In mesothelioma, response is usually assessed by computed tomography (CT). In current clinical practice the Response Evaluation Criteria in Solid Tumors (RECIST) or WHO, i.e., the uni-dimensional or the bi-dimensional measurements, is applied to the assessment of therapy response. However, the shape of the mesothelioma volume is very irregular and its longest dimension is almost never in the axial plane. Furthermore, the sections and the sites where radiologists measure the tumor are rather subjective, resulting in poor reproducibility of tumor size measurements. We are developing an objective three-dimensional (3D) computer algorithm to automatically identify and quantify tumor volumes that are associated with malignant pleural mesothelioma to assess therapy response. The algorithm first extracts the lung pleural surface from the volumetric CT images by interpolating the chest ribs over a number of adjacent slices and then forming a volume that includes the thorax. This volume allows a separation of mesothelioma from the chest wall. Subsequently, the structures inside the extracted pleural lung surface, including the mediastinal area, lung parenchyma, and pleural mesothelioma, can be identified using a multiple thresholding technique and morphological operations. Preliminary results have shown the potential of utilizing this algorithm to automatically detect and quantify tumor volumes on CT scans and thus to assess therapy response for malignant pleural mesothelioma.
Liver segmentation is critical for the development of algorithms to detect and define focal lesions. It is also helpful in presurgical planning for hepatic resection and to gauge the results of therapies. The purpose of this study was to develop a computerized method for extraction of liver contours on contrast-enhanced hepatic CT.
The method is based on a snake algorithm with Gradient Vector Flow (GVF) field as its external force, which uses an edge map and an initial contour as its starting point. A Canny edge algorithm is thus applied to obtain the initial edge map. To suppress edges inside liver parenchyma, a liver template determined by analyzing the histogram of the liver image is employed. Based on the modified edge map, the GVF field is then computed in an iterative manner. Due to the finite iteration step, an area uncovered by the GVF field in the liver can be extracted and serves as an initial contour for the snake algorithm. Preliminary results have shown the potential of separating the liver from its adjacent structures (e.g., kidney and stomach) of similar densities.