Protocol to annotate and automate single-cell instance segmentation on stimulated Raman histology using deep learning
Abhishek Bhattacharya, Eric Landgraf, Cheng Jiang, Asadur Chowdury, Akhil Kondepudi, Lin Wang, Edward S Harake, Xinhai Hou, Lisa Walsh, Todd C Hollon · 2025
Read Protocol to annotate and automate single-cell instance segmentation on stimulated Raman histology using deep learning by Abhishek Bhattacharya, Eric Landgraf, Cheng Jiang, Asadur Chowdury, Akhil Kondepudi, Lin Wang, Edward S Harake, Xinhai Hou, Lisa Walsh, Todd C Hollon (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12702318.txt.
Description
Abhishek Bhattacharya and nine co-authors set out a protocol for teaching computers to outline single cells in a new kind of surgical image. The imaging method reads tissue chemistry with light, giving surgeons stain-free views during brain operations. To read such images cell by cell, a model must first learn from hand-labeled examples. The protocol walks through labeling cells with a web tool built for the task.
It then covers training a deep learning model with a paired software library. The images come from real neurosurgical cases taken during operations. The paper aims to make single-cell spatial study of these images routine for other labs.
Catalog record
| CREATOR | Abhishek Bhattacharya, Eric Landgraf, Cheng Jiang, Asadur Chowdury, Akhil Kondepudi, Lin Wang, Edward S Harake, Xinhai Hou, Lisa Walsh, Todd C Hollon |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 79 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |