Semantic Scholar Open Access 2023 206 sitasi

Segment Anything for Microscopy

Anwai Archit Sushmita Nair Nabeel Khalid Paul Hilt Vikas Rajashekar +5 lainnya

Lihat Sumber DOI

Abstrak

We present Segment Anything for Microscopy, a tool for interactive and automatic segmentation and tracking of objects in multi-dimensional microscopy data. Our method is based on Segment Anything, a vision foundation model for image segmentation. We extend it by training specialized models for microscopy data that significantly improve segmentation quality for a wide range of imaging conditions. We also implement annotation tools for interactive (volumetric) segmentation and tracking, that speed up data annotation significantly compared to established tools. Our work constitutes the first application of vision foundation models to microscopy, laying the groundwork for solving image analysis problems in these domains with a small set of powerful deep learning architectures.

Topik & Kata Kunci

Medicine Biology

Penulis (10)

Anwai Archit

Sushmita Nair

Nabeel Khalid

Paul Hilt

Vikas Rajashekar

Marei Freitag

Sagnik Gupta

A. Dengel

Sheraz Ahmed

Constantin Pape

Format Sitasi

APA MLA BibTeX

Archit, A., Nair, S., Khalid, N., Hilt, P., Rajashekar, V., Freitag, M. et al. (2023). Segment Anything for Microscopy. https://doi.org/10.1038/s41592-024-02580-4

Akses Cepat

Lihat di Sumber doi.org/10.1038/s41592-024-02580-4

Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Total Sitasi: 206×
Sumber Database: Semantic Scholar
DOI: 10.1038/s41592-024-02580-4
Akses: Open Access ✓