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

Area of Science:

• Analytical Chemistry
• Proteomics
• Biotechnology
• Computational Biology

Background:

• Gel electrophoresis image analysis is hampered by manual labor, subjectivity, and artifacts like lane distortion.
• Traditional densitometry methods struggle with accuracy due to rigid lane assumptions and complex backgrounds.
• Existing automated solutions often lack precise quantification or require extensive preprocessing.

Purpose of the Study:

• To develop an end-to-end, fully automated framework for gel electrophoresis band segmentation.
• To overcome the limitations of manual analysis and traditional densitometry for improved accuracy and speed.
• To provide a robust and objective alternative for high-throughput proteomics and analytical chemistry workflows.

Main Methods:

• Implementation of a lightweight YOLO11-Seg architecture for automated band segmentation.
• Utilized transfer learning for cross-stain compatibility (Coomassie, silver, fluorescence) on diverse datasets.
• Processed high-resolution images without preprocessing, employing pixel-level masks for quantification.

Main Results:

• Achieved high segmentation accuracy (mAP50 = 0.947) with rapid processing (3.1 ms latency).
• Demonstrated superior performance over U-Net and profile-based methods in resolving faint/distorted bands.
• Exhibited excellent linearity (R² = 0.9964) and low variability (CV = 3.3%) for protein quantification.
• Reduced analysis time from 4 minutes (manual) to approximately 1 second per gel.

Conclusions:

• The YOLO11-Seg framework offers a robust, objective, and high-throughput solution for gel electrophoresis analysis.
• Eliminates dependence on manual intervention and lane division, resolving the speed-precision trade-off.
• Lightweight design and cross-stain generalizability make it a practical tool for routine large-scale applications.