Automated Segmentation Refinement of Small Lung Nodules in CT Scans by Local Shape Analysis

• Published in: IEEE transactions on biomedical engineering Vol. 58; no. 12; pp. 3418 - 3428
• Main Authors: Diciotti, Stefano, Lombardo, Simone, Falchini, Massimo, Picozzi, Giulia, Mascalchi, Mario
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.12.2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Applied sciences; Automation; Biological and medical sciences; Computed tomography; Computer-aided diagnosis; Databases, Factual; Educational institutions; Exact sciences and technology; Humans; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Information, signal and communications theory; Lung Neoplasms - diagnostic imaging; lung nodules; Lungs; Materials; Medical sciences; Pattern recognition; Pneumology; Radiographic Image Interpretation, Computer-Assisted - methods; Reproducibility of Results; segmentation; Shape; shape analysis; Signal processing; Studies; Telecommunications and information theory; Tomography, X-Ray Computed - methods; Tumors of the respiratory system and mediastinum; Lung disease; Radiodiagnosis; Segmentation; Image processing; Respiratory disease; Refinement method; lung nodules; shape analysis; CT; Morphological analysis; Lung nodule; Medical imagery; Computerized axial tomography; Diagnostic aid; Pattern analysis; Computer-aided diagnosis; Biomedical engineering
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2011.2167621

One of the most important problems in the segmentation of lung nodules in CT imaging arises from possible attachments occurring between nodules and other lung structures, such as vessels or pleura. In this report, we address the problem of vessels attachments by proposing an automated correction method applied to an initial rough segmentation of the lung nodule. The method is based on a local shape analysis of the initial segmentation making use of 3-D geodesic distance map representations. The correction method has the advantage that it locally refines the nodule segmentation along recognized vessel attachments only, without modifying the nodule boundary elsewhere. The method was tested using a simple initial rough segmentation, obtained by a fixed image thresholding. The validation of the complete segmentation algorithm was carried out on small lung nodules, identified in the ITALUNG screening trial and on small nodules of the lung image database consortium (LIDC) dataset. In fully automated mode, 217/256 (84.8%) lung nodules of ITALUNG and 139/157 (88.5%) individual marks of lung nodules of LIDC were correctly outlined and an excellent reproducibility was also observed. By using an additional interactive mode, based on a controlled manual interaction, 233/256 (91.0%) lung nodules of ITALUNG and 144/157 (91.7%) individual marks of lung nodules of LIDC were overall correctly segmented. The proposed correction method could also be usefully applied to any existent nodule segmentation algorithm for improving the segmentation quality of juxta-vascular nodules.