title: A deep learning based framework for accurate segmentation of cervical cytoplasm and nuclei.
abstract: In this paper, a superpixel and convolution neural network (CNN) based segmentation method is proposed for cervical cancer cell segmentation. Since the background and cytoplasm contrast is not relatively obvious, cytoplasm segmentation is first performed. Deep learning based on CNN is explored for region of interest detection. A coarse-to-fine nucleus segmentation for cervical cancer cell segmentation and further refinement is also developed. Experimental results show that an accuracy of 94.50% is achieved for nucleus region detection and a precision of 0.9143±0.0202 and a recall of 0.8726±0.0008 are achieved for nucleus cell segmentation. Furthermore, our comparative analysis also shows that the proposed method outperforms the related methods.
authors.forename: Youyi
authors.initials: Y
authors.lastname: Song
authors.forename: Ling
authors.initials: L
authors.lastname: Zhang
authors.forename: Siping
authors.initials: S
authors.lastname: Chen
authors.forename: Dong
authors.initials: D
authors.lastname: Ni
authors.forename: Baopu
authors.initials: B
authors.lastname: Li
authors.forename: Yongjing
authors.initials: Y
authors.lastname: Zhou
authors.forename: Baiying
authors.initials: B
authors.lastname: Lei
authors.forename: Tianfu
authors.initials: T
authors.lastname: Wang
mesh_terms: D000328:Adult; D000465:Algorithms; D002467:Cell Nucleus / Q000473:pathology; D003593:Cytoplasm / Q000473:pathology; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008875:Middle Aged; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis*; D055815:Young Adult