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A Microscopic Image Processing Platform for Multi-class Cell Segmentation Using Deep Learning
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DOI：10.1016/j.imed.2025.08.002.png)
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Abstract
En 中文
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 on 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 which are both user-friendly and capable of delivering credible results. Methods This paper proposes 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 Watershed algorithm is used for microscopic cell image segmentation tasks. Third, a Graphics User Interface (GUI) based on QT is 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 (IoU), Precision, Recall, and F1-score have achieved 0.846, 0.908, 0.925, and 0.917 respectively, which are highly satisfactory results indicated the efficacy of this platform. Moreover, the mean error of Watershed algorithm has achieved 0.113, a margin acceptable in clinical diagnostics. Compared with traditional methods, the proposed method significantly improves the performance. Conclusions This research focuses on integrating automated segmentation and analysis into an image processing platform. With the efficient segmentation based on deep learning and accurate quantitative analysis for the results, this platform outperforms many existing medical analysis systems and has applications in the field of auxiliary medical diagnosis.
Keywords:
Image Segmentation
Deep Learning
Image Analysis
U-Net++
Watershed
GUI
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