title: Markerless estimation of patient orientation, posture and pose using range and pressure imaging : for automatic patient setup and scanner initialization in tomographic imaging.
abstract: OBJECTIVE
  In diagnostic tomographic imaging, patient setup and scanner initialization is a manual, tedious procedure in clinical practice. A fully-automatic detection of the patient's position, orientation, posture and pose on the patient table holds great potential for optimizing this part of the imaging workflow. We propose a markerless framework that is capable of extracting this information within seconds from either range imaging (RI) or pressure imaging (PI) data.
  
  
  METHODS
  The proposed method is composed of three stages: First, the position and orientation of the reclined patient are determined. Second, the patient's posture is classified. Third, based on the estimated orientation and posture, an approximate body pose is recovered by fitting an articulated model to the observed RI/PI data. Being a key issue for clinical application, our approach does not require an initialization pose.
  
  
  RESULTS
  In a case study on real data from 16 subjects, the performance of the proposed system was evaluated quantitatively with a 3-D time-of-flight RI camera and a pressure sensing mattress (PI). The patient orientation was successfully determined for all subjects, independent of the modality. At the posture recognition stage, our method achieved mean classification rates of 79.4% for RI and 95.5% for PI data, respectively. Concerning the approximate body pose estimation, anatomical body landmarks were localized with an accuracy of ±5.84 cm (RI) and ±5.53 cm (PI).
  
  
  CONCLUSIONS
  The results indicate that an estimation of the patient's position, orientation, posture and pose using RI and PI sensors, respectively, is feasible, and beneficial for optimizing the workflow in diagnostic tomographic imaging. Both modalities achieved comparable pose estimation results using different models that account for modality-specific characteristics. PI outperforms RI in discriminating between prone and supine postures due to the distinctive pressure distribution of the human body.
authors.affiliation: Pattern Recognition Lab, Graduate School in Advanced Optical Technologies (SAOT), Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 3, 91058, Erlangen, Germany. robert.grimm@informatik.uni-erlangen.de
authors.forename: Robert
authors.initials: R
authors.lastname: Grimm
authors.forename: Sebastian
authors.initials: S
authors.lastname: Bauer
authors.forename: Johann
authors.initials: J
authors.lastname: Sukkau
authors.forename: Joachim
authors.initials: J
authors.lastname: Hornegger
authors.forename: Günther
authors.initials: G
authors.lastname: Greiner
mesh_terms: D000465:Algorithms; D001331:Automation; D006801:Humans; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging; D055095:Optics and Photonics; D056888:Patient Positioning / Q000379:methods*; D011187:Posture; D011312:Pressure; D014057:Tomography, X-Ray Computed