title: Dual-Channel Active Contour Model for Megakaryocytic Cell Segmentation in Bone Marrow Trephine Histology Images.
abstract: Assessment of morphological features of megakaryocytes (MKs) (special kind of cells) in bone marrow trephine biopsies play an important role in the classification of different subtypes of Philadelphia-chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs). In order to aid hematopathologists in the study of MKs, we propose a novel framework that can efficiently delineate the nuclei and cytoplasm of these cells in digitized images of bone marrow trephine biopsies. The framework first employs a supervised machine learning approach that utilizes color and texture features to delineate megakaryocytic nuclei. It then employs a novel dual-channel active contour model to delineate the boundary of megakaryocytic cytoplasm by using different deconvolved stain channels. Compared to other recent models, the proposed framework achieves accurate results for both megakaryocytic nuclear and cytoplasmic delineation.
authors.forename: Tzu-Hsi
authors.initials: TH
authors.lastname: Song
authors.forename: Victor
authors.initials: V
authors.lastname: Sanchez
authors.forename: Hesham
authors.initials: H
authors.lastname: EIDaly
authors.forename: Nasir M
authors.initials: NM
authors.lastname: Rajpoot
mesh_terms: D001706:Biopsy / Q000379:methods*; D001853:Bone Marrow / Q000000981:diagnostic imaging*; D001854:Bone Marrow Cells / Q000166:cytology*; D002467:Cell Nucleus; D006652:Histological Techniques / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008533:Megakaryocytes / Q000166:cytology*; D009196:Myeloproliferative Disorders; D000069553:Supervised Machine Learning