Many medical image processing applications rely on targeted regions of interest within a larger volumetric image. Whole-body scans represent an extreme case in which large volumes must be broken into smaller sub-volumes for regional analysis. In this work, we sought automatic solutions to divide medical X-ray computed tomography (CT) images into six main anatomical regions: head, neck, chest, abdomen, pelvis and legs. We implemented and compared three methods: (1) an analytical approach which does not require training and solely relies on utilizing critical points in image intensity profiles to derive cut-planes that divide the scan into the mentioned regions, (2) a classical convolutional neural network (CNN) approach, which classifies each transaxial 2D plane independently and then concatenates classification results, and (3) CNN followed by a context-based correction algorithm (CBCA) which improves the CNN classification using positional relationships between all CT slices. The analytical approach achieved acceptable accuracy for anatomical region segmentation without the need for explicit data labeling and was effective for batch labeling whole-body CTs, greatly reducing manual labeling efforts. CNNs achieved superior accuracy and allowed for rapid development and training, but required labeled data and were susceptible to produce discontinuous anatomical regions and therefore ambiguous anatomical boundaries. Post hoc correction of CNN results using CBCA overcame these limitations, achieving nearly perfect CT slice labeling and anatomical region segmentation.

许多医学图像处理应用程序依赖于较大体积图像中的目标目标区域。全身扫描代表了一种极端情况，在这种情况下，必须将大体积分成较小的子体积以进行区域分析。在这项工作中，我们寻求将医学X射线计算机断层扫描（CT）图像分为六个主要解剖区域的自动解决方案：头部，颈部，胸部，腹部，骨盆和腿部。我们实施并比较了三种方法：（1）一种无需训练的分析方法，仅依靠利用图像强度曲线中的临界点来得出将扫描分为所提及区域的切平面，（2）经典卷积神经网络网络（CNN）方法，该方法将每个跨轴2D平面独立分类，然后合并分类结果，（3）CNN之后是基于上下文的校正算法（CBCA），该算法使用所有CT切片之间的位置关系来改善CNN分类。该分析方法无需明确的数据标记即可实现可接受的解剖区域分割精度，并且对于批量标记全身CT有效，大大减少了手动标记工作。CNN的准确性很高，可以快速开发和训练，但需要标记的数据，并且容易产生不连续的解剖区域，因此产生不明确的解剖边界。使用CBCA事后校正CNN结果克服了这些限制，实现了近乎完美的CT切片标记和解剖区域分割。