MASK: combining 2D and 3D segmentation methods to enhance functionality

Abstract

Image segmentation, in particular, defining normal anatomic structures and diseased or malformed tissue from tomographic images, is common in medical applications. Defining tumors or arterio-venous malformations from computed tomography (CT) or magnetic resonance (MRI) images are typical examples. This paper describes a program, Medical Anatomy Segmentation Kit (MASK), whose design acknowledges that no single segmentation technique has proven to be successful or optimal for all object definition tasks associated with medical images. A practical solution is offered through a suite of complementary user-guided segmentation techniques and extensive manual editing functions to reach the final object definition goal. Manual editing can also be used to define objects which are abstract or otherwise not well represented in the image data and so require direct human definition - e.g., a radiotherapy target volume which requires human knowledge and judgement regarding image interpretation and t..