title: Multi-organ segmentation with missing organs in abdominal CT images.
abstract: 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,
authors.affiliation: Department of Computer Science, San Francisco State University, USA. miyukis@sfsu.edu
authors.forename: Miyuki
authors.initials: M
authors.lastname: Suzuki
authors.forename: Marius George
authors.initials: MG
authors.lastname: Linguraru
authors.forename: Kazunori
authors.initials: K
authors.lastname: Okada
mesh_terms: D000465:Algorithms; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011860:Radiography, Abdominal / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014781:Viscera / Q000000981:diagnostic imaging* / Q000601:surgery*