A comparison of the two transfer learning methods

The dataset used in this study included 18 non-small cell lung cancer (NSCLC) patients’ planning 4D-CT data from the Cancer Imaging Archive. Each patient in the dataset had 5 to 7 4D-CT sets that covered their periods of treatment, which ranged from 25 to 35 days. To reconstruct the CBCT image, we used the sixth phase of the CT image set for each patient and simulated 360-projection data sets from the 3D volume. From the 360-projection data sets, we then used 72 (20%) projections to reconstruct the limited-projection CBCT images using the FDK algorithm. The training sets for the basic U-Net model only used the first day’s CT image sets of 17 patients. In training the network, we shuffled the paired 4D-CT images and CBCT images and extracted 5% of the data for network validation. This corresponded to 2,040 slices of images for the training and validation data. The remaining patient’s data were used for transfer-learning and network-testing purposes. Written informed consent was obtained from the patient to publish the results and any accompanying images in this study.

In this study, a U-Net model was first trained with the group data of 17 patients. We then conducted experiments on the two different transfer learning methods mentioned above using the testing data. The first days’ 4D-CT images from the testing data and the corresponding limited-projection CBCT images were used for transfer learning. A basic U-Net model, a layer-freezing model, and a whole-network fine-tuning model were tested with the second day’s 4D-CBCT images reconstructed from 72 projections.

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