Abstract
The automated analysis of Computed Tomography scans of the lung holds great potential to enhance current clinical workflows for the screening of lung cancer. Among the tasks of interest in such analysis this paper is concerned with the segmentation of lung nodules and their characterization in terms of texture. This paper describes our solution for these two problems in the context of the LNdB challenge, held jointly with ICIAR 2020. We propose a) the optimization of a standard 2D Residual Network, but with a regularization technique adapted for the particular problem of texture classification, and b) a 3D U-Net architecture endowed with residual connections within each block and also connecting the downsampling and the upsampling paths. Cross-validation results indicate that our approach is specially effective for the task of texture classification. In the test set withheld by the organization, the presented method ranked 4th in texture classification and 3rd in the nodule segmentation tasks. Code to reproduce our results is made available at http://www.github.com/agaldran/lndb.
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- 1.
The challenge website at https://lndb.grand-challenge.org/Evaluation/ contains rigorous definitions of each of these quantities.
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Galdran, A., Bouchachia, H. (2020). Residual Networks for Pulmonary Nodule Segmentation and Texture Characterization. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12132. Springer, Cham. https://doi.org/10.1007/978-3-030-50516-5_35
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