Convolutional Neural Network Application to Automate the Process of Aliquoting Biosamples

Abstract

To automate the aliquoting process, it is necessary to determine the required depth of immersion of the pipette into the blood serum. This paper presents the results of a study aimed at creating a vision system that makes it possible to determine the position and nature of the fractional interface based on the use of a convolutional neural network. As a result of training on photographic images of tubes ready for aliquoting, the neural network acquired the ability to determine the visible part of the tube, the upper fraction of its contents, and the fibrin strands with high accuracy, allowing the required pipette immersion depth to be calculated.

Keywords

• automation
• aliquoting
• blood serum
• image recognition
• convolutional neural network
• U-Net