Abstract
Lung Segmentation is one of the pre-processing steps for lung cancer diagnosis. Segmentation of lung contour is challenging when the nodules are attached to the surrounding tissues of the lung, such as juxta-pleural boundary or vasculature. This paper proposes a lung parenchyma segmentation framework based on multiple image frames with novel approaches for juxta-pleural nodule identification and lung contour correction. The juxta-pleural nodule identification works by computing the distance between the lung borders on adjacent slices. These approaches extract the lung boundary of current and previous slices and calculate the shortest distance between the two boundary contour points to detect the nodule candidates and correct the nodule boundary. These approaches were experimented on at least 11 thoracic image volumes with juxta-pleural nodules from the LIDC-IDRI dataset and achieved an average volumetric overlap fraction of 98.59%. Compared with the other state-of-the-art methods, the proposed method is simple and very efficient for segmenting the lung parenchyma while including the juxta-pleural nodules.
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Data Availability
The datasets generated during and/or analysed during the current study are available in the LIDC-IDRI repository, https://wiki.cancerimagingarchive.net/display/ Public/LIDC-IDRI.
Code Availability
The code for the same is hosted at http://github.com/sujijenkin/border2border.
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Acknowledgements
The authors would like to acknowledge the Kiran Division, Department of Science and Technology, Govt. of India, for funding this research work through the SR/WOSA/ET-153/2017 Research Grant. The authors also thank the anonymous reviewers for their encouraging reviews and recommendations.
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Suji, R.J., Godfrey, W.W. & Dhar, J. Border to border distance based lung parenchyma segmentation including juxta-pleural nodules. Multimed Tools Appl 82, 10421–10443 (2023). https://doi.org/10.1007/s11042-022-13660-y
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DOI: https://doi.org/10.1007/s11042-022-13660-y