Accurate estimation of splenic volume is crucial for the determination of disease progression and response to
treatment for diseases that result in enlargement of the spleen. However, there is no consensus with respect to
the use of single or multiple one-dimensional, or volumetric measurement. Existing methods for human reviewers
focus on measurement of cross diameters on a representative axial slice and craniocaudal length of the organ. We
propose two heuristics for the selection of the optimal axial plane for splenic volume estimation: the maximal area
axial measurement heuristic and the novel conformal welding shape-based heuristic. We evaluate these heuristics
on time-variant data derived from both healthy and sick subjects and contrast them to established heuristics.
Under certain conditions our heuristics are superior to standard practice volumetric estimation methods. We
conclude by providing guidance on selecting the optimal heuristic for splenic volume estimation.
In this paper, we present an adaptive approach for fully automatic centerline extraction and small intestine removal based on partial volume (PV) image segmentation and distance field modeling. Computed tomographic colonography (CTC) volume image is first segmented for the colon wall mucosa layer, which represents the PV effect around the colon wall. Then centerline extraction is performed in the presence of colon collapse and small intestine touch by the use of distance field within the segmented PV mucosa layer, where centerline breakings due to collapse are recovered and centerline branches due to small intestine tough are removed. Experimental results from 24 patient CTC scans with small intestine touch rendered 100% removal of the touch, while only 16 out of the 24 could be done by the well-known isolated component method. Our voxel-by-voxel marking strategy in the automated procedure preserves the topology and validity of the colon structure. The marked inner and outer boundaries on cleansed colon are very close to those labeled by the experts. Experimental results demonstrated the robustness and efficiency of the presented adaptive approach for CTC utility.
Accurate assessment of colorectal polyp size is of great significance for early diagnosis and management of colorectal cancers. Due to the complexity of colon structure, polyps with diverse geometric characteristics grow from different landform surfaces. In this paper, we present a new colon decomposition approach for polyp measurement. We first apply an efficient maximum a posteriori expectation-maximization (MAP-EM) partial volume segmentation algorithm to achieve an effective electronic cleansing on colon. The global colon structure is then decomposed into different kinds of morphological shapes, e.g. haustral folds or haustral wall. Meanwhile, the polyp location is identified by an automatic computer aided detection algorithm. By integrating the colon structure decomposition with the computer aided detection system, a patch volume of colon polyps is extracted. Thus, polyp size assessment can be achieved by finding abnormal protrusion on a relative uniform morphological surface from the decomposed colon landform. We evaluated our method via physical phantom and clinical datasets. Experiment results demonstrate the feasibility of our method in consistently quantifying the size of polyp volume and, therefore, facilitating characterizing for clinical management.
Colorectal cancer is the third most common type of cancer. However, this disease can be prevented by detection and removal of precursor adenomatous polyps after the diagnosis given by experts on computer tomographic colonography (CTC). During CTC diagnosis, the radiologist looks for colon polyps and measures not only the size but also the malignancy. It is a common sense that to segment polyp volumes from their complicated growing environment is of much significance for accomplishing the CTC based early diagnosis task. Previously, the polyp volumes are mainly given from the manually or semi-automatically drawing by the radiologists. As a result, some deviations cannot be avoided since the polyps are usually small (6~9mm) and the radiologists’ experience and knowledge are varying from one to another. In order to achieve automatic polyp segmentation carried out by the machine, we proposed a new method based on the colon decomposition strategy. We evaluated our algorithm on both phantom and patient data. Experimental results demonstrate our approach is capable of segment the small polyps from their complicated growing background.