Suzuki Miyuki, Linguraru Marius George, Okada Kazunori
Department of Computer Science, San Francisco State University, USA.
Med Image Comput Comput Assist Interv. 2012;15(Pt 3):418-25. doi: 10.1007/978-3-642-33454-2_52.
Currently, multi-organ segmentation (MOS) in abdominal CT can fail to handle clinical patient population with missing organs due to surgical resection. In order to enable the state-of-the-art MOS for these clinically important cases, we propose (1) automatic missing organ detection (MOD) by testing abnormality of post-surgical organ motion and organ-specific intensity homogeneity, and (2) atlas-based MOS of 10 abdominal organs that handles missing organs automatically. The proposed methods are validated with 44 abdominal CT scans including 9 diseased cases with surgical organ resections, resulting in 93.3% accuracy for MOD and improved overall segmentation accuracy by the proposed MOS method when tested on difficult diseased cases,
目前，腹部CT中的多器官分割（MOS）可能无法处理因手术切除而出现器官缺失的临床患者群体。为了在这些临床上重要的病例中实现最先进的MOS，我们提出：（1）通过测试术后器官运动异常和器官特异性强度均匀性来进行自动缺失器官检测（MOD）；（2）基于图谱的10个腹部器官的MOS，可自动处理缺失器官。我们用44例腹部CT扫描对所提出的方法进行了验证，其中包括9例有手术器官切除的患病病例，MOD的准确率达到93.3%，并且在所提出的MOS方法对困难患病病例进行测试时，整体分割准确率得到了提高。
