Dr. Bong-Seog Kim Profile

Dr. Bong-Seog Kim

at Boryung

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Proceedings Article | 3 April 2024 Poster + Paper

Enhancing lung tumor segmentation: a comparative study of CNN-based network with multi-scale strategies and attention mechanisms and hybrid transformer-CNN network

Hye Ryun Kim, Jumin Lee, Helen Hong, Bong-Seog Kim

Proceedings Volume 12927, 129272K (2024) https://doi.org/10.1117/12.3004691

KEYWORDS: Convolutional neural networks, Tumors, Lung, Image segmentation, Transformers, Computed tomography

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This study is to evaluate the performance of a CNN-based network with multi-scale dual attention module and a hybrid Transformer-CNN network, aiming to identify the best practices for segmenting lung tumors with significant variability. Moreover, we evaluate and compare the use of size-invariant patches, which maintain consistent tumor-tobackground ratios regardless of tumor size, against the use of the original patches. To assess the performance of two different networks, we evaluate MSDA-Net and TransUNet, using original and size-invariant patches, respectively. The results reveal practical insights into the segmentation of both small-sized and large-sized tumors. In small-sized tumor segmentation, employing size-invariant patches helps achieve better segmentation results by addressing the variability in tumor sizes and ensuing a consistent scale for analysis. For large-sized tumor segmentation, using original patch helps preserve more context information, enabling better segmentation results. Additionally, our results indicate that the CNN-based network with multi-scale dual attention modules outperformed the hybrid Transformer-CNN network in providing more robust segmentation results for both small-sized and large-sized tumors.

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