基于空间聚合的整体嵌套网络胰腺自动分割
Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation
摘要：
准确的器官自动分割是医学图像分析中的一个重要而又具有挑战性的问题。胰腺是一个腹部器官，具有非常高的解剖变异性。这抑制了传统分割方法实现高精度，特别是与其他器官（例如肝脏、心脏或肾脏）相比。在本文中，我们提出了一个整体的学习方法，通过强大的空间聚合，使用随机森林集成语义中级线索的深入学习器官内部和边界地图。我们的方法生成边界保持像素类标签胰腺分割。对82例患者的CT扫描进行定量评价，采用4倍交叉验证。我们实现了（平均$\pm$标准）dev.）在测试中，Dice相似系数为78.01% $\pm$ 8.2%，在相同的评价标准下，显著优于先前最先进的方法71.8% $\pm$ 10.7%。
英文摘要：
Accurate automatic organ segmentation is an important yet challenging problem for medical image analysis. The pancreas is an abdominal organ with very high anatomical variability. This inhibits traditional segmentation methods from achieving high accuracies, especially compared to other organs such as the liver, heart or kidneys. In this paper, we present a holistic learning approach that integrates semantic mid-level cues of deeply-learned organ interior and boundary maps via robust spatial aggregation using random forest. Our method generates boundary preserving pixel-wise class labels for pancreas segmentation. Quantitative evaluation is performed on CT scans of 82 patients in 4-fold cross-validation. We achieve a (mean $\pm$ std. dev.) Dice Similarity Coefficient of 78.01% $\pm$ 8.2% in testing which significantly outperforms the previous state-of-the-art approach of 71.8% $\pm$ 10.7% under the same evaluation criterion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
