Segmentation of Pulmonary Nodules in Thoracic CT Scans: A Region Growing Approach

By Jamshid Dehmeshki, H. Amin, M. V. Casique, Xujiong Ye. IEEE Transactions on Medical Imaging, 2008

297 citations recorded by the source corpus.

This paper presents an efficient algorithm for segmenting different types of pulmonary nodules including high and low contrast nodules, nodules with vasculature attachment, and nodules in the close vicinity of the lung wall or diaphragm. The algorithm performs an adaptive sphericity oriented contrast region growing on the fuzzy connectivity map of the object of interest. This region growing is operated within a volumetric mask which is created by first applying a local adaptive segmentation algorithm that identifies foreground and background regions within a certain window size. The foreground objects are then filled to remove any holes, and a spatial connectivity map is generated to create a 3-D mask. The mask is then enlarged to contain the background while excluding unwanted foreground regions. Apart from generating a confined search volume, the mask is also used to estimate the parameters for the subsequent region growing, as well as for repositioning the seed point in order to ensure reproducibility. The method was run on 815 pulmonary nodules. By using randomly placed seed points, the approach was shown to be fully reproducible. As for acceptability, the segmentation results were visually inspected by a qualified radiologist to search for any gross misssegmentation. 84% of the first results of the segmentation were accepted by the radiologist while for the remaining 16% nodules, alternative segmentation solutions that were provided by the method were selected.

Subfields: Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/81020ce3e7f24f03888302a05276858f3147a1f0

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In Computer Vision and Pattern Recognition

In Pulmonary and Respiratory Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation (2017, methodology): “Therefore, Dehmeshki et al. (2008) introduced a shape hypothesis and proposed sphericity contrast based region growing method to detach nodule from lung wall.”
QIN “Radiomics: The Process and the Challenges” (2012, methodology): “Many popular segmentation algorithms have been applied in medical imaging studies within the last 20 years; the most popular ones include region-growing methods [31,32], level set methods [33–38], graph cut methods [39–44], active contours (snake) algorithms [45–49] and semiautomatic segmentations s”