原著
深層学習とワークステーションを用いた小児生体肝移植後の体積測定
キーワード:
deep convolutional neural network,
volume measurements,
living donor liver transplantation,
computed tomography
2020 年 76 巻 11 号 p. 1133-1142
詳細
2020 年 76 巻 11 号 p. 1133-1142
Purpose: The purpose of this study was to propose a method for segmentation and volume measurement of graft liver and spleen of pediatric transplant recipients on digital imaging and communications in medicine (DICOM) -format images using U-Net and three-dimensional (3-D) workstations (3DWS) . Method: For segmentation accuracy assessments, Dice coefficients were calculated for the graft liver and spleen. After verifying that the created DICOM-format images could be imported using the existing 3DWS, accuracy rates between the ground truth and segmentation images were calculated via mask processing. Result: As per the verification results, Dice coefficients for the test data were as follows: graft liver, 0.758 and spleen, 0.577. All created DICOM-format images were importable using the 3DWS, with accuracy rates of 87.10±4.70% and 80.27±11.29% for the graft liver and spleen, respectively. Conclusion: The U-Net could be used for graft liver and spleen segmentations, and volume measurement using 3DWS was simplified by this method.
