title: Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.
abstract: Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature.
authors.affiliation: Department of Computer Science, Technical University of Cluj-Napoca, Cluj-Napoca, Romania; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
authors.forename: Anca
authors.initials: A
authors.lastname: Ciurte
authors.affiliation: Department of Radiology, University Hospital Center and University of Lausanne, Lausanne, Switzerland; Center for Biomedical Imaging, Signal Processing Core, Lausanne, Switzerland.
authors.forename: Xavier
authors.initials: X
authors.lastname: Bresson
authors.affiliation: Department of Radiology, University Hospital Center and University of Lausanne, Lausanne, Switzerland; Center for Biomedical Imaging, Signal Processing Core, Lausanne, Switzerland.
authors.forename: Olivier
authors.initials: O
authors.lastname: Cuisenaire
authors.affiliation: Swiss Institute of Bioinformatics (SIB), University Hospital Center and University of Lausanne, Lausanne, Switzerland.
authors.forename: Nawal
authors.initials: N
authors.lastname: Houhou
authors.affiliation: Department of Computer Science, Technical University of Cluj-Napoca, Cluj-Napoca, Romania.
authors.forename: Sergiu
authors.initials: S
authors.lastname: Nedevschi
authors.affiliation: Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology, University Hospital Center and University of Lausanne, Lausanne, Switzerland.
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Thiran
authors.affiliation: Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology, University Hospital Center and University of Lausanne, Lausanne, Switzerland; Center for Biomedical Imaging, Signal Processing Core, Lausanne, Switzerland.
authors.forename: Meritxell Bach
authors.initials: MB
authors.lastname: Cuadra
mesh_terms: D000465:Algorithms; D005134:Eye Neoplasms / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging; D008297:Male; D010363:Pattern Recognition, Automated; D011467:Prostate / Q000000981:diagnostic imaging; D014463:Ultrasonography; D016216:Ultrasonography, Prenatal