tissue areas, ensuring that the molecular data remain reliable and biologically meaningful. This integrated approach has broad implications for improving spatial biology analysis and enabling more accurate high-throughput assays, thereby accelerating advancements in the field.Competing Interest StatementThe authors have declared no competing interest. The rapidly advancing field of imaging-based spatial transcriptomics demands microscopy images with larger fields of view (FOV) and high spatial resolution to accurately and efficiently quantify RNA molecules at the single-cell level. This challenge requires advanced image processing algorithms to seamlessly stitch multiple microscopic images while overcoming the inherent FOV limitations of microscopes. However, image stitching accuracy is often compromised by optical distortions, which are typically uncorrected or require cumbersome hardware-based calibration. Here, we present DistortCorrect Stitcher, a powerful software package that simultaneously estimates and corrects optical distortion during the stitching process. By iteratively integrating image stitching with distortion correction, our method significantly enhances alignment accuracy without relying on external calibration, providing a robust solution for processing spatial transcriptomics data. DistortCorrect Stitcher maintains high stitching fidelity across large tissue areas, ensuring that the molecular data remain reliable and biologically meaningful. This integrated approach has broad implications for improving spatial biology analysis and enabling more accurate high-throughput assays, thereby accelerating advancements in the field. |
| Author | Hu Cang |
| Author_xml | – sequence: 1 givenname: Hu surname: Cang fullname: Cang, Hu email: hucang@gmail.com organization: Department of Developmental and Cell Biology, University of California Irvine – sequence: 2 givenname: Huaxun surname: Fan fullname: Fan, Huaxun organization: Department of Biochemistry, School of Molecular and Cellular Biology, University of Illinois at Urbana-Champaign – sequence: 3 givenname: Sha surname: Sun fullname: Sun, Sha organization: Department of Developmental and Cell Biology, University of California Irvine – sequence: 4 givenname: Kai surname: Zhang fullname: Zhang, Kai organization: Department of Biochemistry, School of Molecular and Cellular Biology, University of Illinois at Urbana-Champaign |
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| DOI | 10.1101/2024.10.28.620720 |
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| Title | DistortCorrect Stitcher: Microscopy Image Stitching with Integrated Optical Distortion Correction |
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