Automated identification of diploid reference cells in cervical smears using image analysis

Abstract

Linear and cyclic analogues of cyclolinopeptide A (CLA) with two dipeptide segments (Val5Pro6 and Pro6Pro7) replaced by their tetrazole derivatives were synthesized by the SPPS technique and cyclized using TBTU (O‐(benzotriazol‐1‐yl)‐1,1,3,3‐tetramethyluronium tetrafluoroborate) reagent. The conformational properties of the c(Leu1Ile2Ile3Leu4Val5Pro6 ψ[CN4]Ala7Phe8Phe9) were investigated by NMR and computational techniques. The overall solution structure of this cyclic peptide resembles that observed for the CLA in the solid state. These studies of cyclic tetrazole CLA analogue confirm that the 1,5‐disubstituted tetrazole ring functions as an effective, well‐tolerated cis‐amide bond mimic in solution. The peptides were examined for their immunosuppressive activity in the humoral response test. For cyclic analogues the immunosuppressive activity, at low doses, is equal in magnitude to the activity presented by cyclosporin A and native CLA. The conformational and biological data seem indicate that the ProProPhePhe moiety and the preservation of the CLA backbone conformation are important for immunosuppressive activity. © 2002 Wiley Periodicals, Inc. Biopolymers 63: 343–357, 2002

Advances in molecular labeling, high throughput imaging and machine intelligence portend powerful functional cellular biochemistry tools

“…High-level image interpretation has been used in a wide range of applications [Hudson and Cohen, 2000;Duda et al, 2001] including microscope images of: cervical smears [Kemp et al, 1997;Mackin et al, 1998;Van der Laak et al, 2002]; premalignant prostate, colon and esophageal tissue [Weyn et al, 2000]; and cultured cells [Boland et al, 1998;Boland and Murphy, 2001]. However, automated image interpretation using classical clinical stains (e.g., hematoxylin and eosin, and variations of the Pap stain) has only shown limited success [Bartels and Vooijs, 1999].…”

A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification

“…Despite not having the same final purpose of cervical cell abnormality identification, van der Laak [66] also presents pertinent work for adequacy assessment. Discriminant functions (DF) classifiers are proposed in order to recognize debris and inflammatory cells as well as distinguish nuclei from different types.…”

Automation‐assisted cervical cancer screening in manual liquid‐based cytology with hematoxylin and eosin staining

“…The first task attempts to eliminate noncellular artifacts such as debris and inflammatory cell clusters. Typically the shape, size and intensity features (16,24,33,34) are exploited to identify artifacts. For classifier training, the linear discriminant analysis (33), maximum likelihood (34), and support vector machines (SVMs; 16,24) are used.…”

“…Preprocessing. Before applying the nucleus segmentation algorithm, a practical problem should be considered: if the cytoplasm is deep stained or some inflammation cells cluster together, there might be many abnormal segments, which are often the major challenge in cervical cytology automation (33,34). To enhance the contrast between nuclei and cytoplasm, the original color image is preprocessed using the procedure designed in our previous work (49).…”