3-D Lung Segmentation by Incremental Constrained Nonnegative Matrix Factorization


Accurate lung segmentation from large-size 3-D chest-computed tomography images is crucial for computer-assisted cancer diagnostics. To efficiently section a 3-D lung, we tend to extract voxel-wise features of spatial image contexts by unsupervised learning with a proposed incremental constrained nonnegative matrix factorization (ICNMF). The method applies smoothness constraints to learn the options, that are more strong to lung tissue inhomogeneities, and therefore, help to raised segment internal lung pathologies than the known state-of-the-art techniques. Compared to the latter, the ICNMF depends less on the domain skilled data and is a lot of simply tuned thanks to solely some control parameters. Additionally, the proposed slice-wise incremental learning with due regard for interslice signal dependencies decreases the computational complexity of the NMF-based segmentation and is scalable to very massive three-D lung pictures. The tactic is quantitatively validated on simulated realistic lung phantoms that mimic different lung pathologies (seven datasets), in vivo datasets for seventeen subjects, and fifty five datasets from the Lobe and Lung Analysis 201one (LOLA11) study. For the in vivo knowledge, the accuracy of our segmentation w.r.t. the ground truth is zero.96 by the Dice similarity coefficient, nine.0 mm by the changed Hausdorff distance, and zero.87% by absolutely the lung volume distinction, which is significantly higher than for the NMF-primarily based segmentation. In spite of not being designed for lungs with severe pathologies and of no agreement between radiologists on the ground truth in such cases, the ICNMF with its total accuracy of zero.965 was ranked fifth among all others in the LOLA11. Once excluding the 9 too pathological cases from the LOLA11 dataset, the ICNMF accuracy increased to 0.986.

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