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Deep Learning-based Automatic Lung Segmentation on Multiresolution CT Scans from Healthy and Fibrotic Lungs in Mice
Francesco Sforazzini1, Patrick Salome1, Mahmoud Moustafa1
1Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany (F.S., P.S., M.M., C.Z., C.S., K.R., N.B., A.K., J.D., A.A., M.K.); Department of Radiation Oncology (F.S., P.S., M.M., C.Z., C.S., K.R., N.B., A.K., J.D., A.A., M.K.) and National Center for Tumor Diseases (NCT) (F.S., P.S., M.M., C.Z., C.S., K.R., N.B., J.D., A.A., M.K.), Heidelberg University Hospital (UKHD), Heidelberg, Germany; German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (F.S., P.S., M.M., C.Z., C.S., K.R., J.D., A.A., M.K.); National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany (F.S., P.S., M.M., C.Z., C.S., K.R., N.B., A.K., J.D., A.A., M.K.); Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg, Germany (F.S., P.S., M.M., C.Z., C.S., K.R., N.B., J.D., A.A., M.K.); Department of Clinical Pathology, Suez Canal University, Ismailia, Egypt (M.M.); Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China (C.Z.); and The D-Laboratory and the M-Laboratory, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, the Netherlands (H.W., L.D., P.L.).
Purpose:
To develop a model to accurately segment mouse lungs with varying levels of fibrosis and investigate its applicability to mouse images with different resolutions.
Materials And Methods:
In this experimental retrospective study, a U-Net was trained to automatically segment lungs on mouse CT images. The model was trained (n = 1200), validated (n = 300), and tested (n = 154) on longitudinally acquired and semiautomatically segmented CT images, which included both healthy and irradiated mice (group A). A second independent group of 237 mice (group B) was used for external testing. The Dice score coefficient (DSC) and Hausdorff distance (HD) were used as metrics to quantify segmentation accuracy. Transfer learning was applied to adapt the model to high-spatial-resolution mouse micro-CT segmentation (n = 20; group C [n = 16 for training and n = 4 for testing]).
Results:
The trained model yielded a high median DSC in both test datasets: 0.984 (interquartile range [IQR], 0.977-0.988) in group A and 0.966 (IQR, 0.955-0.972) in group B. The median HD in both test datasets was 0.47 mm (IQR, 0-0.51 mm [group A]) and 0.31 mm (IQR, 0.30-0.32 mm [group B]). Spatially resolved quantification of differences toward reference masks revealed two hot spots close to the air-tissue interfaces, which are particularly prone to deviation. Finally, for the higher-resolution mouse CT images, the median DSC was 0.905 (IQR, 0.902-0.929) and the median 95th percentile of the HD was 0.33 mm (IQR, 2.61-2.78 mm).
Conclusion:
The developed deep learning-based method for mouse lung segmentation performed well independently of disease state (healthy, fibrotic, emphysematous lungs) and CT resolution.Keywords: Deep Learning, Lung Fibrosis, Radiation Therapy, Segmentation, Animal Studies, CT, Thorax, Lung Supplemental material is available for this article. Published under a CC BY 4.0 license.
