Automated Papanicolaou smear analysis as a screening tool for female lower genital tract malignancies

Enhancing Image Characteristics of Retinal Images of Aggressive Posterior Retinopathy of Prematurity Using a Novel Software, (RetiView)

Purpose. To report pilot data from a novel image analysis software "RetiView," to highlight clinically relevant information in RetCam images of infants with aggressive posterior retinopathy of prematurity (APROP). Methods. Twenty-three imaging sessions of consecutive infants of Asian Indian origin with clinically diagnosed APROP underwent three protocols (Grey Enhanced (GE), Color Enhanced (CE), and "Vesselness Measure" (VNM)) of the software. The postprocessed images were compared to baseline data from the archived unprocessed images and clinical exam by the retinopathy of prematurity (ROP) specialist for anterior extent of the vessels, capillary nonperfusion zones (CNP), loops, hemorrhages, and flat neovascularization. Results. There was better visualization of tortuous loops in the GE protocol (56.5%); "bald" zones within the CNP zones (26.1%), hemorrhages (13%), and edge of the disease (34.8%) in the CE images; neovascularization on both GE and CE protocols (13% each); clinically relevant information in cases with poor pupillary dilatation (8.7%); anterior extent of vessels on the VNM protocol (13%) effecting a "reclassification" from zone 1 to zone 2 posterior. Conclusions. RetiView is a noninvasive and inexpensive method of customized image enhancement to detect clinically difficult characteristics in a subset of APROP images with a potential to influence treatment planning.

Integrated cervical smear screening using liquid based cytology and bioimpedance analysis

OBJECTIVE

To minimize the false negativity in cervical cancer screening with Papanicolaou (Pap) test, there is a need to explore novel cytological technique and identification of unique and important cellular features from the perspectives of morphological as well as biophysical properties.

MATERIALS AND METHODS

The present study explores the feasibility of low-cost cervical monolayer techniques in extracting cyto-pathological features to classify normal and abnormal conditions. The cervical cells were also analyzed in respect to their electrical bioimpedance.

RESULT

The results show that newly developed monolayer technique for cervical smears is cost effective, capable of cyto-pathological evaluation. Electrical bioimpedance study evidenced distinction between abnormal and normal cell population at more than two order of magnitude difference.

CONCLUSION

The integration of bioimpedance observation along with the proposed low-cost monolayer technology could increase the efficiency of the cervical screening to a greater extent thereby reducing the rates of faulty diagnosis.

Semi-automatic segmentation and classification of Pap smear cells

Cytologic screening has been widely used for detecting the cervical cancers. In this study, a semiautomatic PC-based cellular image analysis system was developed for segmenting nuclear and cytoplasmic contours and for computing morphometric and textual features to train support vector machine (SVM) classifiers to classify four different types of cells and to discriminate dysplastic from normal cells. A software program incorporating function, including image reviewing and standardized denomination of file names, was also designed to facilitate and standardize the workflow of cell analyses. Two experiments were conducted to verify the classification performance. The cross-validation results of the first experiment showed that average accuracies of 97.16% and 98.83%, respectively, for differentiating four different types of cells and in discriminating dysplastic from normal cells have been achieved using salient features (8 for four-cluster and 7 for two-cluster classifiers) selected with SVM recursive feature addition. In the second experiment, 70% (837) of the cell images were used for training and 30% (361) for testing, achieving an accuracy of 96.12% and 98.61% for four-cluster and two-cluster classifiers, respectively. The proposed system provides a feasible and effective tool in evaluating cytologic specimens.

Evaluation of microbial enzymes in normal and abnormal cervicovaginal fluids of cervical dysplasia: a case control study

The aim of the present study was to evaluate the role of microbial enzymes in normal and abnormal cervicovaginal fluids of cervical dysplasia. The cervicovaginal infections were evaluated through the estimation of microbial enzymes in patients with and without abnormal cervical cytology like bacterial and fungal infections. The patients were categorized based on infection caused by organism and stages of dysplasia. The pH, Whiff test, and Pap smear tests were conducted for normal and abnormal cervical swabs based on standard protocols. Microbial enzymes include mucinase, sialidases, and proteases of the cervical swabs and are estimated according to standard methods. The results of abnormal cervical cytological smears showed increased pH and the presence of amines with different levels of Pap smear test. Increased levels of microbial enzymes were observed in patients with abnormal cytology than normal cytology. Three microbial enzymes mucinase, sialidase, and protease were significantly (P < 0.01) more elevated in patients with bacterial infections (8.97 ± 0.64, 10.39 ± 0.28, 8.12 ± 0.64) than without dysplasia (2.02 ± 0.8, 1.98 ± 0.3, 1.96 ± 0.8). The results reinforce that the microbial infection seems to be more prone to cervical dysplasia and may act as risk-factor for the development of cervical cancer along with HPV infection.

Image analysis for retinopathy of prematurity diagnosis

PURPOSE

To review findings from the authors' published studies involving telemedicine and image analysis for retinopathy of prematurity (ROP) diagnosis.

METHODS

Twenty-two ROP experts interpreted a set of 34 wide-angle retinal images for presence of plus disease. For each image, a reference standard diagnosis was defined from expert consensus. A computer-based system was used to measure individual and linear combinations of image parameters for arteries and veins: integrated curvature (IC), diameter, and tortuosity index (TI). Sensitivity, specificity, and receiver operating characteristic areas under the curve (AUC) for plus disease diagnosis were determined for each expert. Sensitivity and specificity curves were calculated for the computer-based system by varying the diagnostic cutoffs for arterial IC and venous diameter. Individual vessels from the original 34 images were identified with particular diagnostic cutoffs, and combined into composite wide-angle images using graphics editing software.

RESULTS

For plus disease diagnosis, expert sensitivity ranged from 0.308-1.000, specificity from 0.571-1.000, and AUC from 0.784 to 1.000. Among computer system parameters, one linear combination had AUC 0.967, which was greater than that of 18 of 22 (81.8%) experts. Composite computer-generated images were produced using the arterial IC and venous diameter values associated with 75% under-diagnosis of plus disease (ie, 25% sensitivity cutoff), 50% under-diagnosis of plus disease (ie, 50% sensitivity cutoff), and 25% under-diagnosis of plus disease (ie, 75% sensitivity cutoff).

CONCLUSIONS

Computer-based image analysis has the potential to diagnose severe ROP with comparable or better accuracy than experts, and could provide added value to telemedicine systems. Future quantitative definitions of plus disease might improve diagnostic objectivity.

Plus disease in retinopathy of prematurity: pilot study of computer-based and expert diagnosis

PURPOSE

To measure accuracy of plus disease diagnosis by recognized experts in retinopathy of prematurity (ROP), and to conduct a pilot study examining performance of a computer-based image analysis system, Retinal Image multiScale Analysis (RISA).

METHODS

Twenty-two ROP experts independently interpreted a set of 34 wide-angle retinal images for presence of plus disease. A reference standard diagnosis based on expert consensus was defined for each image. Images were analyzed by the computer-based system using individual and linear combinations of system parameters for arterioles and venules: integrated curvature (IC), diameter, and tortuosity index (TI). Sensitivity, specificity, and receiver operating characteristic areas under the curve (AUC) for plus disease diagnosis compared with the reference standard were determined for each expert, as well as for the computer-based system.

RESULTS

Expert sensitivity ranged from 0.308 to 1.000, specificity ranged from 0.571 to 1.000, and AUC ranged from 0.784 to 1.000. Among individual computer system parameters, venular IC had highest AUC (0.853). Among all computer system parameters, the linear combination of arteriolar IC, arteriolar TI, venular IC, venular diameter, and venular TI had highest AUC (0.967), which was greater than that of 18 (81.8%) of 22 experts.

CONCLUSIONS

Accuracy of ROP experts for plus disease diagnosis is imperfect. A computer-based image analysis system has potential to diagnose plus disease with high accuracy. Further research involving RISA system parameter cut-off values from this study are required to fully validate performance of this computer-based system compared with that of human experts.

State of the Art Symposium: Prescreening and rescreening

The effectiveness of prescreening and rescreening cervical cytology preparations-whether used as quality assurance measures or in the attempt to improve sensitivity-has engendered broad discussion within the cytology community. This paper forms an introduction to some of the issues surrounding prescreening and rescreening in cervical cytology.