Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest. Issue 7 (21st June 2016)

• Record Type: Journal Article
• Authors: Liu, Jiamin; Hoffman, Joanne; Zhao, Jocelyn; Yao, Jianhua; Lu, Le; Kim, Lauren; Turkbey, Evrim B.; Summers, Ronald M.
• Is Part Of: Medical physics. Volume 43:Issue 7(2016)
• Page Start: 4362
• Page End: 4374
• Publication Date: 2016-06-21
• Subjects: bone -- cancer -- computerised tomography -- feature extraction -- Hessian matrices -- image classification -- image colour analysis -- image segmentation -- image texture -- lung -- medical image processing -- orthopaedics -- support vector machines -- tumours; Computed tomography -- Matrix theory -- Cancer -- Segmentation; Computerised tomographs -- Orthopaedic methods or devices for non‐surgical treatment of bones or joints; Nursing devices -- 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 -- Analysis of texture -- Inference methods or devices; lymph node detection -- lymph node station labeling -- spatial prior -- random forest classifier -- multiatlas label fusion; Computed tomography -- Lungs -- Medical image segmentation -- Heart -- Cancer -- Medical image spatial resolution -- Muscles -- Calibration; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4954009
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: To develop an automated system for mediastinal lymph node detection and station mapping for chest CT. Methods: The contextual organs, trachea, lungs, and spine are first automatically identified to locate the region of interest (ROI) (mediastinum). The authors employ shape features derived from Hessian analysis, local object scale, and circular transformation that are computed per voxel in the ROI. Eight more anatomical structures are simultaneously segmented by multiatlas label fusion. Spatial priors are defined as the relative multidimensional distance vectors corresponding to each structure. Intensity, shape, and spatial prior features are integrated and parsed by a random forest classifier for lymph node detection. The detected candidates are then segmented by the following curve evolution process. Texture features are computed on the segmented lymph nodes and a support vector machine committee is used for final classification. For lymph node station labeling, based on the segmentation results of the above anatomical structures, the textual definitions of mediastinal lymph node map according to the International Association for the Study of Lung Cancer are converted into patient‐specific color‐coded CT image, where the lymph node station can be automatically assigned for each detected node. Results: The chest CT volumes from 70 patients with 316 enlarged mediastinal lymph nodes are used for validation. For lymph node detection, their system achieves 88% sensitivity at eight false positives per patient. For lymph node station labeling, 84.5% of lymph nodes are correctly assigned to their stations. Conclusions: Multiple‐channel shape, intensity, and spatial prior features aggregated by a random forest classifier improve mediastinal lymph node detection on chest CT. Using the location information of segmented anatomic structures from the multiatlas formulation enables accurate identification of lymph node stations.