title: Principles and methods for automatic and semi-automatic tissue segmentation in MRI data.
abstract: The development of magnetic resonance imaging (MRI) revolutionized both the medical and scientific worlds. A large variety of MRI options have generated a huge amount of image data to interpret. The investigation of a specific tissue in 3D or 4D MR images can be facilitated by image processing techniques, such as segmentation and registration. In this work, we provide a brief review of the principles and methods that are commonly applied to achieve superior tissue segmentation results in MRI. The impacts of MR image acquisition on segmentation outcome and the principles of selecting and exploiting segmentation techniques tailored for specific tissue identification tasks are discussed. In the end, two exemplary applications, breast and fibroglandular tissue segmentation in MRI and myocardium segmentation in short-axis cine and real-time MRI, are discussed to explain the typical challenges that can be posed in practical segmentation tasks in MRI data. The corresponding solutions that are adopted to deal with these challenges of the two practical segmentation tasks are thoroughly reviewed.
authors.affiliation: Institute for Medical Image Computing, Fraunhofer MEVIS Universitaetsallee 29, 28359, Bremen, Germany. lei.wang@mevis.fraunhofer.de.
authors.forename: Lei
authors.identifier: http://orcid.org/0000-0001-6612-918X
authors.initials: L
authors.lastname: Wang
authors.affiliation: Institute for Medical Image Computing, Fraunhofer MEVIS Universitaetsallee 29, 28359, Bremen, Germany.
authors.forename: Teodora
authors.initials: T
authors.lastname: Chitiboi
authors.affiliation: Institute for Medical Image Computing, Fraunhofer MEVIS Universitaetsallee 29, 28359, Bremen, Germany.
authors.forename: Hans
authors.initials: H
authors.lastname: Meine
authors.affiliation: Institute for Medical Image Computing, Fraunhofer MEVIS Universitaetsallee 29, 28359, Bremen, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Günther
authors.affiliation: Institute for Medical Image Computing, Fraunhofer MEVIS Universitaetsallee 29, 28359, Bremen, Germany.
authors.forename: Horst K
authors.initials: HK
authors.lastname: Hahn
mesh_terms: D000465:Algorithms; D001940:Breast / Q000000981:diagnostic imaging*; D006321:Heart / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: MRI; Non-uniformity correction; Segmentation