Abstract
This paper proposes an automated method for detecting mediastinal lymph nodes from contrasted 3D chest CT images to assist transbronchial needle aspiration (TBNA). In lung cancer treatment, it is vital to check whether metastasis occurs in lymph nodes. For this purpose, two-phase contrasted CT images are used for precise and accurate image diagnosis in the clinical field. In the proposed method, two input CT images are first registered and then the processing area is calculated on the basis of intensity information and an atlas for the lymph node map. Lymph node metastasis candidates are extracted roughly by using local intensity structure analysis. Lymph node metastasis regions are obtained by using a shape modification process based on using the shape features of candidates to achieve region growth and false positive (FP) reduction. To provide assistance for TBNA, detection results are visualized through the semi-translucent bronchial wall by virtual bronchoscopy. In experimentally applying the method to 42 pairs of contrasted chest CT images, the results show that the method detected 92.9% of the lymph node metastases, while a radiologist detected 57.1%. The lymph nodes were clearly visualized in the virtual bronchoscopy as the simulation of TBNA guidance.
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Kitasaka, T., Kishimoto, M., Oda, M., Iwano, S., Mori, K. (2013). Automated Detection of Mediastinal Lymph Nodes for Assistance of Transbronchial Needle Aspiration. In: Linte, C.A., Chen, E.C.S., Berger, MO., Moore, J.T., Holmes, D.R. (eds) Augmented Environments for Computer-Assisted Interventions. AE-CAI 2012. Lecture Notes in Computer Science, vol 7815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38085-3_16
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DOI: https://doi.org/10.1007/978-3-642-38085-3_16
Publisher Name: Springer, Berlin, Heidelberg
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