Automatic Organ Localization on X-Ray CT Images by Using Ensemble-Learning Techniques

Background

3D images of anatomical structures of a patient are critical factors that support diagnosis, surgery, and therapy in clinical medicine. A modern radiographic CT scan can generate a 3D volumetric image that provides detailed anatomical information of a human torso within 10 s. However, a long time and experience are required to interpret such volumetric CT images by the traditional method to identify suspicious regions. Therefore, development of computer-based image analysis algorithms and visualization tools is expected to enable doctors increase efficiency and accuracy of interpretation, and reduce tedium and oversights during CT image interpretation.

A fundamental component of such a computer system is accurate and efficient organ segmentation. Separating a target organ region from the background by identifying the contour (surface in 3D) of the organ region is the major goal of organ segmentation. However, it may not be feasible to search for all voxels in a CT scan to determine the correct contour of a target organ. Organ localization by detecting the center position and deciding the range (a 3D bounding box) of the target organ in a CT scan is a practical pre-processing step that can be very useful for facilitating accurate organ segmentation. Such a 3D rectangle not only increases the accuracy and reduces the difficulty for further image segmentation, but also describes local geometry and density properties that can be used for diagnosis. In practice, a torso CT scan always shows the complex anatomical structures of all inner organs. Thus, automatic localization of different organ regions in torso CT scans is a very difficult problem that remains to be addressed.