Liver and liver tumor segmentation based on abdomen computed tomography (CT) images is an essential step in computer-assisted clinical interventions. However, liver and tumor segmentation remains the difficult issue in the medical image processing field, which is ascribed to the anatomical complexity of the liver and the poor demarcation between the liver and other nearby organs on the image. The existing 3D automatic liver and tumor segmentation algorithms based on full convolutional networks, such as V-net, have utilized the loss functions on the basis of integration (summing) over a segmented region (like Dice or cross-entropy). Unfortunately, the number of foreground and background voxels is usually highly imbalanced in liver and tumor segmentation tasks. This greatly varies the value of regional loss between various segmentation classes, and affects the training stability and effect. In the present study, an improved V-net algorithm was applied for 3D liver and tumor segmentation based on region and distance metrics. The distance metric-based loss function utilized a distance metric of the contour (or shape) space rather than the area. The model was jointly trained by the original regional loss and the three distance-based loss functions (including Boundary (BD) loss, Hausdorff (HD) loss, and Signed Distance Map (SDM) loss) to solve the problem of the highly unbalanced liver and tumor segmentation. Besides, the algorithm was tested in two databases LiTS 2017 (Technical University of Munich, Munich, Germany, 2017) and 3D-IRCADb (Research Institute against Digestive Cancer, Strasbourg Cedex, France, 2009), and the results proved the effectiveness of improvement.

中文翻译：

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基于区域和距离指标的CNN 3D肝脏和肿瘤分割

基于腹部计算机断层扫描（CT）图像的肝和肝肿瘤分割是计算机辅助临床干预中必不可少的步骤。然而，肝脏和肿瘤的分割仍然是医学图像处理领域中的难题，这归因于肝脏的解剖复杂性以及肝脏与图像上其他附近器官之间的界限不佳。现有的基于完全卷积网络（例如V-net）的3D自动肝和肿瘤分割算法已基于对分割区域（如Dice或交叉熵）的积分（求和）利用了损失函数。不幸的是，前景和背景体素的数量通常在肝脏和肿瘤分割任务中高度不平衡。这极大地改变了各个细分类别之间区域损失的价值，并影响训练的稳定性和效果。在本研究中，基于区域和距离指标，将改进的V-net算法应用于3D肝脏和肿瘤分割。基于距离度量的损失函数利用轮廓（或形状）空间而不是面积的距离度量。该模型由原始区域损失和三个基于距离的损失函数（包括边界（BD）损失，Hausdorff（HD）损失和符号距离图（SDM）损失）共同训练，以解决肝脏高度不平衡的问题和肿瘤分割。此外，该算法在两个数据库LiTS 2017（慕尼黑工业大学，德国慕尼黑，2017）和3D-IRCADb（法国斯特拉斯堡Cedex消化癌症研究所，2009）中进行了测试，结果证明了改进的有效性。