Int J Comput Assist Radiol Surg - Markerless estimation of patient orientation, posture and pose using range and pressure imaging : for automatic patient setup and scanner initialization in tomographic imaging.

Tópicos

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Resumo

RPOSE: 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.

Resumo Limpo

rpose diagnost tomograph imag patient setup scanner initi manual tedious procedur clinic practic fullyautomat detect patient posit orient postur pose patient tabl hold great potenti optim part imag workflow propos markerless framework capabl extract inform within second either rang imag ri pressur imag pi datamethod propos method compos three stage first posit orient reclin patient determin second patient postur classifi third base estim orient postur approxim bodi pose recov fit articul model observ ripi data key issu clinic applic approach requir initi poseresult case studi real data subject perform propos system evalu quantit d timeofflight ri camera pressur sens mattress pi patient orient success determin subject independ modal postur recognit stage method achiev mean classif rate ri pi data respect concern approxim bodi pose estim anatom bodi landmark local accuraci cm ri cm piconclus result indic estim patient posit orient postur pose use ri pi sensor respect feasibl benefici optim workflow diagnost tomograph imag modal achiev compar pose estim result use differ model account modalityspecif characterist pi outperform ri discrimin prone supin postur due distinct pressur distribut human bodi

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