An independent replicability study of MesoNet for automated segmentation and landmark identification in functional widefield optical imaging in mice

Aurora Yuan; Hayden B. Fisher; Jonah Padawer-Curry; Adam Q. Bauer; Brian R. White

An independent replicability study of MesoNet for automated segmentation and landmark identification in functional widefield optical imaging in mice

Aurora Yuan, Hayden B. Fisher, Jonah Padawer-Curry, Adam Q. Bauer, Brian R. White

Research output: Contribution to conference › Paper › peer-review

Abstract

MesoNet is an automated landmark identification and segmentation program for widefield optical imaging in mice. Using a multi-institutional dataset to assess external reliability we find promising results, but errors that prevent use without retraining.

Original language	English
State	Published - 2024
Event	Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics - Fort Lauderdale, United States Duration: Apr 7 2024 → Apr 10 2024

Conference

Conference	Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics
Country/Territory	United States
City	Fort Lauderdale
Period	04/7/24 → 04/10/24

Cite this

Yuan, A., Fisher, H. B., Padawer-Curry, J., Bauer, A. Q., & White, B. R. (2024). An independent replicability study of MesoNet for automated segmentation and landmark identification in functional widefield optical imaging in mice. Paper presented at Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics, Fort Lauderdale, United States.

Yuan, Aurora ; Fisher, Hayden B. ; Padawer-Curry, Jonah et al. / An independent replicability study of MesoNet for automated segmentation and landmark identification in functional widefield optical imaging in mice. Paper presented at Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics, Fort Lauderdale, United States.

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title = "An independent replicability study of MesoNet for automated segmentation and landmark identification in functional widefield optical imaging in mice",

abstract = "MesoNet is an automated landmark identification and segmentation program for widefield optical imaging in mice. Using a multi-institutional dataset to assess external reliability we find promising results, but errors that prevent use without retraining.",

author = "Aurora Yuan and Fisher, {Hayden B.} and Jonah Padawer-Curry and Bauer, {Adam Q.} and White, {Brian R.}",

note = "Publisher Copyright: {\textcopyright} 2024 The Author(s); Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics ; Conference date: 07-04-2024 Through 10-04-2024",

year = "2024",

language = "English",

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Yuan, A, Fisher, HB, Padawer-Curry, J, Bauer, AQ & White, BR 2024, 'An independent replicability study of MesoNet for automated segmentation and landmark identification in functional widefield optical imaging in mice', Paper presented at Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics, Fort Lauderdale, United States, 04/7/24 - 04/10/24.

An independent replicability study of MesoNet for automated segmentation and landmark identification in functional widefield optical imaging in mice. / Yuan, Aurora; Fisher, Hayden B.; Padawer-Curry, Jonah et al.
2024. Paper presented at Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics, Fort Lauderdale, United States.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - An independent replicability study of MesoNet for automated segmentation and landmark identification in functional widefield optical imaging in mice

AU - Yuan, Aurora

AU - Fisher, Hayden B.

AU - Padawer-Curry, Jonah

AU - Bauer, Adam Q.

AU - White, Brian R.

PY - 2024

Y1 - 2024

N2 - MesoNet is an automated landmark identification and segmentation program for widefield optical imaging in mice. Using a multi-institutional dataset to assess external reliability we find promising results, but errors that prevent use without retraining.

AB - MesoNet is an automated landmark identification and segmentation program for widefield optical imaging in mice. Using a multi-institutional dataset to assess external reliability we find promising results, but errors that prevent use without retraining.

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M3 - Paper

AN - SCOPUS:85204894801

T2 - Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

Y2 - 7 April 2024 through 10 April 2024

ER -

Yuan A, Fisher HB, Padawer-Curry J, Bauer AQ, White BR. An independent replicability study of MesoNet for automated segmentation and landmark identification in functional widefield optical imaging in mice. 2024. Paper presented at Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics, Fort Lauderdale, United States.

An independent replicability study of MesoNet for automated segmentation and landmark identification in functional widefield optical imaging in mice

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