Automatic Lung Cancer Segmentation in 18 FFDG PET/CT Using a Two-Stage Deep Learning Approach / 대한핵의학회잡지

Purpose@#Since accurate lung cancer segmentation is required to determine the functional volume of a tumor in [ 18 F]FDG PET/CT, we propose a two-stage U-Net architecture to enhance the performance of lung cancer segmentation using [ 18 F]FDG PET/CT. @*Methods@#The whole-body [ 18 F]FDG PET/CT scan data of 887 patients with lung cancer were retrospectively used for network training and evaluation. The ground-truth tumor volume of interest was drawn using the LifeX software. The dataset was randomly partitioned into training, validation, and test sets. Among the 887 PET/CT and VOI datasets, 730 were used to train the proposed models, 81 were used as the validation set, and the remaining 76 were used to evaluate the model. In Stage 1, the global U-net receives 3D PET/CT volume as input and extracts the preliminary tumor area, generating a 3D binary volume as output. In Stage 2, the regional U-net receives eight consecutive PET/CT slices around the slice selected by the Global U-net in Stage 1 and generates a 2D binary image as the output. @*Results@#The proposed two-stage U-Net architecture outperformed the conventional one-stage 3D U-Net in primary lung cancer segmentation. The two-stage U-Net model successfully predicted the detailed margin of the tumors, which was determined by manually drawing spherical VOIs and applying an adaptive threshold. Quantitative analysis using the Dice similarity coefficient confirmed the advantages of the two-stage U-Net. @*Conclusion@#The proposed method will be useful for reducing the time and effort required for accurate lung cancer segmentation in [ 18 F]FDG PET/CT.

Self-supervised PET Denoising / 대한핵의학회잡지

Purpose@#Early deep-learning-based image denoising techniques mainly focused on a fully supervised model that learns how to generate a clean image from the noisy input (noise2clean: N2C). The aim of this study is to explore the feasibility of the self-supervised methods (noise2noise: N2N and noiser2noise: Nr2N) for PET image denoising based on the measured PET data sets by comparing their performance with the conventional N2C model. @*Methods@#For training and evaluating the networks, 18F-FDG brain PET/CT scan data of 14 patients was retrospectively used (10 for training and 4 for testing). From the 60-min list-mode data, we generated a total of 100 data bins with 10-s duration. We also generated 40-s-long data by adding four non-overlapping 10-s bins and 300-s-long reference data by adding all list-mode data. We employed U-Net that is widely used for various tasks in biomedical imaging to train and test proposed denoising models. @*Results@#All the N2C, N2N, and Nr2N were effective for improving the noisy inputs. While N2N showed equivalent PSNR to the N2C in all the noise levels, Nr2N yielded higher SSIM than N2N. N2N yielded denoised images similar to reference image with Gaussian filtering regardless of input noise level. Image contrast was better in the N2N results. @*Conclusion@#The self-supervised denoising method will be useful for reducing the PET scan time or radiation dose.

Massive Thrombosis after Central Venous Catheterization in a Patient with Previously Undiagnosed Behcet's Disease

Thrombosis is an important complication of central venous catheterization. Among the many intrinsic and extrinsic factors, the patient's medical disease can play a role in thrombogenesis. Behcet's disease (BD), classified as a vasculitis, is a multisystem disease involving the small blood vessels. It is often difficult to recognize and diagnose the disease. A 24-yr-old female patient showed massive central venous thrombosis which caused superior vena cava syndrome after subclavian vein catheterization. Twenty days after catheterization, the patient exhibited swelling of the face, neck, and both upper extremities. Despite thrombectomy and continuous anticoagulation therapy, her facial and upper extremity swelling reappeared and follow-up chest computed tomography (CT) showed the recurrent thrombosis in the same central veins previously affected. A diagnosis of BD was then made. Following steroid therapy, neither clinical symptoms nor CT findings suggestive of central venous thrombosis were observed during the subsequent 6-months of follow-up period. This case emphasizes that central venous catheterization in a patient with BD should be performed with great caution.