Abstract
Purpose
In selective internal radiation therapy (SIRT), an accurate total liver segmentation is required for activity prescription and absorbed dose calculation. Our goal was to investigate the feasibility of using automatic liver segmentation based on a convolutional neural network (CNN) for CT imaging in SIRT, and the ability of CNN to reduce inter-observer variability of the segmentation.
Methods
A multi-scale CNN was modified for liver segmentation for SIRT patients. The CNN model was trained with 139 datasets from three liver segmentation challenges and 12 SIRT patient datasets from our hospital. Validation was performed on 13 SIRT datasets and 12 challenge datasets. The model was tested on 40 SIRT datasets. One expert manually delineated the livers and adjusted the liver segmentations from CNN for 40 test SIRT datasets. Another expert performed the same tasks for 20 datasets randomly selected from the 40 SIRT datasets. The CNN segmentations were compared with the manual and adjusted segmentations from the experts. The difference between the manual segmentations was compared with the difference between the adjusted segmentations to investigate the inter-observer variability. Segmentation difference was evaluated through dice similarity coefficient (DSC), volume ratio (RV), mean surface distance (MSD), and Hausdorff distance (HD).
Results
The CNN segmentation achieved a median DSC of 0.94 with the manual segmentation and of 0.98 with the manually corrected CNN segmentation, respectively. The DSC between the adjusted segmentations is 0.98, which is 0.04 higher than the DSC between the manual segmentations.
Conclusion
The CNN model achieved good liver segmentations on CT images of good image quality, with relatively normal liver shapes and low tumor burden. 87.5% of the 40 CNN segmentations only needed slight adjustments for clinical use. However, the trained model failed on SIRT data with low dose or contrast, lesions with large density difference from their surroundings, and abnormal liver position and shape. The abovementioned scenarios were not adequately represented in the training data. Despite this limitation, the current CNN is already a useful clinical tool which improves inter-observer agreement and therefore contributes to the standardization of the dosimetry. A further improvement is expected when the CNN will be trained with more data from SIRT patients.
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Funding
This project is funded by the H2020-ITN (MSCA 764458) project Hybrid and by the Research Foundation Flanders (FWO) project G082418N.
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Georg Schramm is supported by NIH Grant 1P41EB017183-01A1 CAI2R TRDP #3. David Robben is employed by icometrix, Leuven, Belgium. Christophe M. Deroose is a Senior Clinical Investigator at the Research Foundation Flanders (FWO). Mark Gooding is employed by Mirada Medical Ltd, Oxford, UK, a medical software company. The department of nuclear medicine at KU Leuven receives support from GE for image reconstruction research. No other potential conflicts of interest relevant to this article exist.
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the Ethics Committee Research of UZ/KU Leuven and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
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This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence)
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Tang, X., Jafargholi Rangraz, E., Coudyzer, W. et al. Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT. Eur J Nucl Med Mol Imaging 47, 2742–2752 (2020). https://doi.org/10.1007/s00259-020-04800-3
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DOI: https://doi.org/10.1007/s00259-020-04800-3