Precise, fast, and automated gel quantification powered by YOLO11 instance segmentation.

Saved in:
Bibliographic Details
Title: Precise, fast, and automated gel quantification powered by YOLO11 instance segmentation.
Authors: Tian Y; School of Automation and Intelligence Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China., Ji W; Shanghai High School, Shanghai, 200231, China., Wang L; School of Automation and Intelligence Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China., Liu W; School of Automation and Intelligence Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China., Zhang Q; School of Automation and Intelligence Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China. Electronic address: billy_z@sjtu.edu.cn., Wang Y; School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, 200240, China. Electronic address: wyx75@sjtu.edu.cn., Cao C; School of Automation and Intelligence Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China. Electronic address: cxcao@sjtu.edu.cn.
Source: Analytica chimica acta [Anal Chim Acta] 2026 May 15; Vol. 1399, pp. 345293. Date of Electronic Publication: 2026 Feb 26.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0370534 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-4324 (Electronic) Linking ISSN: 00032670 NLM ISO Abbreviation: Anal Chim Acta Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1873-4324
DOI:10.1016/j.aca.2026.345293