PDF(Pubmed)
Abstract：
UNASSIGNED: Recipient donor matching in liver transplantation can require precise estimations of liver volume. Currently utilized demographic-based organ volume estimates are imprecise and nonspecific. Manual image organ annotation from medical imaging is effective; however, this process is cumbersome, often taking an undesirable length of time to complete. Additionally, manual organ segmentation and volume measurement incurs additional direct costs to payers for either a clinician or trained technician to complete. Deep learning-based image automatic segmentation tools are well positioned to address this clinical need.
UNASSIGNED: To build a deep learning model that could accurately estimate liver volumes and create 3D organ renderings from computed tomography (CT) medical images.
UNASSIGNED: We trained a nnU-Net deep learning model to identify liver borders in images of the abdominal cavity. We used 151 publicly available CT scans. For each CT scan, a board-certified radiologist annotated the liver margins (ground truth annotations). We split our image dataset into training, validation, and test sets. We trained our nnU-Net model on these data to identify liver borders in 3D voxels and integrated these to reconstruct a total organ volume estimate.
UNASSIGNED: The nnU-Net model accurately identified the border of the liver with a mean overlap accuracy of 97.5% compared with ground truth annotations. Our calculated volume estimates achieved a mean percent error of 1.92% + 1.54% on the test set.
UNASSIGNED: Precise volume estimation of livers from CT scans is accurate using a nnU-Net deep learning architecture. Appropriately deployed, a nnU-Net algorithm is accurate and quick, making it suitable for incorporation into the pretransplant clinical decision-making workflow.
UNASSIGNED: To build a deep learning model that could accurately estimate liver volumes and create 3D organ renderings from computed tomography (CT) medical images.
UNASSIGNED: We trained a nnU-Net deep learning model to identify liver borders in images of the abdominal cavity. We used 151 publicly available CT scans. For each CT scan, a board-certified radiologist annotated the liver margins (ground truth annotations). We split our image dataset into training, validation, and test sets. We trained our nnU-Net model on these data to identify liver borders in 3D voxels and integrated these to reconstruct a total organ volume estimate.
UNASSIGNED: The nnU-Net model accurately identified the border of the liver with a mean overlap accuracy of 97.5% compared with ground truth annotations. Our calculated volume estimates achieved a mean percent error of 1.92% + 1.54% on the test set.
UNASSIGNED: Precise volume estimation of livers from CT scans is accurate using a nnU-Net deep learning architecture. Appropriately deployed, a nnU-Net algorithm is accurate and quick, making it suitable for incorporation into the pretransplant clinical decision-making workflow.
摘要：
未经证实：肝移植中的受体供体匹配可能需要精确估计肝脏体积。目前使用的基于人口统计的器官体积估计是不精确和非特异性的。来自医学成像的手动图像器官注释是有效的；然而，这个过程很麻烦，通常需要一段不受欢迎的时间来完成。此外,手动器官分割和体积测量会给付款人带来额外的直接成本，以供临床医生或训练有素的技术人员完成。基于深度学习的图像自动分割工具可以很好地满足这种临床需求。
UNASSIGNED：构建一个深度学习模型，该模型可以准确估计肝脏体积并从计算机断层扫描（CT）医学图像创建3D器官渲染图。
UNASSIGNED：我们训练了nnU-Net深度学习模型来识别腹腔图像中的肝脏边界。我们使用了151个公开的CT扫描。对于每次CT扫描，董事会认证的放射科医师注释了肝脏边缘（基本事实注释）。我们把图像数据集分成训练，验证,和测试集。我们在这些数据上训练了我们的nnU-Net模型，以识别3D体素中的肝脏边界，并整合这些以重建总器官体积估计。
UNASSIGNED：nnU-Net模型与地面实况注释相比，以97.5％的平均重叠精度准确地识别了肝脏的边界。我们计算的体积估计值在测试集上实现了1.92%+1.54%的平均百分比误差。
UNASSIGNED：使用nnU-Net深度学习架构，从CT扫描精确估计肝脏体积是准确的。部署得当,nnU-Net算法准确、快速，使其适合纳入移植前临床决策工作流程。
UNASSIGNED：构建一个深度学习模型，该模型可以准确估计肝脏体积并从计算机断层扫描（CT）医学图像创建3D器官渲染图。
UNASSIGNED：我们训练了nnU-Net深度学习模型来识别腹腔图像中的肝脏边界。我们使用了151个公开的CT扫描。对于每次CT扫描，董事会认证的放射科医师注释了肝脏边缘（基本事实注释）。我们把图像数据集分成训练，验证,和测试集。我们在这些数据上训练了我们的nnU-Net模型，以识别3D体素中的肝脏边界，并整合这些以重建总器官体积估计。
UNASSIGNED：nnU-Net模型与地面实况注释相比，以97.5％的平均重叠精度准确地识别了肝脏的边界。我们计算的体积估计值在测试集上实现了1.92%+1.54%的平均百分比误差。
UNASSIGNED：使用nnU-Net深度学习架构，从CT扫描精确估计肝脏体积是准确的。部署得当,nnU-Net算法准确、快速，使其适合纳入移植前临床决策工作流程。
