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Kolluru C, Joseph N, Seckler J, Fereidouni F, Levenson R, Shoffstall A, Jenkins M, Wilson D. NerveTracker: a Python-based software toolkit for visualizing and tracking groups of nerve fibers in serial block-face microscopy with ultraviolet surface excitation images. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:076501. [PMID: 38912214 PMCID: PMC11188586 DOI: 10.1117/1.jbo.29.7.076501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 05/10/2024] [Accepted: 05/13/2024] [Indexed: 06/25/2024]
Abstract
Significance Information about the spatial organization of fibers within a nerve is crucial to our understanding of nerve anatomy and its response to neuromodulation therapies. A serial block-face microscopy method [three-dimensional microscopy with ultraviolet surface excitation (3D-MUSE)] has been developed to image nerves over extended depths ex vivo. To routinely visualize and track nerve fibers in these datasets, a dedicated and customizable software tool is required. Aim Our objective was to develop custom software that includes image processing and visualization methods to perform microscopic tractography along the length of a peripheral nerve sample. Approach We modified common computer vision algorithms (optic flow and structure tensor) to track groups of peripheral nerve fibers along the length of the nerve. Interactive streamline visualization and manual editing tools are provided. Optionally, deep learning segmentation of fascicles (fiber bundles) can be applied to constrain the tracts from inadvertently crossing into the epineurium. As an example, we performed tractography on vagus and tibial nerve datasets and assessed accuracy by comparing the resulting nerve tracts with segmentations of fascicles as they split and merge with each other in the nerve sample stack. Results We found that a normalized Dice overlap (Dice norm ) metric had a mean value above 0.75 across several millimeters along the nerve. We also found that the tractograms were robust to changes in certain image properties (e.g., downsampling in-plane and out-of-plane), which resulted in only a 2% to 9% change to the meanDice norm values. In a vagus nerve sample, tractography allowed us to readily identify that subsets of fibers from four distinct fascicles merge into a single fascicle as we move ∼ 5 mm along the nerve's length. Conclusions Overall, we demonstrated the feasibility of performing automated microscopic tractography on 3D-MUSE datasets of peripheral nerves. The software should be applicable to other imaging approaches. The code is available at https://github.com/ckolluru/NerveTracker.
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Affiliation(s)
- Chaitanya Kolluru
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Naomi Joseph
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - James Seckler
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Farzad Fereidouni
- UC Davis Medical Center, Department of Pathology and Laboratory Medicine, Sacramento, California, United States
| | - Richard Levenson
- UC Davis Medical Center, Department of Pathology and Laboratory Medicine, Sacramento, California, United States
| | - Andrew Shoffstall
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
- Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio, United States
| | - Michael Jenkins
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
- Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio, United States
- Case Western Reserve University, Department of Pediatrics, Cleveland, Ohio, United States
| | - David Wilson
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
- Case Western Reserve University, Department of Radiology, Cleveland, Ohio, United States
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Bishop KW, Erion Barner LA, Han Q, Baraznenok E, Lan L, Poudel C, Gao G, Serafin RB, Chow SSL, Glaser AK, Janowczyk A, Brenes D, Huang H, Miyasato D, True LD, Kang S, Vaughan JC, Liu JTC. An end-to-end workflow for nondestructive 3D pathology. Nat Protoc 2024; 19:1122-1148. [PMID: 38263522 DOI: 10.1038/s41596-023-00934-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 10/23/2023] [Indexed: 01/25/2024]
Abstract
Recent advances in 3D pathology offer the ability to image orders of magnitude more tissue than conventional pathology methods while also providing a volumetric context that is not achievable with 2D tissue sections, and all without requiring destructive tissue sectioning. Generating high-quality 3D pathology datasets on a consistent basis, however, is not trivial and requires careful attention to a series of details during tissue preparation, imaging and initial data processing, as well as iterative optimization of the entire process. Here, we provide an end-to-end procedure covering all aspects of a 3D pathology workflow (using light-sheet microscopy as an illustrative imaging platform) with sufficient detail to perform well-controlled preclinical and clinical studies. Although 3D pathology is compatible with diverse staining protocols and computationally generated color palettes for visual analysis, this protocol focuses on the use of a fluorescent analog of hematoxylin and eosin, which remains the most common stain used for gold-standard pathological reports. We present our guidelines for a broad range of end users (e.g., biologists, clinical researchers and engineers) in a simple format. The end-to-end workflow requires 3-6 d to complete, bearing in mind that data analysis may take longer.
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Affiliation(s)
- Kevin W Bishop
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | | | - Qinghua Han
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Elena Baraznenok
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Lydia Lan
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Biology, University of Washington, Seattle, WA, USA
| | - Chetan Poudel
- Department of Chemistry, University of Washington, Seattle, WA, USA
| | - Gan Gao
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Robert B Serafin
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Sarah S L Chow
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Adam K Glaser
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Andrew Janowczyk
- Department of Biomedical Engineering, Emory University, Atlanta, GA, USA
- Department of Oncology, Division of Precision Oncology, University Hospital of Geneva, Geneva, Switzerland
- Department of Diagnostics, Division of Clinical Pathology, University Hospital of Geneva, Geneva, Switzerland
| | - David Brenes
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Hongyi Huang
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Dominie Miyasato
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Lawrence D True
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Soyoung Kang
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Joshua C Vaughan
- Department of Chemistry, University of Washington, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Jonathan T C Liu
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA.
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