title: Semi-automatic medical image segmentation with adaptive local statistics in Conditional Random Fields framework.
abstract: Planning radiotherapy and surgical procedures usually require onerous manual segmentation of anatomical structures from medical images. In this paper we present a semi-automatic and accurate segmentation method to dramatically reduce the time and effort required of expert users. This is accomplished by giving a user an intuitive graphical interface to indicate samples of target and non-target tissue by loosely drawing a few brush strokes on the image. We use these brush strokes to provide the statistical input for a Conditional Random Field (CRF) based segmentation. Since we extract purely statistical information from the user input, we eliminate the need of assumptions on boundary contrast previously used by many other methods, A new feature of our method is that the statistics on one image can be reused on related images without registration. To demonstrate this, we show that boundary statistics provided on a few 2D slices of volumetric medical data, can be propagated through the entire 3D stack of images without using the geometric correspondence between images. In addition, the image segmentation from the CRF can be formulated as a minimum s-t graph cut problem which has a solution that is both globally optimal and fast. The combination of a fast segmentation and minimal user input that is reusable, make this a powerful technique for the segmentation of medical images.
authors.affiliation: Medical Physics Department, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA. huj@mskcc.org
authors.forename: Yu-Chi J
authors.initials: YC
authors.lastname: Hu
authors.forename: Michael D
authors.initials: MD
authors.lastname: Grossberg
authors.forename: Gikas S
authors.initials: GS
authors.lastname: Mageras
mesh_terms: D000465:Algorithms; D001331:Automation; D003196:Computer Graphics; D003952:Diagnostic Imaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000145:classification / Q000379:methods* / Q000706:statistics & numerical data; D008099:Liver / Q000473:pathology; D015233:Models, Statistical; D016011:Normal Distribution; D019047:Phantoms, Imaging; D011336:Probability; D015203:Reproducibility of Results; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods