Lung Nodule Segmentation: Exploring Data Efficiency and Advanced Architectures

Nima Shafiei Rezvani Nezhad, Meysam Mansouri, Saeid Ghayour, Ruhollah Abolhasani MD, Shahla Azizi PhD

2025-05-2647th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - EMBC 2025 5Lung Nodule SegmentationSegmentation
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Abstract

This study explores the application of various deep learning models for the segmentation of lung nodules using LIDC- IDRI. Unlike traditional approaches that utilize the full dataset, our work emphasizes the efficacy of training models on a filtered subset of 356 samples. Novel configurations, including attention mechanisms and advanced preprocessing strategies, were em- ployed to optimize segmentation accuracy. Among the models evaluated, the DPLinkNet50 with a Channel Attention Bridge and ResNet backbone demonstrated the highest performance with a Dice score of 0.86 and percision 0.88, significantly outper- forming conventional architectures. This work underscores the potential of leveraging data efficiency and tailored architectures in achieving robust segmentation performance, paving the way for improved computer-aided diagnosis in clinical settings.

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