Interactive lung segmentation in abnormal human and animal chest CT scans
- Image Sciences Institute, University Medical Center Utrecht, 3584 CX Utrecht (Netherlands)
- Department of Radiology, Meander Medical Centre, 3813 TZ Amersfoort, The Netherlands and Diagnostic Image Analysis Group, Radboud University Nijmegen Medical Centre, 6525 GA Nijmegen (Netherlands)
- Center for Diagnostic Imaging and Physiology, Skåne University Hospital, Lund University, SE-221 85 Lund (Sweden)
- Cancer Imaging Laboratory, Center for Applied Medical Research, University of Navarra, ES-31008 Pamplona, Navarra (Spain)
- Diagnostic Image Analysis Group, Radboud University Nijmegen Medical Centre, 6525 GA Nijmegen (Netherlands)
- 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 (United States)
- Department of Radiology, University Medical Center Utrecht, 3584 CX Utrecht (Netherlands)
Purpose: 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.
- OSTI ID:
- 22409582
- Journal Information:
- Medical Physics, Vol. 41, Issue 8; Other Information: (c) 2014 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); ISSN 0094-2405
- Country of Publication:
- United States
- Language:
- English
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