Fissure Extraction Using Dual Tree Complex Wavelet Transform and Lung Lobe Segmentation from CT Lung Images

Firdouse, M. Jannathl; Balasubramanian, M.

doi:10.1007/978-3-319-57141-6_9

Cited by 1 publication

(2 citation statements)

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“…They used adaptive fissure sweeping to find fissure regions first and then identified fissure locations and curvatures using wavelet transformation. Based on [6], Firdouse et al [7] proposed to use dual tree complex wavelet transform to achieve better detection accuracy. In [8,9], the authors used a multi-atlas approach based on fissures, the lungs, and the bronchial trees to localize the fissures.…”

Section: Introductionmentioning

confidence: 99%

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Learnable interpolation and extrapolation network for fuzzy pulmonary lobe segmentation

Fan¹,

Xu²,

Feng³

et al. 2023

IET Image Processing

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Pulmonary lobe segmentation is an important prerequisite for accurately quantifying pulmonary damage in many pulmonary diseases and planning treatment. However, due to the incomplete lobar structures and morphological changes caused by diseases, the lobe segmentation still encounters great challenges. In this study, a Learnable Interpolation and Extrapolation Network (LIE‐Net) is proposed to form complete and consecutive fissure surfaces by learning to extract information of the fissures from existing fissure points and absent points (unsegmented points belonging to fissures) to predict the z coordinate of the absent fissure points. The completed pulmonary fissures are further used for accurate pulmonary lobe segmentation. Specifically, LIE‐Net takes the coordinate information of existing fissure points (their (x, y, z) coordinates) and absent fissure points (their (x, y) coordinates) as two independent inputs, and predicts the z coordinates of absent points. The proposed LIE‐Net makes voxel‐wise predictions based on the spatial structure characteristics of the lung fissure, and is able to provide a consecutive fissure surface in space. According to the evaluation of radiologists, the lobe segmentation performance was remarkably enhanced in approximately 76% of patients in our additional dataset after the application of LIE‐Net, especially for those cases with large‐scale missing fissures.

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Section: Introductionmentioning

confidence: 99%

“…Based on [6], Firdouse et al. [7] proposed to use dual tree complex wavelet transform to achieve better detection accuracy. In [8, 9], the authors used a multi‐atlas approach based on fissures, the lungs, and the bronchial trees to localize the fissures.…”

Section: Introductionmentioning

confidence: 99%