title: Interactive lung segmentation in abnormal human and animal chest CT scans.
abstract: OBJECTIVE
  Many medical image analysis systems require segmentation of the structures of interest as a first step. For scans with gross pathology, automatic segmentation methods may fail. The authors' aim is to develop a versatile, fast, and reliable interactive system to segment anatomical structures. In this study, this system was used for segmenting lungs in challenging thoracic computed tomography (CT) scans.
  
  
  METHODS
  In volumetric thoracic CT scans, the chest is segmented and divided into 3D volumes of interest (VOIs), containing voxels with similar densities. These VOIs are automatically labeled as either lung tissue or nonlung tissue. The automatic labeling results can be corrected using an interactive or a supervised interactive approach. When using the supervised interactive system, the user is shown the classification results per slice, whereupon he/she can adjust incorrect labels. The system is retrained continuously, taking the corrections and approvals of the user into account. In this way, the system learns to make a better distinction between lung tissue and nonlung tissue. When using the interactive framework without supervised learning, the user corrects all incorrectly labeled VOIs manually. Both interactive segmentation tools were tested on 32 volumetric CT scans of pigs, mice and humans, containing pulmonary abnormalities.
  
  
  RESULTS
  On average, supervised interactive lung segmentation took under 9 min of user interaction. Algorithm computing time was 2 min on average, but can easily be reduced. On average, 2.0% of all VOIs in a scan had to be relabeled. Lung segmentation using the interactive segmentation method took on average 13 min and involved relabeling 3.0% of all VOIs on average. The resulting segmentations correspond well to manual delineations of eight axial slices per scan, with an average Dice similarity coefficient of 0.933.
  
  
  CONCLUSIONS
  The authors have developed two fast and reliable methods for interactive lung segmentation in challenging chest CT images. Both systems do not require prior knowledge of the scans under consideration and work on a variety of scans.
authors.affiliation: Image Sciences Institute, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.
authors.forename: Thessa T J P
authors.initials: TT
authors.lastname: Kockelkorn
authors.affiliation: Department of Radiology, Meander Medical Centre, 3813 TZ Amersfoort, The Netherlands and Diagnostic Image Analysis Group, Radboud University Nijmegen Medical Centre, 6525 GA Nijmegen, The Netherlands.
authors.forename: Cornelia M
authors.initials: CM
authors.lastname: Schaefer-Prokop
authors.affiliation: Center for Diagnostic Imaging and Physiology, Skåne University Hospital, Lund University, SE-221 85 Lund, Sweden.
authors.forename: Gracijela
authors.initials: G
authors.lastname: Bozovic
authors.affiliation: Cancer Imaging Laboratory, Center for Applied Medical Research, University of Navarra, ES-31008 Pamplona, Navarra, Spain.
authors.forename: Arrate
authors.initials: A
authors.lastname: Muñoz-Barrutia
authors.affiliation: Diagnostic Image Analysis Group, Radboud University Nijmegen Medical Centre, 6525 GA Nijmegen, The Netherlands.
authors.forename: Eva M
authors.initials: EM
authors.lastname: van Rikxoort
authors.affiliation: Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, University of California, Los Angeles, California 90024.
authors.forename: Matthew S
authors.initials: MS
authors.lastname: Brown
authors.affiliation: Department of Radiology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.
authors.forename: Pim A
authors.initials: PA
authors.lastname: de Jong
authors.affiliation: Image Sciences Institute, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.
authors.forename: Max A
authors.initials: MA
authors.lastname: Viergever
authors.affiliation: Diagnostic Image Analysis Group, Radboud University Nijmegen Medical Centre, 6525 GA Nijmegen, The Netherlands and Image Sciences Institute, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.
authors.forename: Bram
authors.initials: B
authors.lastname: van Ginneken
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D002908:Chronic Disease; D004195:Disease Models, Animal; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008168:Lung / Q000000981:diagnostic imaging*; D016040:Lung Transplantation; D051379:Mice; D010363:Pattern Recognition, Automated / Q000379:methods; D011014:Pneumonia / Q000000981:diagnostic imaging; D013902:Radiography, Thoracic / Q000379:methods*; D013552:Swine; D013995:Time; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014376:Tuberculosis / Q000000981:diagnostic imaging