BreCaHAD: A dataset for breast cancer histopathological annotation and diagnosis

Objectives: Histopathological tissue analysis by a pathologist determines the diagnosis and prognosis of most tumors, such as breast cancer. To estimate the aggressiveness of cancer, a pathologist evaluates the microscopic appearance of a biopsied tissue sample based on morphological features which have been correlated with patient outcome. Data description: This paper introduces a dataset of 162 breast cancer histopathology images, namely the breast cancer histopathological annotation and diagnosis dataset (BreCaHAD) which allows researchers to optimize and evaluate the usefulness of their proposed methods. The dataset includes various malignant cases. The task associated with this dataset is to automatically classify histological structures in these hematoxylin and eosin (H&E) stained images into six classes, namely mitosis, apoptosis, tumor nuclei, non-tumor nuclei, tubule, and non-tubule. By providing this dataset to the biomedical imaging community, we hope to encourage researchers in computer vision, machine learning and medical fields to contribute and develop methods/tools for automatic detection and diagnosis of cancerous regions in breast cancer histology images. © 2019 The Author(s)

Intelligent Medical Image Analysis for Quality Assurance, Teaching and Evaluation

Manually spotting and annotating the affected area(s) on histopathological images with high accuracy is regarded as the gold standard in cancer diagnosis and grading. However, this is a time-consuming and tedious task that requires considerable effort, expertise and experience of a pathologist. These are gained over time by analyzing more cases. Whereas this visual interpretation has strict guidelines. This brings a certain subjectivity to the histological analysis, and therefore, leads to inter/intra-observer variability and some reproducibility issues. Besides, these issues may have a direct effect on patient prognosis and treatment plan. These problems can be alleviated by developing automated image analysis tools for digitized histopathology. Thanks to the rapid development in the image capturing and analysis technology which could be employed to not only give more insight to pathologists, but also guide them in detecting and grading diseases. These quantitative computational tools aim to improve the quality of pathology researchers in terms of speed and accuracy. Thus, it is very important to develop an automatic assessment tool for quantitative and qualitative analysis to help remove this drawback. The main contribution of this thesis is an intelligent system for quality assurance, teaching and evaluation applications in anatomical pathology. We present a spatial clustering algorithm, named CutESC (Cut-Edge for Spatial Clustering) with a graph-based approach. CutESC performs clustering automatically for complicated shapes and different density without requiring any prior information and parameters. We have developed an automatic cell nuclei detection method where the proposed solution uses the traditional CNN learning scheme solely to detect nuclei, and then applies single-pass voting with spatial clustering explicitly to detect them. We also propose an automated method to identify and locate the mitotic cells, and tubules in histopathology images using deep neural network frameworks. We present a dataset of breast cancer histopathology images named BreCaHAD which is publicly available to the biomedical imaging community. Moreover, we propose an efficient method for salient region detection. Finally, we introduce a new tool called CACTUS (Cancer Image Annotating, Calibrating, Testing, Understanding and Sharing) which is proposed to help and guide pathologists in their effort to improve disease diagnosis and thereby reduce their workload and bias among them. CACTUS can be useful for both disseminating anatomical pathology images for teaching, as well as for evaluating agreement amongst pathologists or against a gold standard for evaluation or quality assurance

Complex networks driven salient region detection based on superpixel segmentation

In this paper, we propose an efficient method for salient region detection. First, the image is decomposed by using superpixel segmentation which groups similar pixels and generates compact regions. Based upon the generated superpixels, similarity between the regions is calculated by benefiting from color, location, histogram, intensity, and area information of each region as well as community identification via complex networks theory in the over-segmented image. Then, contrast, distribution and complex networks based saliency maps are generated by using the mentioned features. These saliency maps are used to create a final saliency map. The applicability, effectiveness and consistency of the proposed approach are illustrated by conducting some experiments using publicly available datasets. The tests have been used to compare the proposed method with some state-of-the-art methods. The reported results cover qualitative and quantitative assessments which demonstrate that our approach outputs high quality saliency maps and mostly achieves the highest precision rate compared to the other methods

Boosting real-time recognition of hand posture and gesture for virtual mouse operations with segmentation

The design and implementation of polylogarithmically or polynomially bounded algorithms on faster processors has gained popularity and attracted the attention of both researchers and practitioners. The evolution in the computer hardware technology has boosted the development of real-time applications which are expected to respond within a strict time frame. One attractive sophisticated application, which requires real time response, is image capturing and recognition for effective human computer interaction. It is gaining popularity, especially after the development of hand held devices and touch screens. Real-time video processing response time is expressed by means of frame sequences; device dependent capability (20 frame/sec) designates real-time restrictions (a frame is needed to be processed within 50 ms). Video processing of virtual mouse operations requires real-time recognition, i.e., no delay in response can be tolerated. There are indeed several attempts to recognize hand gestures for different purposes. Sign language recognition stands out as the most popular one. However, virtual mouse operations may also be used in general by the majority of people in parallel for the proliferation of different applications on a variety of platforms such as tablet PCs, embedded devices, etc. One significant advantage of such systems fulfills the need for extra hardware system. To this end, we have developed a novel real-time virtual mouse application. Our system architecture recognizes defined postures and gestures. We have implemented, tested, and compared the performance of four methods, namely Chai (static), face (dynamic), regional (dynamic), and Duan. Further, various conditions, such as lighting, distinguishing skin color, and complex background have been considered and discussed. © 2015, Springer Science+Business Media New York

BreCaHAD: a dataset for breast cancer histopathological annotation and diagnosis

Abstract Objectives Histopathological tissue analysis by a pathologist determines the diagnosis and prognosis of most tumors, such as breast cancer. To estimate the aggressiveness of cancer, a pathologist evaluates the microscopic appearance of a biopsied tissue sample based on morphological features which have been correlated with patient outcome. Data description This paper introduces a dataset of 162 breast cancer histopathology images, namely the breast cancer histopathological annotation and diagnosis dataset (BreCaHAD) which allows researchers to optimize and evaluate the usefulness of their proposed methods. The dataset includes various malignant cases. The task associated with this dataset is to automatically classify histological structures in these hematoxylin and eosin (H&E) stained images into six classes, namely mitosis, apoptosis, tumor nuclei, non-tumor nuclei, tubule, and non-tubule. By providing this dataset to the biomedical imaging community, we hope to encourage researchers in computer vision, machine learning and medical fields to contribute and develop methods/tools for automatic detection and diagnosis of cancerous regions in breast cancer histology images

CutESC: Cutting edge spatial clustering technique based on proximity graphs

In this paper, we propose a cut-edge algorithm for spatial clustering (CutESC) based on proximity graphs. The CutESC algorithm removes edges when a cut-edge value for the edge's endpoints is below a threshold. The cut-edge value is calculated by using statistical features and spatial distribution of data based on its neighborhood. Also, the algorithm works without any prior information and preliminary parameter settings while automatically discovering clusters with non-uniform densities, arbitrary shapes, and outliers. However, there is an option which allows users to set two parameters to better adapt clustering solutions for particular problems. To assess advantages of CutESC algorithm, experiments have been conducted using various two-dimensional synthetic, high-dimensional real-world, and image segmentation datasets. Published by Elsevier Ltd

Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends

Background: Breast cancer is a serious disease which affects many women and may lead to death. It has received considerable attention from the research community. Thus, biomedical researchers aim to find genetic biomarkers indicative of the disease. Novel biomarkers can be elucidated from the existing literature. However, the vast amount of scientific publications on breast cancer make this a daunting task. This paper presents a framework which investigates existing literature data for informative discoveries. It integrates text mining and social network analysis in order to identify new potential biomarkers for breast cancer. Results: We utilized PubMed for the testing. We investigated gene-gene interactions, as well as novel interactions such as gene-year, gene-country, and abstract-country to find out how the discoveries varied over time and how overlapping/diverse are the discoveries and the interest of various research groups in different countries. Conclusions: Interesting trends have been identified and discussed, e.g., different genes are highlighted in relationship to different countries though the various genes were found to share functionality. Some text analysis based results have been validated against results from other tools that predict gene-gene relations and gene functions. © 2016 Jurca et al