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myst.yml
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# See docs at: https://mystmd.org/guide/frontmatter
version: 1
project:
doi: 10.69761/HDTA8338
id: msa-em-microscopy-seg-track-1
title: 'Deep Learning Applications in Microscopy: Segmentation and Tracking'
description: Comparative analysis of recent computer vision models in microscopic image segmentation (including EfficientSAM-tiny, YOLOv8n-seg, Swin-UNet, and VMamba). Integration of EfficientSAM-tiny and DeAOT for video tracking in high-temperature sintering processes under a microscope.
short_title: Microscopy Segment Track
keywords:
- deep learning
- transmission electron microscopy
- image segmentation
- bright field transmission electron microscopy
- nanoparticles
github: https://github.com/msa-em/microscopy-seg-track
jupyter: true
# jupyter:
# binder:
# url: https://y2j74k9mn0bz.curvenote.dev/services/binder/
# repo: msa-em/microscopy-seg-track
# jupyter:
# server:
# url: 'http://localhost:8888'
# token: '512ac78f14e1141db1fac17e8b4099c1e5bc7d589518b38c'
open_access: true
license: CC-BY-4.0
banner: banner.png
thumbnail: thumbnail.webp
date: 2024-10-03
history:
submitted: 2024-09-19
published: 2024-10-03
venue:
title: Elemental Microscopy
short_title: EM
doi: 10.69761/EM
requirements:
- 'environment.yml'
resources:
- 'notebooks/**/*'
references:
mystmd: https://mystmd.org/guide
exports:
- format: meca
exclude:
- README.md
authors:
- id: yifeiduan
name: Yifei Duan
equal_contributor: true
email: [email protected]
# https://orcid.org/
orcid: 0009-0001-4593-921X
affiliations:
- id: upennmse
department: Department of Materials Science and Engineering
institution: University of Pennsylvania
address: 3231 Walnut Street, Philadelphia, Pennsylvania, USA, 19104
# https://ror.org/00b30xv10
ror: 00b30xv10
roles:
# https://credit.niso.org/
- Conceptualization
- Data curation
- Formal analysis
- Investigation
- Methodology
- Project administration
- Software
- Validation
- Visualization
- Writing - original draft
note: Yifei Duan and Yifan Duan contributed equally to this work as co-first authors.
- id: yifanduan
name: Yifan Duan
equal_contributor: true
email: [email protected]
orcid: 0009-0008-2922-3028
affiliations:
- id: berkeleymse
department: Department of Materials Science and Engineering
institution: University of California, Berkeley
address: 210 Hearst Memorial Mining Building, Berkeley, California, USA, 94720
ror: 01an7q238
roles:
- Conceptualization
- Formal analysis
- Investigation
- Methodology
- Software
- Validation
- Visualization
- Writing - original draft
- Writing - review & editing
note: Yifei Duan and Yifan Duan contributed equally to this work as co-first authors.
- id: zequnhe
name: Zequn He
email: [email protected]
orcid: 0000-0002-8565-0170
affiliations:
- id: upennmeam
department: A Department of the School of Engineering and Applied Science
institution: University of Pennsylvania
address: 220 South 33rd Street, Philadelphia, Pennsylvania, USA, 19104
ror: 00b30xv10
roles:
- Conceptualization
- Data curation
- Writing - review & editing
- id: chengyuchen
name: Cheng-Yu Chen
email: [email protected]
orcid: 0009-0009-6193-2358
affiliations: [upennmse]
roles:
- Data curation
- Writing - review & editing
- id: ericastach
name: Eric A. Stach
corresponding: true
email: [email protected]
orcid: 0000-0002-3366-2153
affiliations: [upennmse]
roles:
- Conceptualization
- Project administration
- Resources
- Supervision
- Writing - review & editing
funding:
- statement: |
This work was carried out in part at the Singh Center for Nanotechnology,
which is supported by the NSF National Nanotechnology Coordinated Infrastructure
Program under grant NNCI-2025608 and through the use of facilities supported by
the University of Pennsylvania Materials Research Science and Engineering
Center (MRSEC) DMR-2309043.
awards:
- id: NNCI-2025608
source: NSF National Nanotechnology Coordinated Infrastructure Program
- id: DMR-2309043
source: University of Pennsylvania Materials Research Science and Engineering Center
- statement: |
C.-Y. C. and E.A.S. acknowledge additional support through the NSF Division of
Materials Research's Metals and Metallic Nanostructures program, DMR-2303084.
awards:
- id: DMR-2303084
source: NSF Division of Materials Research's Metals and Metallic Nanostructures
abbreviations:
CLIP: Contrastive Language-Image Pre-training
CNN: Convolutional Neural Networks
CV: computer vision
DeAOT: Decoupling Associating Objects with Transformers
Dice Coefficient: Dice-Sørensen Coefficient
EfficientSAM: Efficient Segment Anything Model
FCNN: Fully Convolutional Neural Networks
hBN: Hexagonal Boron Nitride
IoU: Intersection over Union
LISA: Large Language Instructed Segmentation Assistant
LLM: Large Language Model
LLaMa: Large Language Model Meta AI
LSTR: Long Short-term TRansformer
MAE: Masked Autoencoder
Mask R-CNN: mask regional-based convolutional neural network
S4: Structured State Space sequence model
SAM: Segment Anything Model
SAMI: SAM-leveraged masked image pertraining
SSM: State Space Model
Swin-UNet: Shifted Windows U-shaped network
TEM: Transmission Electron Microscopy
U-Net: U-shaped network
VMamba: Vision Mamba
VRAM: Video Random Access Memory
VSS: Visual State Space
ViT: Vision Transformer
YOLO: You Only Look Once (Real-Time Object Detection)
site:
template: book-theme