Non-invasive imaging technologies allow images of the internal structures of a patient or object to be obtained without performing an invasive procedure on the patient or object. In particular, technologies such as computed tomography (CT) use various physical principals, such as the differential transmission of x-rays through the target volume, to acquire image data and to construct tomographic images (e.g., three-dimensional representations of the interior of the human body or of other imaged structures).
One of the key tasks in oncology is to automatically perform segmentation of reconstructed images to identify organs and other anatomical structures. Unfortunately, many segmentation algorithms are often distracted by the presence of noise and fail to identify correct organ boundaries. For example, since the invention of CT, filtered backprojection (FBP) has been used almost exclusively for image reconstruction because of its computational efficiency. However, one disadvantage of FBP is its suboptimal noise and image quality performance resulting from simplifications made in order to derive the closed form analytic solution. In contrast, iterative reconstruction (IR) algorithms feature substantially reduced noise levels in reconstructed images compared to FBP reconstructed images of the same scanning data. However, IR is still too noisy for automatic segmentation of images due to variations in intensity within anatomical structures in the image. The difficulty is further enhanced by the organ variations from patient to patient. A more reliable and automated organ segmentation is highly desirable for workflow and productivity.