Microscopic image processing platform for multi-class cell segmentation using deep learning

Background From lung cancer and heart disease to rare disorders, research on almost every disease is speeding up. Microscopic cell image analysis is an important area of medical research. As the first step in analysis, segmentation is a significant clinical concern. However, many complexities, such as variations in cell size or shape, overlapping regions, potential poor contrast, and background noise, make automated segmentation of microscopic images a complicated problem. Moreover, there is a notable deficiency in image processing systems that are both user-friendly and capable of delivering credible results.Methods This paper proposed a microscopic cell processing platform to enable efficient and accurate microscopic image analysis. First, the 2018 Data Science Bowl (DSB2018) dataset from the cell segmentation competition of Kaggle in 2018 is grouped into training, validation, and test sets. Then, U-Net++ incorporated with the watershed algorithm was used for microscopic cell image segmentation tasks. Third, a graphics user interface based on QT was designed to display the segmentation process, fine-tune the model according to clinical needs, and automatically generate diagnostic conclusions.Results The average of intersection over union, precision, recall, and F1-score achieved 0.846, 0.908, 0.925, and 0.917, respectively, which were highly satisfactory results, indicating the efficacy of this platform. Moreover, the mean error of the watershed algorithm achieved 0.113, a margin acceptable in clinical diagnostics. Compared with traditional methods, the proposed method significantly improved the performance.Conclusion With the efficient segmentation based on deep learning and accurate quantitative analysis for the results, this microscopic image processing platform may outperform many existing medical analysis systems and have applications in the field of auxiliary medical diagnosis.