Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into "abnormal" and "normal" categories. However, the success of most traditional classification methods relies on the presence of accurate cell segmentations. Despite sixty years of research in this field, accurate segmentation remains a challenge in the presence of cell clusters and pathologies. Moreover, previous classification methods are only built upon the extraction of hand-crafted features, such as morphology and texture. This paper addresses these limitations by proposing a method to directly classify cervical cells-without prior segmentation-based on deep features, using convolutional neural networks (ConvNets). First, the ConvNet is pretrained on a natural image dataset. It is subsequently fine-tuned on a cervical cell dataset consisting of adaptively resampled image patches coarsely centered on the nuclei. In the testing phase, aggregation is used to average the prediction scores of a similar set of image patches. The proposed method is evaluated on both Pap smear and LBC datasets. Results show that our method outperforms previous algorithms in classification accuracy (98.3%), area under the curve (0.99) values, and especially specificity (98.3%), when applied to the Herlev benchmark Pap smear dataset and evaluated using five-fold cross validation. Similar superior performances are also achieved on the HEMLBC (H&E stained manual LBC) dataset. Our method is promising for the development of automation-assisted reading systems in primary cervical screening.

DeepPap: Deep Convolutional Networks for Cervical Cell Classification.

Automated classification of Pap smear images to detect cervical dysplasia

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.

Semi-Automatic Segmentation and Classification of Pap Smear Cells

Classification of cervical cells in Pap smear images is a challenging task due to the limitations these images exhibit and the complexity of the morphological changes in the structural parts of the cells. This procedure is very important as it provides fundamental information for the detection of cancerous or precancerous lesions. For this reason several algorithms have been proposed in order to classify normal and abnormal cells in such images. However, it is a common phenomenon that each research group usually creates its own dataset of images, as well-established datasets are not publicly available. To overcome this obstacle and to assist the research progress in this field, we present an annotated image database of Pap smear images, in which the cells are categorized in five different classes, depending on their cytomorphological features. The area of the cytoplasm and the nucleus in each image is manually defined by experts and salient features of intensity, texture and shape are calculated for each region of interest. Several experiments have been performed for the classification of these images and they include feature and image based classification schemes. In this direction, methods based on support vector machines and deep neural networks are tested and the performance of each classifier is presented in order to constitute a reference point for the evaluation of future classification techniques.

Sipakmed: A New Dataset for Feature and Image Based Classification of Normal and Pathological Cervical Cells in Pap Smear Images

Cytologic screening has been widely used for controlling the prevalence of cervical cancer. Errors from sampling, screening and interpretation, still concealed some unpleasant results. This study aims at designing a cellular image analysis system based on feasible and available software and hardware for a routine cytologic laboratory. Totally 1814 cellular images from the liquid-based cervical smears with Papanicolaou stain in 100x, 200x, and 400x magnification were captured by a digital camera. Cell images were reviewed by pathologic experts with peer agreement and only 503 images were selected for further study. The images were divided into 4 diagnostic categories. A PC-based cellular image analysis system (PCCIA) was developed for computing morphometric parameters. Then support vector machine (SVM) was used to classify signature patterns. The results show that the selected 13 morphometric parameters can be used to correctly differentiate the dysplastic cells from the normal cells (p<0.001). Additionally, SVM classifier has been demonstrated to be able to achieve a high accuracy for cellular classification. In conclusion, the proposed system provides a feasible and effective tool for the evaluation of gynecologic cytologic specimens.

Quantitative assessment of Pap smear cells by PC-based cytopathologic image analysis system and support vector machine

Cytology evaluation is a safe, efficient, and well-establis hed technique for the diagnoses of many diseases. Its abilit y to reduce the mortality and morbidity of cervical cancer is through mass screening to early detect dysplasia or pre-invasive cancer cells. Classical cytological diagnosis is based on microscopic observation of specialized cells and qualitative assessment using descriptive criteria, which may be inconsistent because of subjective variability of different observers. Recently, web-based le arning is becoming prevalent in schools and enterprises around the world for its advantages of providing easy access to information and knowledge, supporting ubiquitous learning environment, and increasing cost-effectiveness for both educational institution an d students. The objectives of this study were to design automatic classifier s based on integrated genetic algorithm (GA) and support vector machine (SVM) to cluster four different types of cervical cells and t o discriminate dysplasia from normal cells, as well as to implement a web-based cytopathology training and testing system to increase learning efficiency of cytopathologic education. A p rototypic system composed of a microscope, digital camera, personal computer, cellular processing and analyzing program, and cell classifier was designed to facilitate acquisition, image processing a nd analysis, and classification of cell images. Furthermore , a web-based cytopathology training and testing (WBCTT) systems were developed based on the classified cell images to train students , resident physicians, and novice pathologists to discriminate vario us types of cervical cells. The experimental results demonstrate that the classification and diagnostic accuracy achieves 96.82% and 99.6% , respectively. System evaluation based on questionnaire survey of extended technology acceptance model (TAM) shows that the proposed system embedded with cell classifier and WBCTT is u seful in cytopathology diagnosis and training. Most of the users agreed the operation interface is friendly and easy to use. They also expressed strong behaviour intention to further adopt the system. It i s expected to have significant contributions in increasing d iagnostic efficiency and promoting learning efficiency.

http://naturalspublishing.com/files/published/oh6c4rd35dn861.pdf

Po-Chi Huang

Papers

Semi-Automatic Segmentation and Classification of Pap Smear Cells

Quantitative assessment of Pap smear cells by PC-based cytopathologic image analysis system and support vector machine

Design and Evaluation of Cervical Pap Smear E-learning System for the Education of Cytopathology