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29 May 2024 Quantifying lung fissure integrity using a three-dimensional patch-based convolutional neural network on CT images for emphysema treatment planning
Dallas K. Tada, Pangyu Teng, Kalyani Vyapari, Ashley Banola, George Foster, Esteban Diaz, Grace Hyun J. Kim, Jonathan G. Goldin, Fereidoun Abtin, Michael McNitt-Gray, Matthew S. Brown
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Journal of Medical Imaging, Vol. 11, Issue 3, 034502 (May 2024). https://doi.org/10.1117/1.JMI.11.3.034502
Abstract

Purpose

Evaluation of lung fissure integrity is required to determine whether emphysema patients have complete fissures and are candidates for endobronchial valve (EBV) therapy. We propose a deep learning (DL) approach to segment fissures using a three-dimensional patch-based convolutional neural network (CNN) and quantitatively assess fissure integrity on CT to evaluate it in subjects with severe emphysema.

Approach

From an anonymized image database of patients with severe emphysema, 129 CT scans were used. Lung lobe segmentations were performed to identify lobar regions, and the boundaries among these regions were used to construct approximate interlobar regions of interest (ROIs). The interlobar ROIs were annotated by expert image analysts to identify voxels where the fissure was present and create a reference ROI that excluded non-fissure voxels (where the fissure is incomplete). A CNN configured by nnU-Net was trained using 86 CT scans and their corresponding reference ROIs to segment the ROIs of left oblique fissure (LOF), right oblique fissure (ROF), and right horizontal fissure (RHF). For an independent test set of 43 cases, fissure integrity was quantified by mapping the segmented fissure ROI along the interlobar ROI. A fissure integrity score (FIS) was then calculated as the percentage of labeled fissure voxels divided by total voxels in the interlobar ROI. Predicted FIS (p-FIS) was quantified from the CNN output, and statistical analyses were performed comparing p-FIS and reference FIS (r-FIS).

Results

The absolute percent error mean (±SD) between r-FIS and p-FIS for the test set was 4.0% (±4.1%), 6.0% (±9.3%), and 12.2% (±12.5%) for the LOF, ROF, and RHF, respectively.

Conclusions

A DL approach was developed to segment lung fissures on CT images and accurately quantify FIS. It has potential to assist in the identification of emphysema patients who would benefit from EBV treatment.
© 2024 Society of Photo-Optical Instrumentation Engineers (SPIE)
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Dallas K. Tada, Pangyu Teng, Kalyani Vyapari, Ashley Banola, George Foster, Esteban Diaz, Grace Hyun J. Kim, Jonathan G. Goldin, Fereidoun Abtin, Michael McNitt-Gray, and Matthew S. Brown "Quantifying lung fissure integrity using a three-dimensional patch-based convolutional neural network on CT images for emphysema treatment planning," Journal of Medical Imaging 11(3), 034502 (29 May 2024). https://doi.org/10.1117/1.JMI.11.3.034502
Received: 13 December 2023; Accepted: 3 May 2024; Published: 29 May 2024
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KEYWORDS
Image segmentation
Lung
Voxels
Education and training
Radio over Fiber
Computed tomography
Emphysema
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