Abstract
Automatic pathological pulmonary lobe segmentation(PPLS) enables regional analyses of lung disease, a clinically important capability. Due to often incomplete lobe boundaries, PPLS is difficult even for experts, and most prior art requires inference from contextual information. To address this, we propose a novel PPLS method that couples deep learning with the random walker (RW) algorithm. We first employ the recent progressive holistically-nested network (P-HNN) model to identify potential lobar boundaries, then generate final segmentations using a RW that is seeded and weighted by the P-HNN output. We are the first to apply deep learning to PPLS. The advantages are independence from prior airway/vessel segmentations, increased robustness in diseased lungs, and methodological simplicity that does not sacrifice accuracy. Our method posts a high mean Jaccard score of \(0.888\pm 0.164\) on a held-out set of 154 CT scans from lung-disease patients, while also significantly (\(p < 0.001\)) outperforming a state-of-the-art method.
This work is supported by the Intramural Research Program of the National Institutes of Health, Clinical Center and the National Institute of Allergy and Infectious Diseases. Support also received by the Natural Sciences and Engineering Research Council of Canada. We also thank Nvidia for the donation of a Tesla K40 GPU.
The rights of this work are transferred to the extent transferable according to title 17 \(\S \) 105 U.S.C.
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George, K., Harrison, A.P., Jin, D., Xu, Z., Mollura, D.J. (2017). Pathological Pulmonary Lobe Segmentation from CT Images Using Progressive Holistically Nested Neural Networks and Random Walker. In: Cardoso, M., et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_23
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