We describe a system for the automatic detection of rib metastasis in thoracic CT volume. Rib metastasis manifest itself as alterations of bone intensities or shapes, and the detection of these alterations is the goal of the algorithm. According to the tubular shape of the rib structures, the detection is based on the construction of 2D cross-sections planes along the full lengths of each of the individual ribs. The set of planes is orthogonal to the rib centerline, with is extracted by a previously developed segmentation algorithm based on recursive tracing. On each of these planes, a 2D image is constructed by interpolation in the region of interest around the centerline intersection and the plane. From this image the cortical and trabecular bones are segmented separately. The appearance and geometric properties of the bone structures are analyzed and categorized according to a set of rules that summarize the possible variation types due to metastasis. The features extracted from the cross-sections along a short length of the centerline are jointly evaluated. A positive detection is accepted only if the alteration of shape and appearance is consistent with a number of consecutive cross-sections along the rib centerline.
We describe a system that automatically extracts body sections of interest from volume data sets obtained from major medical modalities. This is critical as an effort to save storage and transmission bandwidth and improve data sharing efficiency. The data to be split is stored in a series of files, and each of the files contains one axial slice image. This is how the DICOM data is stored. The splitting of volume data will therefore be applied in the axial direction. The core of the system is an algorithm module that automatically detects lines of separation in the axial direction of the data. Afterwards, the system will copy the files that contain the desired section of slice images to the destination, according to the detected separation lines. To obtain the split lines, features are extracted from human anatomies that are specific to each body section. The method and principle can be applied to major modalities where the extraction of various data sections is needed.
We describe a visualization tool for the reporting of organ tumors such as lung nodules. It provides a 3D visual summary of all the detected and segmented tumors and allows the user to navigate through the display. The detected and segmented nodules are displayed, using surface rendering to show their shapes and relative sizes. Anatomic features are used as references. In this implementation, the two lung surfaces are rendered semi-transparent as the visual reference. However, other references could be used, such as the thoracic cage, airways, or vessel trees. The display is of 3D nature, meaning that user can rotate the objects as a whole, view the display at different angles. The user can also zoom the display at will to see an enlarged view of a nodule. The 3D display is spatially synchronized with the main window that displays the volume data. A click on a nodule in the 3D display will update the main display to the corresponding slice where the nodule is located, and the nodule location will be outlined in the slice that is shown in the main widow. This is a general reporting tool that can be applied to all oncology applications using all modalities, whenever the segmentation and detection of tumors are essential. It can also be extended as a visualization tool for combinatorial reporting of all relevant pathologies.
The ribs within computed tomography (CT) images form curved structures intersecting the axial plane at oblique angles. Rib metastases and other pathologies of the rib are apparent in CT images. Analysis of the ribs using conventional 2D axial slice viewing involves manually tracking them through multiple slices. 3D visualization of the ribs also has drawbacks due to occlusion. Examination of a single rib may require repositioning the viewpoint several times in order to avoid other ribs. We propose a novel visualization method that eliminates rib curvatures by straightening each rib along its centerline. This reduces both 2D and 3D viewing complexities. Our method is based upon first segmenting and extracting the centerlines of each rib. These steps are done through a tracing based segmentation. Next, the centerlines are refined to a smoother contour. Each centerline is then used to resample and digitally straighten each rib. The result is a simplified volume containing only the straightened ribs, which can be quickly examined both in 3D and by scrolling through a series of about 40 slices. Additionally, a projection of the image can yield a single 2D image for examination. The method was tested on chest CT images obtained from patients both positive and negative for rib metastases. Running time was less than 15 seconds per dataset. Preliminary results demonstrate the effectiveness of the visualization in detecting and delineating these metastases.
Lung nodules that exhibit growth over time are considered highly suspicious for malignancy. We present a completely automated system for detection of growing lung nodules, using initial and follow-up multi-slice CT studies. The system begins with automatic detection of lung nodules in the later CT study, generating a preliminary list of candidate nodules. Next an automatic system for registering locations in two studies matches each candidate in the later study to its corresponding position in the earlier study. Then a method for automatic segmentation of lung nodules is applied to each candidate and its matching location, and the computed volumes are compared. The output of the system is a list of nodule candidates that are new or have exhibited volumetric growth since the previous scan. In a preliminary test of 10 patients examined by two radiologists, the automatic system identified 18 candidates as growing nodules. 7 (39%) of these corresponded to validated nodules or other focal abnormalities that exhibited growth. 4 of the 7 true detections had not been identified by either of the radiologists during their initial examinations of the studies. This technique represents a powerful method of surveillance that may reduce the probability of missing subtle or early malignant disease.
We present an algorithm for local surface smoothing in a defined Volume of Interest (VOI) cropped from 3D volume data, such as lung CT data. There is generally a smooth and piecewise linear surface in the VOI, with one or more bumps on the surface. In lung CT data, such bumps can be nodules that are grown from the chest wall, which represent a possibility of lung cancer. Through surface smoothing, the nodules are segmented from the chest wall and its size can be measured as diagnostic evidence. The algorithm has the advantage of high consistency and robustness, and is useful in a segmentation module of a Compute Aided Diagnosis (CAD) system.
We propose in this paper a novel approach to the automatic segmentation of lung nodules in a given volume of interest (VOI) from high resolution multi-slice CT images by dynamically initializing and adjusting a 3D template and analyzing its cross correlation with the structure of interest. First, thresholding techniques are used to separate the background voxels. The structure of interest, comprising of a nodule candidate and possible attached vessels, is then extracted by excluding any part of the chest wall inside the VOI. Afterwards, the proposed segmentation method finds the core of the structure of interest, which corresponds to the nodule, analyzes its orientation and size, and initializes a 3D template accordingly. Next, The template gradually expands, with its cross correlation to the original structure of interest being computed at each step. The template is then optimized based on the analysis of the cross correlation curve. A segmentation of the nodule is first roughly obtained by doing an 'AND' operation between the optimal template and the extracted structure and then refined by a spatial reasoning method. Template parameters can be recorded and recalled in later diagnosis so that reproducibility and consistency can be achieved. Preliminary results show that segmentation results are consistent, with a mean intra-scan volume measurement deviation of 2.8% for phantom data and 8.1% for real patient data.
Multi-slice computed tomography (CT) provides a promising technology for lung cancer detection and treatment. To optimize automatic detections of a more complete spectrum of lung nodules on CT requires multiple specialized algorithms in a coherently integrated detection system. We have developed a knowledge-based system for automatic lung nodule detection and analysis, which coherently integrates several robust novel detection algorithms to detect different types of nodules, including those attached to the chest wall, nodules adjacent to or fed by vessels, and solitary nodules, simultaneously. The system architecture can be easily extended in the future to include a still greater range of nodule types, most importantly so-called ground-glass opacities (GGOs). In addition, automatic local adaptive histogram analysis, dynamic cross-correlation analysis, and the automatic volume projection analysis by using by data dimension reduction method, are used in nodule detection. The proposed system has been applied to 10 patients screened with low-dose multi-slice CT. Preliminary clinical tests show that (1) the false positive rate averages about 3.2 per study; and (2) by using the system radiologists are able to detect nearly twice the number of nodules as compared with working alone.