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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

12.04.2021 | Original Article

RPLS-Net: pulmonary lobe segmentation based on 3D fully convolutional networks and multi-task learning

verfasst von: Jinxin Liu, Chengdi Wang, Jixiang Guo, Jun Shao, Xiuyuan Xu, Xiaoxin Liu, Hongxia Li, Weimin Li, Zhang Yi

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 6/2021

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Abstract

Purpose

The robust and automatic segmentation of the pulmonary lobe is vital to surgical planning and regional image analysis of pulmonary related diseases in real-time Computer Aided Diagnosis systems. While a number of studies have examined this issue, the segmentation of unclear borders of the five lobes of the lung remains challenging because of incomplete fissures, the diversity of anatomical pulmonary information, and obstructive lesions caused by pulmonary diseases. This study proposes a model called Regularized Pulmonary Lobe Segmentation Network to accurately predict the lobes as well as the borders.

Methods

First, a 3D fully convolutional network is constructed to extract contextual features from computed tomography images. Second, multi-task learning is employed to learn the segmentations of the lobes and the borders between them to train the neural network to better predict the borders via shared representation. Third, a 3D depth-wise separable de-convolution block is proposed for deep supervision to efficiently train the network. We also propose a hybrid loss function by combining cross-entropy loss with focal loss using adaptive parameters to focus on the tissues and the borders of the lobes.

Results

Experiments are conducted on a dataset annotated by experienced clinical radiologists. A 4-fold cross-validation result demonstrates that the proposed approach can achieve a mean dice coefficient of 0.9421 and average symmetric surface distance of 1.3546 mm, which is comparable to state of the art methods. The proposed approach has the capability to accurately segment voxels that are near the lung wall and fissure.

Conclusion

In this paper, a 3D fully convolutional networks framework is proposed to segment pulmonary lobes in chest CT images accurately. Experimental results show the effectiveness of the proposed approach in segmenting the tissues as well as the borders of the lobes.

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Titel: RPLS-Net: pulmonary lobe segmentation based on 3D fully convolutional networks and multi-task learning
verfasst von: Jinxin Liu
Chengdi Wang
Jixiang Guo
Jun Shao
Xiuyuan Xu
Xiaoxin Liu
Hongxia Li
Weimin Li
Zhang Yi
Publikationsdatum: 12.04.2021
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 6/2021
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-021-02360-x

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Abstract

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