Department of Computing, Imperial College London, London, UK.
IEEE Trans Med Imaging. 2013 Sep;32(9):1723-30. doi: 10.1109/TMI.2013.2265805. Epub 2013 Jun 3.
A robust automated segmentation of abdominal organs can be crucial for computer aided diagnosis and laparoscopic surgery assistance. Many existing methods are specialized to the segmentation of individual organs and struggle to deal with the variability of the shape and position of abdominal organs. We present a general, fully-automated method for multi-organ segmentation of abdominal computed tomography (CT) scans. The method is based on a hierarchical atlas registration and weighting scheme that generates target specific priors from an atlas database by combining aspects from multi-atlas registration and patch-based segmentation, two widely used methods in brain segmentation. The final segmentation is obtained by applying an automatically learned intensity model in a graph-cuts optimization step, incorporating high-level spatial knowledge. The proposed approach allows to deal with high inter-subject variation while being flexible enough to be applied to different organs. We have evaluated the segmentation on a database of 150 manually segmented CT images. The achieved results compare well to state-of-the-art methods, that are usually tailored to more specific questions, with Dice overlap values of 94%, 93%, 70%, and 92% for liver, kidneys, pancreas, and spleen, respectively.
腹部器官的精确自动分割对于计算机辅助诊断和腹腔镜手术辅助至关重要。许多现有的方法都是针对特定器官的分割而专门设计的，难以应对腹部器官形状和位置的可变性。我们提出了一种用于腹部计算机断层扫描（CT）扫描的多器官自动分割的通用方法。该方法基于分层图谱注册和加权方案，通过结合多图谱注册和基于补丁的分割这两种在脑分割中广泛使用的方法的各个方面，从图谱数据库中生成针对目标的先验信息。最终的分割是通过在图割优化步骤中应用自动学习的强度模型获得的，该模型整合了高级别的空间知识。所提出的方法允许处理高个体间变异性，同时具有足够的灵活性，可应用于不同的器官。我们在一个包含 150 张手动分割 CT 图像的数据库上评估了分割结果。与通常针对更具体问题的最先进方法相比，其 Dice 重叠值分别为 94%、93%、70%和 92%，分别用于肝脏、肾脏、胰腺和脾脏。
