Accurate detection of dysmorphic nuclei using dynamic programming and supervised classification

A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows

Detection of complete and partial chromosome gains and losses by comparative genomic in situ hybridization

Comparative genomic in situ hybridization (CGH) provides a new possibility for searching genomes for imbalanced genetic material. Labeled genomic test DNA, prepared from clinical or tumor specimens, is mixed with differently labeled control DNA prepared from cells with normal chromosome complements. The mixed probe is used for chromosomal in situ suppression (CISS) hybridization to normal metaphase spreads (CGH-metaphase spreads). Hybridized test and control DNA sequences are detected via different fluorochromes, e.g., fluorescein isothiocyanate (FITC) and tetraethylrhodamine isothiocyanate (TRITC). The ratios of FITC/TRITC fluorescence intensities for each chromosome or chromosome segment should then reflect its relative copy number in the test genome compared with the control genome, e.g., 0.5 for monosomies, 1 for disomies, 1.5 for trisomies, etc. Initially, model experiments were designed to test the accuracy of fluorescence ratio measurements on single chromosomes. DNAs from up to five human chromosome-specific plasmid libraries were labeled with biotin and digoxigenin in different hapten proportions. Probe mixtures were used for CISS hybridization to normal human metaphase spreads and detected with FITC and TRITC. An epifluorescence microscope equipped with a cooled charge coupled device (CCD) camera was used for image acquisition. Procedures for fluorescence ratio measurements were developed on the basis of commercial image analysis software. For hapten ratios 4/1, 1/1 and 1/4, fluorescence ratio values measured for individual chromosomes could be used as a single reliable parameter for chromosome identification. Our findings indicate (1) a tight correlation of fluorescence ratio values with hapten ratios, and (2) the potential of fluorescence ratio measurements for multiple color chromosome painting. Subsequently, genomic test DNAs, prepared from a patient with Down syndrome, from blood of a patient with Tcell prolymphocytic leukemia, and from cultured cells of a renal papillary carcinoma cell line, were applied in CGH experiments. As expected, significant differences in the fluorescence ratios could be measured for chromosome types present in different copy numbers in these test genomes, including a trisomy of chromosome 21, the smallest autosome of the human complement. In addition, chromosome material involved in partial gains and losses of the different tumors could be mapped to their normal chromosome counterparts in CGH-metaphase spreads. An alternative and simpler evaluation procedure based on visual inspection of CCD images of CGH-metaphase spreads also yielded consistent results from several independent observers. Pitfalls, methodological improvements, and potential applications of CGH analyses are discussed

Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has the potential to increase intraoperative precision, extend resection, and tailor surgery for malignant invasive brain tumors because of its subcellular dimension resolution. Despite its promising diagnostic potential, interpreting the gray tone fluorescence images can be difficult for untrained users. In this review, we provide a detailed description of bioinformatical analysis methodology of CLE images that begins to assist the neurosurgeon and pathologist to rapidly connect on-the-fly intraoperative imaging, pathology, and surgical observation into a conclusionary system within the concept of theranostics. We present an overview and discuss deep learning models for automatic detection of the diagnostic CLE images and discuss various training regimes and ensemble modeling effect on the power of deep learning predictive models. Two major approaches reviewed in this paper include the models that can automatically classify CLE images into diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and models that can localize histological features on the CLE images using weakly supervised methods. We also briefly review advances in the deep learning approaches used for CLE image analysis in other organs. Significant advances in speed and precision of automated diagnostic frame selection would augment the diagnostic potential of CLE, improve operative workflow and integration into brain tumor surgery. Such technology and bioinformatics analytics lend themselves to improved precision, personalization, and theranostics in brain tumor treatment.Comment: See the final version published in Frontiers in Oncology here: https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful

Automated Discrimination of Pathological Regions in Tissue Images: Unsupervised Clustering vs Supervised SVM Classification

Recognizing and isolating cancerous cells from non pathological tissue areas (e.g. connective stroma) is crucial for fast and objective immunohistochemical analysis of tissue images. This operation allows the further application of fully-automated techniques for quantitative evaluation of protein activity, since it avoids the necessity of a preventive manual selection of the representative pathological areas in the image, as well as of taking pictures only in the pure-cancerous portions of the tissue. In this paper we present a fully-automated method based on unsupervised clustering that performs tissue segmentations highly comparable with those provided by a skilled operator, achieving on average an accuracy of 90%. Experimental results on a heterogeneous dataset of immunohistochemical lung cancer tissue images demonstrate that our proposed unsupervised approach overcomes the accuracy of a theoretically superior supervised method such as Support Vector Machine (SVM) by 8%

Rapid mapping of digital integrated circuit logic gates via multi-spectral backside imaging

Modern semiconductor integrated circuits are increasingly fabricated at untrusted third party foundries. There now exist myriad security threats of malicious tampering at the hardware level and hence a clear and pressing need for new tools that enable rapid, robust and low-cost validation of circuit layouts. Optical backside imaging offers an attractive platform, but its limited resolution and throughput cannot cope with the nanoscale sizes of modern circuitry and the need to image over a large area. We propose and demonstrate a multi-spectral imaging approach to overcome these obstacles by identifying key circuit elements on the basis of their spectral response. This obviates the need to directly image the nanoscale components that define them, thereby relaxing resolution and spatial sampling requirements by 1 and 2 - 4 orders of magnitude respectively. Our results directly address critical security needs in the integrated circuit supply chain and highlight the potential of spectroscopic techniques to address fundamental resolution obstacles caused by the need to image ever shrinking feature sizes in semiconductor integrated circuits

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe