title: Transfer Shape Modeling Towards High-throughput Microscopy Image Segmentation.
abstract: In order to deal with ambiguous image appearances in cell segmentation, high-level shape modeling has been introduced to delineate cell boundaries. However, shape modeling usually requires sufficient annotated training shapes, which are often labor intensive or unavailable. Meanwhile, when applying the model to different datasets, it is necessary to repeat the tedious annotation process to generate enough training data, and this will significantly limit the applicability of the model. In this paper, we propose to transfer shape modeling learned from an existing but different dataset (e.g. lung cancer) to assist cell segmentation in a new target dataset (e.g. skeletal muscle) without expensive manual annotations. Considering the intrinsic geometry structure of cell shapes, we incorporate the shape transfer model into a sparse representation framework with a manifold embedding constraint, and provide an efficient algorithm to solve the optimization problem. The proposed algorithm is tested on multiple microscopy image datasets with different tissue and staining preparations, and the experiments demonstrate its effectiveness.
authors.affiliation: Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA; J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA.
authors.forename: Fuyong
authors.initials: F
authors.lastname: Xing
authors.affiliation: J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA.
authors.forename: Xiaoshuang
authors.initials: X
authors.lastname: Shi
authors.affiliation: Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA.
authors.forename: Zizhao
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA.
authors.forename: JinZheng
authors.initials: J
authors.lastname: Cai
authors.affiliation: J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA.
authors.forename: Yuanpu
authors.initials: Y
authors.lastname: Xie
authors.affiliation: Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA; J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA; Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA.
authors.forename: Lin
authors.initials: L
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D048430:Cell Shape; D066264:Datasets as Topic; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D000069550:Machine Learning; D008853:Microscopy / Q000379:methods*; D018482:Muscle, Skeletal / Q000166:cytology* / Q000000981:diagnostic imaging; D018358:Neuroendocrine Tumors / Q000000981:diagnostic imaging / Q000473:pathology; D010190:Pancreatic Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity