title: Automatic anatomy recognition in whole-body PET/CT images.
abstract: OBJECTIVE
  Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., "Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images," Med. Image Anal. 18, 752-771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images.
  
  
  METHODS
  The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process, to bring performance to the level achieved on diagnostic CT and MR images in body-region-wise approaches. The intermodality approach fosters the use of already existing fuzzy models, previously created from diagnostic CT images, on PET/CT and other derived images, thus truly separating the modality-independent object assembly anatomy from modality-specific tissue property portrayal in the image.
  
  
  RESULTS
  Key ways of combining the above three basic ideas lead them to 15 different strategies for recognizing objects in PET/CT images. Utilizing 50 diagnostic CT image data sets from the thoracic and abdominal body regions and 16 whole-body PET/CT image data sets, the authors compare the recognition performance among these 15 strategies on 18 objects from the thorax, abdomen, and pelvis in object localization error and size estimation error. Particularly on texture membership images, object localization is within three voxels on whole-body low-dose CT images and 2 voxels on body-region-wise low-dose images of known true locations. Surprisingly, even on direct body-region-wise PET images, localization error within 3 voxels seems possible.
  
  
  CONCLUSIONS
  The previous body-region-wise approach can be extended to whole-body torso with similar object localization performance. Combined use of image texture and intensity property yields the best object localization accuracy. In both body-region-wise and whole-body approaches, recognition performance on low-dose CT images reaches levels previously achieved on diagnostic CT images. The best object recognition strategy varies among objects; the proposed framework however allows employing a strategy that is optimal for each object.
authors.affiliation: College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China and Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104.
authors.forename: Huiqian
authors.initials: H
authors.lastname: Wang
authors.affiliation: Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104.
authors.forename: Jayaram K
authors.initials: JK
authors.lastname: Udupa
authors.affiliation: Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104.
authors.forename: Dewey
authors.initials: D
authors.lastname: Odhner
authors.affiliation: Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104.
authors.forename: Yubing
authors.initials: Y
authors.lastname: Tong
authors.affiliation: Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 and Research Center of Intelligent System and Robotics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
authors.forename: Liming
authors.initials: L
authors.lastname: Zhao
authors.affiliation: Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104.
authors.forename: Drew A
authors.initials: DA
authors.lastname: Torigian
mesh_terms: D000005:Abdomen / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D000328:Adult; D000368:Aged; D001331:Automation; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D049268:Positron-Emission Tomography; D011860:Radiography, Abdominal; D014057:Tomography, X-Ray Computed; D060726:Torso / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D051598:Whole Body Imaging