Fully Automated Spleen Localization And Segmentation Using Machine Learning And 3D Active Contours
Fully Automated Spleen Localization And Segmentation Using Machine Learning And 3D Active Contours
复制标题
DOI：
10.1109/embc.2018.8512182
复制
发表时间：
2018-07
期刊：
影响因子：
--
通讯作者：
Alexander Wood;S. Soroushmehr;Negar Farzaneh;D. Fessell;Kevin Ward;Jonathan Gryak;Delaram Kahrobaei-;K. Najarian
中科院分区：
文献类型：
--
作者：
Alexander Wood;S. Soroushmehr;Negar Farzaneh;D. Fessell;Kevin Ward;Jonathan Gryak;Delaram Kahrobaei-;K. Najarian
由于脾脏在腹腔内的大小、形状和位置的变化以及腹腔中器官之间的强度值的相似性，CT体积中脾脏的自动分割是困难的。在本文中，我们提出了一种使用训练的分类模型、活动轮廓、解剖信息和自适应特征在腹部轴向CT体积内自动定位和分割脾脏的方法。结果显示，在不同对比阶段，经历各种胸部、腹部和骨盆创伤的患者的平均Dice评分为0.873。
Automated segmentation of the spleen in CT volumes is difficult due to variations in size, shape, and position of the spleen within the abdominal cavity as well as similarity of intensity values among organs in the abdominal cavity. In this paper we present a method for automated localization and segmentation of the spleen within axial abdominal CT volumes using trained classification models, active contours, anatomical information, and adaptive features. The results show an average Dice score of 0.873 on patients experiencing various chest, abdominal, and pelvic traumas taken at different contrast phases.