Automated Nuclei Segmentation on Dysplastic Oral Tissues Using CNN

Nuclei segmentation is a fundamental step to measure cell motility and morphology in High-throughput microscopy images. One prerequisite of automatic nuclei segmentation is to manually label a huge amount of cell images, which is a labor-intensive and time-consuming process. This paper develops a semi-supervised learning approach to reduce the dependence on the amount of labeled images, and it consists of three main steps. First, cell regions are automatically extracted from the background through a novel convolutional neural network (CNN). Comparing with the state-of-art segmentation network structure, our designed network structure has fewer parameters and combines layers of the feature hierarchy that refines the spatial precision of the output probability map. Second, discriminator networks are explored to produce confidence maps for unlabeled microscopy images by applying a fully convolution network (FCN) as the discriminator network. It also can be used to improve the performance of the segmentation network by coupling the adversarial loss with standard cross-entropy loss. Finally, combining both labeled images and unlabeled images with confidence maps to train the segmentation network can identify individual nuclei on the extracted cell regions. Experimental results on only 84 images showed that SEG score could achieve 77.9% and F1 score could achieve 76.0%, which outperformed existing using 670 images segmentation methods. Such promising result suggested that the proposed semi-supervised nuclei segmentation approach could achieve high segmentation accuracy with a small set of labeled images, which is a desirable property to analyze microscopy images in the real clinical practice.

Thalamic nuclei have been implicated in several neurological diseases. Thalamic nuclei parcellation from structural MRI is challenging due to poor intra-thalamic nuclear contrast while methods based on diffusion and functional MRI are affected by limited spatial resolution and image distortion. Existing multi-atlas based techniques are often computationally intensive and time-consuming. In this work, we propose a 3D convolutional neural network (CNN) based framework for thalamic nuclei parcellation using T1-weighted Magnetization Prepared Rapid Gradient Echo (MPRAGE) images. Transformation of images to an efficient representation has been proposed to improve the performance of subsequent classification tasks especially when working with limited labeled data. We investigate this by transforming the MPRAGE images to White-Matter-nulled MPRAGE (WMn-MPRAGE) contrast, previously shown to exhibit good intra-thalamic nuclear contrast, prior to the segmentation step. We trained two 3D segmentation frameworks using MPRAGE images (n = 35 subjects): (a) a native contrast segmentation (NCS) on MPRAGE images and (b) a synthesized contrast segmentation (SCS) where synthesized WMn-MPRAGE representation generated by a contrast synthesis CNN were used. Thalamic nuclei labels were generated using THOMAS, a multi-atlas segmentation technique proposed for WMn-MPRAGE images. The segmentation accuracy and clinical utility were evaluated on a healthy cohort (n = 12) and a cohort (n = 45) comprising of healthy subjects and patients with alcohol use disorder (AUD), respectively. Both the segmentation CNNs yielded comparable performances on most thalamic nuclei with Dice scores greater than 0.84 for larger nuclei and at least 0.7 for smaller nuclei. However, for some nuclei, the SCS CNN yielded significant improvements in Dice scores (medial geniculate nucleus, P = 0.003, centromedian nucleus, P = 0.01) and percent volume difference (ventral anterior, P = 0.001, ventral posterior lateral, P = 0.01) over NCS. In the AUD cohort, the SCS CNN demonstrated a significant atrophy in ventral lateral posterior nucleus in AUD patients compared to healthy age-matched controls (P = 0.01), agreeing with previous studies on thalamic atrophy in alcoholism, whereas the NCS CNN showed spurious atrophy of the ventral posterior lateral nucleus. CNN-based segmentation of thalamic nuclei provides a fast and automated technique for thalamic nuclei prediction in MPRAGE images. The transformation of images to an efficient representation, such as WMn-MPRAGE, can provide further improvements in segmentation performance.

As is known, pathologists visually examine the tissue distributions by using microscopes traditionally. The rise in digital image processing and machine learning also allows high-performance computerized analysis of histology images taken with modern imaging systems. In general, histological image segmentation is the first step in the quantitative analysis of histology images. Therefore, a high-accuracy segmentation is essential for histology image analysis in most cases. In this paper, we performed a deep Convolutional Neural Networks (CNNs) based nuclei segmentation study on colon histology images. By using the U-Net biomedical image segmentation model, it is aimed to classify each pixel in colon histology images into one of the following 6 types of nucleus: epithelial, lymphocyte, plasma, eosinophil, neutrophil, connective tissue or classify it as the image background. In comprehensive experimental tests performed on Colon Nuclei Identification and Counting (CoNIC) Challenge dataset, commonly used segmentation and classification metrics were measured, and promising segmentation performances were achieved.

With the use of computer-aided diagnostic systems, the automatic detection and segmentation of the cell nuclei have become essential in pathology due to cellular nuclei counting and nuclear pleomorphism analysis are critical for the classification and grading of breast cancer histopathology. This work describes a methodology for automatic detection and segmentation of cellular nuclei in breast cancer histopathology images obtained from the BreakHis database, the Standford tissue microarray database, and the Breast Cancer Cell Segmentation database. The proposed scheme is based on the characterization of Hematoxylin and Eosin (H&E) staining, size, and shape features. In addition, we use the information obtained from morphological transformations and adaptive intensity adjustments to detect and separate each cell nucleus detected in the image. The segmentation was carried out by testing the proposed methodology in a histological breast cancer database that provides the associated groundtruth segmentation. Subsequently, the Sørensen-Dice similarity coefficient was calculated to analyze the suitability of the results.Clinical relevance- In this work, the detection and segmentation of cell nuclei in breast cancer histological images are carried out automatically. The method can identify cell nuclei regardless of variations in the level of staining and image magnification. Moreover, a granulometric analysis of the components allows identifying cell clumps and segment them into individual cell nuclei. Improved identification of cell nuclei under different image conditions was demonstrated to reach a sensitivity average of 0.76 ± 0.12. The results provide a base for further and complex processes such as cell counting, feature analysis, and nuclear pleomorphism, which are relevant tasks in the evaluation and diagnostic performed by the expert pathologist.

Automated nuclei segmentation and classification are the keys to analyze and understand the cellular characteristics and functionality, supporting computer-aided digital pathology in disease diagnosis. However, the task still remains challenging due to the intrinsic variations in size, intensity, and morphology of different types of nuclei. Herein, we propose a self-guided ordinal regression neural network for simultaneous nuclear segmentation and classification that can exploit the intrinsic characteristics of nuclei and focus on highly uncertain areas during training. The proposed network formulates nuclei segmentation as an ordinal regression learning by introducing a distance decreasing discretization strategy, which stratifies nuclei in a way that inner regions forming a regular shape of nuclei are separated from outer regions forming an irregular shape. It also adopts a self-guided training strategy to adaptively adjust the weights associated with nuclear pixels, depending on the difficulty of the pixels that is assessed by the network itself. To evaluate the performance of the proposed network, we employ large-scale multi-tissue datasets with 276349 exhaustively annotated nuclei. We show that the proposed network achieves the state-of-the-art performance in both nuclei segmentation and classification in comparison to several methods that are recently developed for segmentation and/or classification.

Common oral lesions found during a mass screening examination

Nuclei Segmentation is a very essential and intermediate step for automatic cancer detection from H and E stained histopathology images. In the recent advent, the rise of Convolutional Neural Network (CNN), has enabled researchers to detect nuclei automatically from histopathology images with higher accuracy. However, the performance of automatic nuclei segmentation by CNN is fraught with overfitting, due to very less number of annotated segmented images available. Indeed, we find that the problem of nuclei segmentation is an unsupervised problem, because still now there is no automatic tool available which can make annotated images (nuclei segmented images) accurately, to the best of our knowledge. In this research article, we present a Logarithmic-Base2 of Gaussian (Log-Base2-G) Kernel which has the ability to track only the nuclei portions automatically from Colorectal Cancer H and E stained histopathology images. First, Log-Base2-G Kernel is applied to the input images. Thereafter, we apply an adaptive Canny Edge detector, in order to segment only the nuclei edges from H and E stained histopathology images. Experimental results revealed that our proposed method achieved higher accuracy and F1 score, without the help of any annotated data which is a significant improvement. We have used two different datasets (Con-SeP dataset, and Glass-contest dataset, both contains Colorectal Cancer histopathology images) to check the effectiveness and validity of our proposed method. These results have shown that our proposed method outperformed other image processing or unsupervised methods both qualitatively and quantitatively.

Cervical cancer is identified as the fourth most recurrent cancer among women across the globe. The cancer is treatable, if identified at the early stage. Pap smear test is the most common and the best tool for initial screening of cancer. Pap smear cell level image analysis is an open issue. The limitation of the analysis is due to the complexity of the cell structure. The smear cell image is composed of cytoplasm and nucleus. The shape and structure of the nucleus determines the cancer prevalence. Segmentation of nucleus is an important step in cancer detection. There are various methods developed for nucleus segmentation. The article proposes multithresholding algorithm to segment cytoplasm and nucleus region from the background. Morphological operations are used for correcting the segmented output. Support vector machine classifier is used for classifying the smear cell as normal or abnormal based on the extracted features of the segmented output. The obtained accuracy of the classifier, sensitivity and specificity for single smear cell are 99.66%, 99.85% and 99.17%.

The number of nuclei on a pathology image assists pathologists in consistent diagnosis of breast cancer. Currently, most pathologists make a diagnosis based on a rough estimation of the number of nuclei on pathology images. Because of the rough estimation, the diagnosis is not objective. To assist pathologists to make a consistent, objective and fast diagnosis, it is necessary to develop a computer system to automatically recognize and count several kinds of nuclei. We have developed an algorithm for the automatic segmentation and counting of nuclei in breast cancer pathology images. In the development of the algorithm, we proposed two novel methods: an adaptive-sized hybrid neural network for the automatic segmentation of nuclei, insulin-like growth factor-II messenger RNAs and other structures, and the combined use of both the focused gradient filter and the watersheds algorithm for segmentation of overlapped nuclei.

IntroductionSurgical treatment for subglottic stenosis often faces recurrence of narrowing and patients continue to face quality of life issues. Currently, biomechanical models that inform surgical treatments are difficult to create due to vocal fold intricacies and trouble distinguishing between tissue types–muscle, mucosa, and cartilage. Improved models of laryngeal microanatomy can be used to assess and predict surgical outcomes for patients who suffer from subglottic stenosis. While manual segmentation of histology is the standard, automatic segmentation can be more efficient, sensitive, and specific in tissue differentiation. Manual segmentation faces challenges like extensive operator time and inter‐rater subjectivity. The aim of this study, therefore, was to qualitatively compare manual segmentation to automatic k‐means segmentation of whole‐slide histology rabbit vocal fold images. This is the preliminary step towards enhancing segmentation and, later, achieving a high‐fidelity human larynx model.Methods134 whole‐slide histology images were manually segmented from seven rabbits using colored masks for muscle, cartilage, mucosa, and adipose tissue. Of those images, 31 were also selected for segmentation using k‐means for initial qualitative comparison. At least two images were segmented per rabbit. K‐means segmentation is optimal for our multidimensional data. Since it is deterministic, we selected the number of clusters as our parameter. After inspecting n=1‐10 clusters, n=4 was chosen as qualitatively producing the most distinct clusters without oversampling.ResultsFor our preliminary results, we compared the histological image, the manual segmentation, and the automatically segmented image. In the automatic segmentations, tissue type is distinguished by the grayscale contrast. The four classes visible are cartilage, muscle, adipose tissue, and mucosa. There is occasional loss of visualization of adipose tissue and mucosa, however, the cartilage and muscle tissues remain distinct. The mucosal area boundaries remain clear despite some visualization loss within the mucosal borders, therefore, mucosa can be distinguished from adjacent cartilage and muscle. Training on a large dataset like other machine learning algorithms is unnecessary for k‐means, which is ideal for our relatively small sample size. The speed and versatility of k‐means has qualitatively demonstrated comparable results to our manual segmentations.ConclusionFuture directions for our project include conducting k‐means segmentation of the remainder of the 134 images collected. We have also tested mean‐shift clustering and a deep learning convolutional neural network (CNN) to compare automatic segmentation methods to one another, and to the standard manual segmentation/ground truth. Future testing also includes quantitative analysis of these algorithms’ accuracy. Beyond our histology images, we have at least 14 MR images (at least two per rabbit) of the larynges as well. We intend to combine and overlay the structural detail of the histology images with the soft tissue differentiation of the MRI.

Glands segmentation is a very important and yet challenging problem of histological images analysis. Accurate segmentation of mucous glands is a crucial step to obtain reliable morphological statistics and is necessary for the development of high-quality diagnostic algorithms, which are an integral part of timely medical care. In this paper we propose a two-stage segmentation method, which predicts the probability maps of glands boundaries in histological images based on a priori knowledge about the geometric shape of the mucous glands and uses a convolutional neural network (CNN) model to get the final segmentation result based on the predicted probability maps. The proposed method demonstrates good results in separating adjacent glands, which is one of the most challenging aspects in automatic segmentation of histological glands and one of the most complicated for algorithms based on applying convolutional neural networks. The evaluation of the proposed algorithm was performed with Warwick-QU dataset, which contains real histological images of colon tissue.

Convolutional Neural Networks (CNNs) have been successfully applied in the medical diagnosis of different types of diseases. However, selecting the architecture and the best set of hyperparameters among the possible combinations can be a significant challenge. The purpose of this work is to investigate the use of the Hyperband optimization algorithm in the process of optimizing a CNN applied to the diagnosis of SARS-Cov2 disease (COVID-19). The test was performed with the Optuna framework, and the optimization process aimed to optimize four hyperparameters: (1) backbone architecture, (2) the number of inception modules, (3) the number of neurons in the fully connected layers, and (4) the learning rate. CNNs were trained on 2175 computed tomography (CT) images. The CNN that was proposed by the optimization process was a VGG16 with five inception modules, 128 neurons in the two fully connected layers, and a learning rate of 0.0027. The proposed method achieved a sensitivity, precision, and accuracy of 97%, 82%, and 88%, outperforming the sensitivity of the Real-Time Polymerase Chain Reaction (RT-PCR) tests (53–88%) and the accuracy of the diagnosis performed by human experts (72%).

Computer-aided diagnostics of cancer pathologies based on histological image segmentation is a promising area in the field of computer vision and machine learning. To date, the successes of neural networks in image segmentation in a number of tasks are comparable to human results and can even exceed them. The paper presents a fast algorithm of histological image segmentation based on the convolutional neural network U-Net. Using this approach allows to get better results in the tasks of medical image segmentation. The developed algorithm based on neural network AlexNet was used for the creation of the automatic markup of the histological image database. The neural network algorithms were trained and tested on the NVIDIA DGX-1 supercomputer using histological images. The results of the research show that the fast algorithm based on neural network U-Net can be successfully used for the histological image segmentation in real medical practice, which is confirmed by the high level of similarity of the obtained markup with the expert one.

Background: Candida is the most common cause of fungal oral lesion. Only few decades back, role of Candida in the process of infection was considered as passive and only host's organic weakness was the known mechanism. However, this concept is revamped recently and it is now stated that Candida can actively participate in the process of disease establishment and progression by aggression known as virulence factors. Biolm is one of the important virulence factors of Candida spp. The present study was conducted with an aim to evaluate biolm forming ability among Candida species isolated from various types of oral and maxillofacial lesions. Methodology: Candida spp. isolated fromoral lesionswere identied upto species level by standard mycological protocol. Biolm formation was studied by Confocal Scanning Laser Microscope. Results: A total of 104 Candida spp. were isolated from patients with oral lesions. Leukoplakia, tobacco pouch keratosis and pseudomembranous whitish lesion were the common oral lesions. C. albicans were isolated from 83 (79.8%) patients with oral lesions whereas non-albicansCandida (NAC) spp. were isolated from 21 (20.2%) cases.Atotal of 80 (76.9%) Candida isolates from oral lesions showed biolm formation. As compared to other Candida spp., C. tropicalis showed high ability to form biolm. Candida isolates from oral lesions like leukoplakia and pseudomembranous whitish lesions showed maximum biolm formation. Conclusion: Study of virulence factors like biolm formation will aid in better understanding of association between oral lesions and Candida spp. It may be also facilitate to explore new therapeutic modalities in future.

The purpose of this study is to develop hyperspectral imaging (HSI) for automatic detection of head and neck cancer cells on histologic slides. A compact hyperspectral microscopic system is developed in this study. Histologic slides from 15 patients with squamous cell carcinoma (SCC) of the larynx and hypopharynx are imaged with the system. The proposed nuclei segmentation method based on principle component analysis (PCA) can extract most nuclei in the hyperspectral image without extracting other sub-cellular components. Both spectra-based support vector machine (SVM) and patch-based convolutional neural network (CNN) are used for nuclei classification. CNNs were trained with both hyperspectral images and pseudo RGB images of extracted nuclei, in order to evaluate the usefulness of extra information provided by hyperspectral imaging. The average accuracy of spectra-based SVM classification is 68%. The average AUC and average accuracy of the HSI patch-based CNN classification is 0.94 and 82.4%, respectively. The hyperspectral microscopic imaging and classification methods provide an automatic tool to aid pathologists in detecting SCC on histologic slides.