Classification of Cervical Precursor Lesions via Local Histogram and Cell Morphometric Features.

Cervical squamous intra-epithelial lesions (SIL) are precursor cancer lesions and their diagnosis is important because patients have a chance to be cured before cancer develops. In the diagnosis of the disease, pathologists decide by considering the cell distribution from the basal to the upper membrane. The idea, inspired by the pathologists' point of view, is based on the fact that cell amounts differ in the basal, central, and upper regions of tissue according to the level of Cervical Intraepithelial Neoplasia (CIN). Therefore, histogram information can be used for tissue classification so that the model can be explainable. In this study, two different classification schemes are proposed to show that the local histogram is a useful feature for the classification of cervical tissues. The first classifier is Kullback Leibler divergence-based, and the second one is the classification of the histogram by combining the embedding feature vector from morphometric features. These algorithms have been tested on a public dataset.The method we propose in the study achieved an accuracy performance of 78.69% in a data set where morphology-based methods were 69.07% and Convolutional Neural Network (CNN) patch-based algorithms were 75.77%. The proposed statistical features are robust for tackling real-life problems as they operate independently of the lesions manifold.

Classification of analyzable metaphase images using transfer learning and fine tuning.

Chromosomes are bodies that contain human genetic information. Chromosomal disorders can cause structural and functional disorders in individuals. Detecting the metaphase stages of the cells accurately is a crucial step to detect possible defects in chromosomes. Thus, it is vital at this stage to identify the identical chromosome of each chromosome, to perform the pairing process, and to identify problems arising from this process. In this study, it was investigated whether the analyzable metaphase images can be analyzed by using the transfer learning and fine tuning approaches of deep learning models. The weights of VGG16 and InceptionV3 models trained with ImageNet data set were transferred to this problem and the classification process was carried out. True positive ratio values are 99%(± 0.9) and 99%(± 0.9) for VGG and Inception networks, respectively. The classification performances obtained depending on the changing training set ratios are presented comparatively in figures. F-measure, precision, and recall values obtained for the VGG and Inception networks were observed as 99%(± 1.0) and 99%(± 1.0), respectively. F-measure, precision, and recall values of VGG and Inceptionv3 networks are also presented with respect to the ratio of training size. The obtained results have compared with the state-of-the-art methods in the literature and supported with the tables and graphics. The training phase was also accelerated by using transfer learning and fine tuning methods. Transfer learning and fine tuning processes have almost similar performance as the models used in the literature and trained from scratch in metaphase Graphical Abstract The Flowchart of the proposed system for classifying metaphase candidates detection.

A whole-slide image grading benchmark and tissue classification for cervical cancer precursor lesions with inter-observer variability.

The cervical cancer developing from the precancerous lesions caused by the human papillomavirus (HPV) has been one of the preventable cancers with the help of periodic screening. Cervical intraepithelial neoplasia (CIN) and squamous intraepithelial lesion (SIL) are two types of grading conventions widely accepted by pathologists. On the other hand, inter-observer variability is an important issue for final diagnosis. In this paper, a whole-slide image grading benchmark for cervical cancer precursor lesions is created and the "Uterine Cervical Cancer Database" introduced in this article is the first publicly available cervical tissue microscopy image dataset. In addition, a morphological feature representing the angle between the basal membrane (BM) and the major axis of each nucleus in the tissue is proposed. The presence of papillae of the cervical epithelium and overlapping cell problems are also discussed. Besides that, the inter-observer variability is also evaluated by thorough comparisons among decisions of pathologists, as well as the final diagnosis.

Automatic cell segmentation in histopathological images via two-staged superpixel-based algorithms.

The analysis of cell characteristics from high-resolution digital histopathological images is the standard clinical practice for the diagnosis and prognosis of cancer. Yet, it is a rather exhausting process for pathologists to examine the cellular structures manually in this way. Automating this tedious and time-consuming process is an emerging topic of the histopathological image-processing studies in the literature. This paper presents a two-stage segmentation method to obtain cellular structures in high-dimensional histopathological images of renal cell carcinoma. First, the image is segmented to superpixels with simple linear iterative clustering (SLIC) method. Then, the obtained superpixels are clustered by the state-of-the-art clustering-based segmentation algorithms to find similar superpixels that compose the cell nuclei. Furthermore, the comparison of the global clustering-based segmentation methods and local region-based superpixel segmentation algorithms are also compared. The results show that the use of the superpixel segmentation algorithm as a pre-segmentation method improves the performance of the cell segmentation as compared to the simple single clustering-based segmentation algorithm. The true positive ratio (TPR), true negative ratio (TNR), F-measure, precision, and overlap ratio (OR) measures are utilized as segmentation performance evaluation. The computation times of the algorithms are also evaluated and presented in the study. Graphical Abstract The visual flowchart of the proposed automatic cell segmentation in histopathological images via two-staged superpixel-based algorithms.