Browsing by Author "Aksac, Alper"

Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemOpen Access
    BreCaHAD: a dataset for breast cancer histopathological annotation and diagnosis
    (2019-02-12) Aksac, Alper; Demetrick, Douglas J; Ozyer, Tansel; Alhajj, Reda
    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.
  • Thumbnail Image
    ItemOpen Access
    CACTUS: cancer image annotating, calibrating, testing, understanding and sharing in breast cancer histopathology
    (2020-01-06) Aksac, Alper; Ozyer, Tansel; Demetrick, Douglas J; Alhajj, Reda
    Abstract Objective Develop CACTUS (cancer image annotating, calibrating, testing, understanding and sharing) as a novel web application for image archiving, annotation, grading, distribution, networking and evaluation. This helps pathologists to avoid unintended mistakes leading to quality assurance, teaching and evaluation in anatomical pathology. Effectiveness of the tool has been demonstrated by assessing pathologists performance in the grading of breast carcinoma and by comparing inter/intra-observer assessment of grading criteria amongst pathologists reviewing digital breast cancer images. Reproducibility has been assessed by inter-observer (kappa statistics) and intra-observer (intraclass correlation coefficient) concordance rates. Results CACTUS has been evaluated using a surgical pathology application—the assessment of breast cancer grade. We used CACTUS to present standardized images to four pathologists of differing experience. They were asked to evaluate all images to determine their assessment of Nottingham grade of a series of breast carcinoma cases. For each image, they were asked for their overall grade impression. CACTUS helps and guides pathologists to improve disease diagnosis with higher confidence and thereby reduces their workload and bias. 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.
  • Thumbnail Image
    ItemOpen Access
    Intelligent Medical Image Analysis for Quality Assurance, Teaching and Evaluation
    (2020-06-23) Aksac, Alper; Alhajj, Reda; Demetrick, Douglas James; Rokne, Jon G.; Moshirpour, Mohammad; Karray, Fakhreddine O.
    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.