论文标题
眼镜蛇：仅CPU的腹部器官分割
COBRA: Cpu-Only aBdominal oRgan segmentAtion
论文作者
论文摘要
腹部器官分割是一项艰巨且耗时的任务。为了减轻临床专家的负担，非常需要完全自动化的方法。当前的方法由卷积神经网络（CNN）主导，但是计算要求和对大数据集的需求限制了其在实践中的应用。 By implementing a small and efficient custom 3D CNN, compiling the trained model and optimizing the computational graph: our approach produces high accuracy segmentations (Dice Similarity Coefficient (%): Liver: 97.3$\pm$1.3, Kidneys: 94.8$\pm$3.6, Spleen: 96.4$\pm$3.0, Pancreas: 80.9$\pm$10.1) at a rate of每个图像1.6秒。至关重要的是，我们能够仅对CPU（无需GPU）执行细分推断，从而在没有专家硬件的情况下便利地促进了模型的简单和广泛部署。
Abdominal organ segmentation is a difficult and time-consuming task. To reduce the burden on clinical experts, fully-automated methods are highly desirable. Current approaches are dominated by Convolutional Neural Networks (CNNs) however the computational requirements and the need for large data sets limit their application in practice. By implementing a small and efficient custom 3D CNN, compiling the trained model and optimizing the computational graph: our approach produces high accuracy segmentations (Dice Similarity Coefficient (%): Liver: 97.3$\pm$1.3, Kidneys: 94.8$\pm$3.6, Spleen: 96.4$\pm$3.0, Pancreas: 80.9$\pm$10.1) at a rate of 1.6 seconds per image. Crucially, we are able to perform segmentation inference solely on CPU (no GPU required), thereby facilitating easy and widespread deployment of the model without specialist hardware.
