Model-Based Esophagus Segmentation from CT Scans Using a Spatial Probability Map
Automatic segmentation of the esophagus from CT data is a challenging problem. Its wall consists of muscle tissue, which has low contrast in CT. Sometimes it is filled with air or remains of orally given contrast agent. While air holes are a clear hint to a human when searching for the esophagus, we found that they are rather distracting to discriminative models of the appearance because of their similarity to the trachea and to lung tissue. However, air inside the respiratory organs can be segmented easily. In this paper, we propose to combine a model based segmentation algorithm of the esophagus with a spatial probability map generated from detected air. Threefold cross-validation on 144 datasets showed that this probability map, combined with a technique that puts more focus on hard cases, increases accuracy by 22％. In contrast to prior work, our method is not only automatic on a manually selected region of interest, but on a whole thoracic CT scan, while our mean segmentation error of 1.80mm is even better.
Johannes Feulner S.Kevin Zhou Martin Huber Alexander Cavallaro Joachim Hornegger Dorin Comaniciu
Pattern Recognition Lab, University of Erlangen-Nuremberg, Germany Siemens Corporate Technology, Erl Siemens Corporate Research, Princeton, NJ, USA Siemens Corporate Technology, Erlangen, Germany Radiology Institute, University Hospital Erlangen, Germany Pattern Recognition Lab, University of Erlangen-Nuremberg, Germany
国际会议
北京
英文
95-102
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）