Unsupervised HEp-2 mitosis recognition in indirect immunofluorescence imaging

Deep Active Learning for Automatic Mitotic Cell Detection on HEp-2 Specimen Medical Images

Identifying Human Epithelial Type 2 (HEp-2) mitotic cells is a crucial procedure in anti-nuclear antibodies (ANAs) testing, which is the standard protocol for detecting connective tissue diseases (CTD). Due to the low throughput and labor-subjectivity of the ANAs' manual screening test, there is a need to develop a reliable HEp-2 computer-aided diagnosis (CAD) system. The automatic detection of mitotic cells from the microscopic HEp-2 specimen images is an essential step to support the diagnosis process and enhance the throughput of this test. This work proposes a deep active learning (DAL) approach to overcoming the cell labeling challenge. Moreover, deep learning detectors are tailored to automatically identify the mitotic cells directly in the entire microscopic HEp-2 specimen images, avoiding the segmentation step. The proposed framework is validated using the I3A Task-2 dataset over 5-fold cross-validation trials. Using the YOLO predictor, promising mitotic cell prediction results are achieved with an average of 90.011% recall, 88.307% precision, and 81.531% mAP. Whereas, average scores of 86.986% recall, 85.282% precision, and 78.506% mAP are obtained using the Faster R-CNN predictor. Employing the DAL method over four labeling rounds effectively enhances the accuracy of the data annotation, and hence, improves the prediction performance. The proposed framework could be practically applicable to support medical personnel in making rapid and accurate decisions about the mitotic cells' existence.

Effectiveness of GAN-based Synthetic Samples Generation of Minority Patterns in HEp-2 Cell Images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020;2020:1376-1379. [PMID: 33018245 DOI: 10.1109/embc44109.2020.9175636]

In this paper, we present a framework to address the augmentation of images for the rare and minor appearance of mitotic type staining patterns, for Human Epithelium Type2 (HEp-2) cell images. The identification of mitotic patterns among non-mitotic/interphase patterns is important in the process of diagnosis of various autoimmune disorders. This task leads to a pattern classification problem between mitotic v/s interphase. However, among the two classes, typically, the number of mitotic cells are relatively very less. Thus, in this work, we propose to generate synthetic mitotic samples, which can be used to augment the number of mitotic samples and balance the samples of mitotic and interphase patterns in classification paradigm. An effective feature representation is used, to validate the usefulness of the synthetic samples in classification task, along with a subjective validation done by a medical expert. The results demonstrate that the approach of generating and mingling synthetic samples with existing training data works well and yields good performance, with 0.98 balanced class accuracy (BcA) in one case, over a public dataset, i.e., UQ-SNP I3A Task-3 mitotic cell identification dataset.

Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy

MOTIVATION

Cell microscopy datasets have great diversity due to variability in cell types, imaging techniques and protocols. Existing methods are either tailored to specific datasets or are based on supervised learning, which requires comprehensive manual annotations. Using the latter approach, however, poses a significant difficulty due to the imbalance between the number of mitotic cells with respect to the entire cell population in a time-lapse microscopy sequence.

RESULTS

We present a fully unsupervised framework for both mitosis detection and mother-daughters association in fluorescence microscopy data. The proposed method accommodates the difficulty of the different cell appearances and dynamics. Addressing symmetric cell divisions, a key concept is utilizing daughters' similarity. Association is accomplished by defining cell neighborhood via a stochastic version of the Delaunay triangulation and optimization by dynamic programing. Our framework presents promising detection results for a variety of fluorescence microscopy datasets of different sources, including 2D and 3D sequences from the Cell Tracking Challenge.

AVAILABILITY AND IMPLEMENTATION

Code is available in github (github.com/topazgl/mitodix).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mining textural knowledge in biological images: Applications, methods and trends

Texture analysis is a major task in many areas of computer vision and pattern recognition, including biological imaging. Indeed, visual textures can be exploited to distinguish specific tissues or cells in a biological sample, to highlight chemical reactions between molecules, as well as to detect subcellular patterns that can be evidence of certain pathologies. This makes automated texture analysis fundamental in many applications of biomedicine, such as the accurate detection and grading of multiple types of cancer, the differential diagnosis of autoimmune diseases, or the study of physiological processes. Due to their specific characteristics and challenges, the design of texture analysis systems for biological images has attracted ever-growing attention in the last few years. In this paper, we perform a critical review of this important topic. First, we provide a general definition of texture analysis and discuss its role in the context of bioimaging, with examples of applications from the recent literature. Then, we review the main approaches to automated texture analysis, with special attention to the methods of feature extraction and encoding that can be successfully applied to microscopy images of cells or tissues. Our aim is to provide an overview of the state of the art, as well as a glimpse into the latest and future trends of research in this area.