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Using SILMAS to improve machine learning-assisted quantification of pathology

Andersson, David LU ; Frantz, David LU ; Kirik, Deniz LU orcid ; Berrocal, Edouard LU and Kristensson, Elias LU (2025) In Optics Letters 50(10). p.3465-3468
Abstract

In this study, we investigate the benefits of using structured illumination light-sheet microscopy with axial sweeping (SILMAS) in the context of neural network-assisted quantification of pathology in volumetric data. A bottleneck for training a neural network is the availability of manually labeled training data that adequately covers the variance in unseen samples, allowing the network to correctly extrapolate. Volumetric data obtained using SILMAS imaging have enhanced uniformity of depth resolution and contrast, which could lead to faster convergence during training and require less training data. Here, we compare the performance of a neural network trained on SILMAS data with that of one trained on data from non-structured axially... (More)

In this study, we investigate the benefits of using structured illumination light-sheet microscopy with axial sweeping (SILMAS) in the context of neural network-assisted quantification of pathology in volumetric data. A bottleneck for training a neural network is the availability of manually labeled training data that adequately covers the variance in unseen samples, allowing the network to correctly extrapolate. Volumetric data obtained using SILMAS imaging have enhanced uniformity of depth resolution and contrast, which could lead to faster convergence during training and require less training data. Here, we compare the performance of a neural network trained on SILMAS data with that of one trained on data from non-structured axially swept light-sheet microscopy. The networks are trained on data from a cleared transgenic mouse brain tissue with the objective of quantifying pathological aggregations of alpha-synuclein (aSyn), which plays an integral role in the development of Parkinson’s disease. Overall, our findings demonstrate that the optical filtering enabled by SILMAS reduces the amount of manual labeling required when training neural networks for quantification.

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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Optics Letters
volume
50
issue
10
pages
4 pages
publisher
Optical Society of America
external identifiers
  • scopus:105005222119
ISSN
0146-9592
DOI
10.1364/OL.551597
language
English
LU publication?
yes
additional info
Publisher Copyright: Journal © 2025 Optica Publishing Group.
id
eb05b33e-9f62-4936-9ce2-5237c6855dbb
date added to LUP
2025-08-05 13:47:42
date last changed
2025-08-05 13:48:11
@article{eb05b33e-9f62-4936-9ce2-5237c6855dbb,
  abstract     = {{<p>In this study, we investigate the benefits of using structured illumination light-sheet microscopy with axial sweeping (SILMAS) in the context of neural network-assisted quantification of pathology in volumetric data. A bottleneck for training a neural network is the availability of manually labeled training data that adequately covers the variance in unseen samples, allowing the network to correctly extrapolate. Volumetric data obtained using SILMAS imaging have enhanced uniformity of depth resolution and contrast, which could lead to faster convergence during training and require less training data. Here, we compare the performance of a neural network trained on SILMAS data with that of one trained on data from non-structured axially swept light-sheet microscopy. The networks are trained on data from a cleared transgenic mouse brain tissue with the objective of quantifying pathological aggregations of alpha-synuclein (aSyn), which plays an integral role in the development of Parkinson’s disease. Overall, our findings demonstrate that the optical filtering enabled by SILMAS reduces the amount of manual labeling required when training neural networks for quantification.</p>}},
  author       = {{Andersson, David and Frantz, David and Kirik, Deniz and Berrocal, Edouard and Kristensson, Elias}},
  issn         = {{0146-9592}},
  language     = {{eng}},
  month        = {{05}},
  number       = {{10}},
  pages        = {{3465--3468}},
  publisher    = {{Optical Society of America}},
  series       = {{Optics Letters}},
  title        = {{Using SILMAS to improve machine learning-assisted quantification of pathology}},
  url          = {{http://dx.doi.org/10.1364/OL.551597}},
  doi          = {{10.1364/OL.551597}},
  volume       = {{50}},
  year         = {{2025}},
}