Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queen's Gate, London SW7 2AZ, UK.
Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queen's Gate, London SW7 2AZ, UK.
Med Image Anal. 2015 Jul;23(1):92-104. doi: 10.1016/j.media.2015.04.015. Epub 2015 May 5.
An automated segmentation method is presented for multi-organ segmentation in abdominal CT images. Dictionary learning and sparse coding techniques are used in the proposed method to generate target specific priors for segmentation. The method simultaneously learns dictionaries which have reconstructive power and classifiers which have discriminative ability from a set of selected atlases. Based on the learnt dictionaries and classifiers, probabilistic atlases are then generated to provide priors for the segmentation of unseen target images. The final segmentation is obtained by applying a post-processing step based on a graph-cuts method. In addition, this paper proposes a voxel-wise local atlas selection strategy to deal with high inter-subject variation in abdominal CT images. The segmentation performance of the proposed method with different atlas selection strategies are also compared. Our proposed method has been evaluated on a database of 150 abdominal CT images and achieves a promising segmentation performance with Dice overlap values of 94.9%, 93.6%, 71.1%, and 92.5% for liver, kidneys, pancreas, and spleen, respectively.
本文提出了一种用于腹部 CT 图像多器官分割的自动化分割方法。该方法采用字典学习和稀疏编码技术，为分割生成目标特定的先验知识。该方法从一组选定的图谱中同时学习具有重构能力的字典和具有判别能力的分类器。基于学习到的字典和分类器，然后生成概率图谱，为未见目标图像的分割提供先验知识。最后通过基于图割方法的后处理步骤得到最终的分割结果。此外，本文还提出了一种基于体素的局部图谱选择策略，以解决腹部 CT 图像中存在的高度个体间变异性问题。还比较了不同图谱选择策略下提出的方法的分割性能。我们的方法已经在 150 个腹部 CT 图像的数据库上进行了评估，对于肝脏、肾脏、胰腺和脾脏，其 Dice 重叠值分别达到 94.9%、93.6%、71.1%和 92.5%，取得了很有前景的分割性能。
