Interactive lung segmentation in abnormal human and animal chest CT scans. Issue 8 (30th November 2016)

• Record Type: Journal Article
• Authors: Kockelkorn, Thessa T. J. P.; Schaefer‐Prokop, Cornelia M.; Bozovic, Gracijela; Muñoz‐Barrutia, Arrate; van Rikxoort, Eva M.; Brown, Matthew S.; de Jong, Pim A.; Viergever, Max A.; van Ginneken, Bram
• Is Part Of: Medical physics. Volume 41:Issue 8(2014)Part 1
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-11-30
• Subjects: Multislice -- Segmentation -- Diseases; computerised tomography -- diseases -- image classification -- image segmentation -- learning (artificial intelligence) -- lung -- medical disorders -- medical image processing -- statistical analysis -- user interfaces; lung segmentation -- interactive -- thoracic CT -- computer‐aided diagnosis -- CAD; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Inference methods or devices; Lungs -- Tissues -- Computed tomography -- Medical image segmentation -- Image scanners -- Radiologists -- Computer software -- Medical image reconstruction -- Comparative animal models; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4890597
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : 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.