Anatomy packing with hierarchical segments: an algorithm for segmentation of pulmonary nodules in CT images

Abstract

BackgroundThis paper proposes a semantic segmentation algorithm that provides the spatial distribution patterns of pulmonary ground-glass nodules with solid portions in computed tomography (CT) images.MethodsThe proposed segmentation algorithm, anatomy packing with hierarchical segments (APHS), performs pulmonary nodule segmentation and quantification in CT images. In particular, the APHS algorithm consists of two essential processes: hierarchical segmentation tree construction and anatomy packing. It constructs the hierarchical segmentation tree based on region attributes and local contour cues along the region boundaries. Each node of the tree corresponds to the soft boundary associated with a family of nested segmentations through different scales applied by a hierarchical segmentation operator that is used to decompose the image in a structurally coherent manner. The anatomy packing process detects and localizes individual object instances by optimizing a hierarchical conditional random field model. Ninety-two histopathologically confirmed pulmonary nodules were used to evaluate the performance of the proposed APHS algorithm. Further, a comparative study was conducted with two conventional multi-label image segmentation algorithms based on four assessment metrics: the modified Williams index, percentage statistic, overlapping ratio, and difference ratio.ResultsUnder the same framework, the proposed APHS algorithm was applied to two clinical applications: multi-label segmentation of nodules with a solid portion and surrounding tissues and pulmonary nodule segmentation. The results obtained indicate that the APHS-generated boundaries are comparable to manual delineations with a modified Williams index of 1.013. Further, the resulting segmentation of the APHS algorithm is also better than that achieved by two conventional multi-label image segmentation algorithms.ConclusionsThe proposed two-level hierarchical segmentation algorithm effectively labelled the pulmonary nodule and its surrounding anatomic structures in lung CT images. This suggests that the generated multi-label structures can potentially serve as the basis for developing related clinical applications.

Segmentation of the pulmonary nodule and the attached vessels in the CT scan of the chest using morphological features and topological skeleton of the nodule

“…Scholars segment the nodule in the CT images based on the distribution of the intensity [23]. Examples of the segmentation approaches in the literature are region growing [24], thresholding [25–27], a user‐interactive framework using 3D region growing on the fuzzy connectivity map [28] and based on grey‐level similarity and shape [29], voxel‐level and object‐level classification [30], region growing on the Euclidean distance map [31], active contour [15], anatomy packing with hierarchical segmentation [32], multi‐features clustering with adaptive local region energy [33], a modified deconvolutional neural network [34], deep learning methods [35–44], and graph cut with a deep learned prior [45].…”