Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images

• Published in: IEEE transactions on medical imaging 2003-10, Vol.22 (10), p.1259-1274
• Main Authors: Kostis, W.J., Reeves, A.P., Yankelevitz, D.F., Henschke, C.I.
• Format: Article
• Language: English
• Subjects: Algorithms; Anatomy, Cross-Sectional - methods; Anisotropic magnetoresistance; Biological and medical sciences; Biomedical imaging; Cancer; Cell Division; Computed tomography; Diagnostic radiography; Humans; Image analysis; Image segmentation; Imaging, Three-Dimensional - methods; Lungs; Medical diagnostic imaging; Medical imaging; Medical sciences; Models, Biological; Neoplasm Staging - methods; Radiographic Image Interpretation, Computer-Assisted - methods; Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects); Reproducibility of Results; Sensitivity and Specificity; Solitary Pulmonary Nodule - classification; Solitary Pulmonary Nodule - diagnostic imaging; Solitary Pulmonary Nodule - pathology; Technology. Biomaterials. Equipments. Material. Instrumentation; Time measurement; Tomography, Spiral Computed - methods
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2003.817785

Small pulmonary nodules are a common radiographic finding that presents an important diagnostic challenge in contemporary medicine. While pulmonary nodules are the major radiographic indicator of lung cancer, they may also be signs of a variety of benign conditions. Measurement of nodule growth rate over time has been shown to be the most promising tool in distinguishing malignant from nonmalignant pulmonary nodules. In this paper, we describe three-dimensional (3-D) methods for the segmentation, analysis, and characterization of small pulmonary nodules imaged using computed tomography (CT). Methods for the isotropic resampling of anisotropic CT data are discussed. 3-D intensity and morphology-based segmentation algorithms are discussed for several classes of nodules. New models and methods for volumetric growth characterization based on longitudinal CT studies are developed. The results of segmentation and growth characterization methods based on in vivo studies are described. The methods presented are promising in their ability to distinguish malignant from nonmalignant pulmonary nodules and represent the first such system in clinical use.