Automated lung segmentation of low resolution CT scans of rats

Benjamin M. Rizzo; Steven T. Haworth; Anne V. Clough

doi:10.1117/12.2043724

13 March 2014 Automated lung segmentation of low resolution CT scans of rats

Benjamin M. Rizzo, Steven T. Haworth, Anne V. Clough

Proceedings Volume 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging; 90380X (2014) https://doi.org/10.1117/12.2043724
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Dual modality micro-CT and SPECT imaging can play an important role in preclinical studies designed to investigate mechanisms, progression, and therapies for acute lung injury in rats. SPECT imaging involves examining the uptake of radiopharmaceuticals within the lung, with the hypothesis that uptake is sensitive to the health or disease status of the lung tissue. Methods of quantifying lung uptake and comparison of right and left lung uptake generally begin with identifying and segmenting the lung region within the 3D reconstructed SPECT volume. However, identification of the lung boundaries and the fissure between the left and right lung is not always possible from the SPECT images directly since the radiopharmaceutical may be taken up by other surrounding tissues. Thus, our SPECT protocol begins with a fast CT scan, the lung boundaries are identified from the CT volume, and the CT region is coregistered with the SPECT volume to obtain the SPECT lung region. Segmenting rat lungs within the CT volume is particularly challenging due to the relatively low resolution of the images and the rat’s unique anatomy. Thus, we have developed an automated segmentation algorithm for low resolution micro-CT scans that utilizes depth maps to detect fissures on the surface of the lung volume. The fissure’s surface location is in turn used to interpolate the fissure throughout the lung volume. Results indicate that the segmentation method results in left and right lung regions consistent with rat lung anatomy.

Citation Download Citation

Benjamin M. Rizzo, Steven T. Haworth, and Anne V. Clough "Automated lung segmentation of low resolution CT scans of rats", Proc. SPIE 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging, 90380X (13 March 2014); https://doi.org/10.1117/12.2043724

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KEYWORDS

Lung

Image segmentation

Single photon emission computed tomography

Computed tomography

Binary data

Algorithm development

Tissues

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