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Scalable quorum-based deep neural networks with adversarial learning for automated lung lobe segmentation in fast helical free-breathing CTs

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Fast helical free-breathing CT (FHFBCT) scans are widely used for 5DCT and 5D Cone Beam imaging protocols. For quantitative analysis of lung physiology and function, it is important to segment the lung lobes in these scans. Since the 5DCT protocols use up to 25 FHFBCT scans, it is important that this segmentation task be automated. In this paper, we present a deep neural network (DNN) framework for segmenting the lung lobes in near real time.

Methods

A total of 22 patient datasets (550 3D CT scans) were used for the study. Each of the lung lobes was manually segmented and considered ground-truth. A supervised and constrained generative adversarial network (CGAN) was employed for learning each set of lobe segmentations for each patient with 12 patients designated for training data. The resulting generator DNNs represented the lobe segmentations for each training dataset. A quorum-based algorithm was then implemented to test validation data consisting of 10 separate patient datasets (250 3D CTs). Each of the DNNs predicted their corresponding lobes for the validation data, and equal weights were given to the 12 generator CGANs. The quorum process worked by selecting the weighted average result of all 12 CGAN results for each lobe.

Results

When evaluated against ground-truth segmentations, the quorum-based lobe segmentation was observed to have average structural similarity index, normalized cross-correlation coefficient, and dice coefficient values of 0.929, 0.806, and 0.814, respectively, compared to values of 0.911, 0.698, and 0.696, respectively, using a conventional strategy.

Conclusion

The proposed quorum-based approach computed segmentations with clinically acceptable accuracy in near real time using a multi-GPU-based computing setup. This method is scalable as more patient-specific CGANs can be added to the quorum over time.

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Funding

This work supported by the Tobacco Related Disease Research Program 27IR‐0056, NIH R56 1R56HL139767‐01A1, Ken and Wendy Ruby Foundation, and the UCLA Department of Radiation Oncology.

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Authors and Affiliations

  1. University of California, Los Angeles, CA, 90024, USA

    Bradley Stiehl, Michael Lauria, Kamal Singhrao, Jonathan Goldin, Igor Barjaktarevic, Daniel Low & Anand Santhanam

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  1. Bradley Stiehl
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  2. Michael Lauria
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  3. Kamal Singhrao
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  4. Jonathan Goldin
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  5. Igor Barjaktarevic
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  6. Daniel Low
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  7. Anand Santhanam
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Correspondence to Bradley Stiehl.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Stiehl, B., Lauria, M., Singhrao, K. et al. Scalable quorum-based deep neural networks with adversarial learning for automated lung lobe segmentation in fast helical free-breathing CTs. Int J CARS 16, 1775–1784 (2021). https://doi.org/10.1007/s11548-021-02454-6

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  • DOI: https://doi.org/10.1007/s11548-021-02454-6

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