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Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data
Anatomic tracing is the gold standard tool for delineating brain connections and for validating more recently developed imaging approaches such as diffusion MRI tractography. A key step in the analysis of data from tracer experiments is the careful, manual charting of fiber trajectories on histologi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10592842/ https://www.ncbi.nlm.nih.gov/pubmed/37873366 http://dx.doi.org/10.1101/2023.09.30.560310 |
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author | Sundaresan, Vaanathi Lehman, Julia F. Maffei, Chiara Haber, Suzanne N. Yendiki, Anastasia |
author_facet | Sundaresan, Vaanathi Lehman, Julia F. Maffei, Chiara Haber, Suzanne N. Yendiki, Anastasia |
author_sort | Sundaresan, Vaanathi |
collection | PubMed |
description | Anatomic tracing is the gold standard tool for delineating brain connections and for validating more recently developed imaging approaches such as diffusion MRI tractography. A key step in the analysis of data from tracer experiments is the careful, manual charting of fiber trajectories on histological sections. This is a very time-consuming process, which limits the amount of annotated tracer data that are available for validation studies. Thus, there is a need to accelerate this process by developing a method for computer-assisted segmentation. Such a method must be robust to the common artifacts in tracer data, including variations in the intensity of stained axons and background, as well as spatial distortions introduced by sectioning and mounting the tissue. The method should also achieve satisfactory performance using limited manually charted data for training. Here we propose the first deeplearning method, with a self-supervised loss function, for segmentation of fiber bundles on histological sections from macaque brains that have received tracer injections. We address the limited availability of manual labels with a semi-supervised training technique that takes advantage of unlabeled data to improve performance. We also introduce anatomic and across-section continuity constraints to improve accuracy. We show that our method can be trained on manually charted sections from a single case and segment unseen sections from different cases, with a true positive rate of ~0.80. We further demonstrate the utility of our method by quantifying the density of fiber bundles as they travel through different white-matter pathways. We show that fiber bundles originating in the same injection site have different levels of density when they travel through different pathways, a finding that can have implications for microstructure-informed tractography methods. The code for our method is available at https://github.com/v-sundaresan/fiberbundle_seg_tracing. |
format | Online Article Text |
id | pubmed-10592842 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-105928422023-10-24 Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data Sundaresan, Vaanathi Lehman, Julia F. Maffei, Chiara Haber, Suzanne N. Yendiki, Anastasia bioRxiv Article Anatomic tracing is the gold standard tool for delineating brain connections and for validating more recently developed imaging approaches such as diffusion MRI tractography. A key step in the analysis of data from tracer experiments is the careful, manual charting of fiber trajectories on histological sections. This is a very time-consuming process, which limits the amount of annotated tracer data that are available for validation studies. Thus, there is a need to accelerate this process by developing a method for computer-assisted segmentation. Such a method must be robust to the common artifacts in tracer data, including variations in the intensity of stained axons and background, as well as spatial distortions introduced by sectioning and mounting the tissue. The method should also achieve satisfactory performance using limited manually charted data for training. Here we propose the first deeplearning method, with a self-supervised loss function, for segmentation of fiber bundles on histological sections from macaque brains that have received tracer injections. We address the limited availability of manual labels with a semi-supervised training technique that takes advantage of unlabeled data to improve performance. We also introduce anatomic and across-section continuity constraints to improve accuracy. We show that our method can be trained on manually charted sections from a single case and segment unseen sections from different cases, with a true positive rate of ~0.80. We further demonstrate the utility of our method by quantifying the density of fiber bundles as they travel through different white-matter pathways. We show that fiber bundles originating in the same injection site have different levels of density when they travel through different pathways, a finding that can have implications for microstructure-informed tractography methods. The code for our method is available at https://github.com/v-sundaresan/fiberbundle_seg_tracing. Cold Spring Harbor Laboratory 2023-10-02 /pmc/articles/PMC10592842/ /pubmed/37873366 http://dx.doi.org/10.1101/2023.09.30.560310 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Sundaresan, Vaanathi Lehman, Julia F. Maffei, Chiara Haber, Suzanne N. Yendiki, Anastasia Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data |
title | Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data |
title_full | Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data |
title_fullStr | Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data |
title_full_unstemmed | Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data |
title_short | Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data |
title_sort | self-supervised segmentation and characterization of fiber bundles in anatomic tracing data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10592842/ https://www.ncbi.nlm.nih.gov/pubmed/37873366 http://dx.doi.org/10.1101/2023.09.30.560310 |
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