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Fine-tuning TrailMap: The utility of transfer learning to improve the performance of deep learning in axon segmentation of light-sheet microscopy images.

التفاصيل البيبلوغرافية
العنوان: Fine-tuning TrailMap: The utility of transfer learning to improve the performance of deep learning in axon segmentation of light-sheet microscopy images.
المؤلفون: Oostrom M; AI & Data Analytics Division, Pacific Northwest National Laboratory, Richland, WA, United States of America., Muniak MA; Vollum Institute, Oregon Health & Science University, Portland, OR, United States of America., Eichler West RM; AI & Data Analytics Division, Pacific Northwest National Laboratory, Richland, WA, United States of America., Akers S; AI & Data Analytics Division, Pacific Northwest National Laboratory, Richland, WA, United States of America., Pande P; Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States of America., Obiri M; AI & Data Analytics Division, Pacific Northwest National Laboratory, Richland, WA, United States of America., Wang W; Appel Alzheimer's Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, United States of America., Bowyer K; Appel Alzheimer's Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, United States of America., Wu Z; Appel Alzheimer's Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, United States of America., Bramer LM; Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States of America., Mao T; Vollum Institute, Oregon Health & Science University, Portland, OR, United States of America., Webb-Robertson BJM; Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States of America.
المصدر: PloS one [PLoS One] 2024 Mar 29; Vol. 19 (3), pp. e0293856. Date of Electronic Publication: 2024 Mar 29 (Print Publication: 2024).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
أسماء مطبوعة: Original Publication: San Francisco, CA : Public Library of Science
مواضيع طبية MeSH: Deep Learning*, Animals ; Mice ; Microscopy ; Axons ; Machine Learning ; Brain/diagnostic imaging
مستخلص: Light-sheet microscopy has made possible the 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual undertaking. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through optimizing transfer learning. We fine-tuned the existing TrailMap model using expert-labeled data from noradrenergic axonal structures in the mouse brain. By changing the cross-entropy weights and using augmentation, we demonstrate a generally improved adjusted F1-score over using the originally trained TrailMap model within our test datasets.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)
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معلومات مُعتمدة: R01 NS081071 United States NS NINDS NIH HHS; R01 NS104944 United States NS NINDS NIH HHS; RF1 MH120119 United States MH NIMH NIH HHS; RF1 MH128969 United States MH NIMH NIH HHS
تواريخ الأحداث: Date Created: 20240329 Date Completed: 20240401 Latest Revision: 20240408
رمز التحديث: 20240408
مُعرف محوري في PubMed: PMC10980229
DOI: 10.1371/journal.pone.0293856
PMID: 38551935
قاعدة البيانات: MEDLINE
الوصف
تدمد:1932-6203
DOI:10.1371/journal.pone.0293856