title: Simplified labeling process for medical image segmentation.
abstract: Image segmentation plays a crucial role in many medical imaging applications by automatically locating the regions of interest. Typically supervised learning based segmentation methods require a large set of accurately labeled training data. However, thel labeling process is tedious, time consuming and sometimes not necessary. We propose a robust logistic regression algorithm to handle label outliers such that doctors do not need to waste time on precisely labeling images for training set. To validate its effectiveness and efficiency, we conduct carefully designed experiments on cervigram image segmentation while there exist label outliers. Experimental results show that the proposed robust logistic regression algorithms achieve superior performance compared to previous methods, which validates the benefits of the proposed algorithms.
authors.affiliation: CBIM Center, Rutgers University, Piscataway, NJ 08554, USA.
authors.forename: Mingchen
authors.initials: M
authors.lastname: Gao
authors.forename: Junzhou
authors.initials: J
authors.lastname: Huang
authors.forename: Xiaolei
authors.initials: X
authors.lastname: Huang
authors.forename: Shaoting
authors.initials: S
authors.lastname: Zhang
authors.forename: Dimitris N
authors.initials: DN
authors.lastname: Metaxas
mesh_terms: D000465:Algorithms; D002584:Cervix Uteri / Q000473:pathology*; D004282:Documentation / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity