Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Sundaresan, Vaanathi, Lehman, Julia F., Maffei, Chiara, Haber, Suzanne N., Yendiki, Anastasia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
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
_version_ 1785124352226754560
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
work_keys_str_mv AT sundaresanvaanathi selfsupervisedsegmentationandcharacterizationoffiberbundlesinanatomictracingdata
AT lehmanjuliaf selfsupervisedsegmentationandcharacterizationoffiberbundlesinanatomictracingdata
AT maffeichiara selfsupervisedsegmentationandcharacterizationoffiberbundlesinanatomictracingdata
AT habersuzannen selfsupervisedsegmentationandcharacterizationoffiberbundlesinanatomictracingdata
AT yendikianastasia selfsupervisedsegmentationandcharacterizationoffiberbundlesinanatomictracingdata