Enhancing U-Net Segmentation Accuracy Through Comprehensive Data Preprocessing
Sarsembayeva T. Mansurova M. Abdildayeva A. Serebryakov S.
February 2025Multidisciplinary Digital Publishing Institute (MDPI)
Journal of Imaging
2025#11Issue 2
The accurate segmentation of lung regions in computed tomography (CT) scans is critical for the automated analysis of lung diseases such as chronic obstructive pulmonary disease (COPD) and COVID-19. This paper focuses on enhancing the accuracy of U-Net segmentation models through a robust preprocessing pipeline. The pipeline includes CT image normalization, binarization to extract lung regions, and morphological operations to remove artifacts. Additionally, the proposed method applies region-of-interest (ROI) filtering to isolate lung areas effectively. The dataset preprocessing significantly improves segmentation quality by providing clean and consistent input data for the U-Net model. Experimental results demonstrate that the Intersection over Union (IoU) and Dice coefficient exceeded 0.95 on training datasets. This work highlights the importance of preprocessing as a standalone step for optimizing deep learning-based medical image analysis.
computed tomography (CT) , lung segmentation , medical image analysis , morphological filtering , preprocessing pipeline
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Department of Artificial Intelligence and Big Data, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Smart Parking Technologies Ltd., Almaty, 010000, Kazakhstan
Department of Artificial Intelligence and Big Data
Smart Parking Technologies Ltd.
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