Automated segmentation of tissue images for computerized IHC analysis

This paper presents two automated methods for the segmentation ofimmunohistochemical tissue images that overcome the limitations of themanual approach aswell as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologie

Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review

Instance segmentation of nuclei and glands in the histology images is an important step in computational pathology workflow for cancer diagnosis, treatment planning and survival analysis. With the advent of modern hardware, the recent availability of large-scale quality public datasets and the community organized grand challenges have seen a surge in automated methods focusing on domain specific challenges, which is pivotal for technology advancements and clinical translation. In this survey, 126 papers illustrating the AI based methods for nuclei and glands instance segmentation published in the last five years (2017-2022) are deeply analyzed, the limitations of current approaches and the open challenges are discussed. Moreover, the potential future research direction is presented and the contribution of state-of-the-art methods is summarized. Further, a generalized summary of publicly available datasets and a detailed insights on the grand challenges illustrating the top performing methods specific to each challenge is also provided. Besides, we intended to give the reader current state of existing research and pointers to the future directions in developing methods that can be used in clinical practice enabling improved diagnosis, grading, prognosis, and treatment planning of cancer. To the best of our knowledge, no previous work has reviewed the instance segmentation in histology images focusing towards this direction.Comment: 60 pages, 14 figure

Dual-Layer Spectral CT as Innovative Imaging Guidance in Lung Biopsies: Could Color-Coded Z-Effective Images Allow More Diagnostic Samplings and Biomarkers Information?

The aim of the study was to try to obtain more information on diagnostic samplings and biomarkers using dual-layer spectral CT in lung biopsies. Lung biopsies were performed by merging images obtained with CBCT with those from spectral CT to use them as functional guidance, experimenting with double sampling to determine the difference between the area with a higher Z-effective number and that with a lower Z-effective number. Ten patients with large lung lesions on spectral CT were selected and underwent percutaneous transthoracic lung mass biopsy. Technical success was calculated. The percentage of neoplastic, inflammatory, fibrotic, necrotic cells, or non-neoplastic lung parenchyma was reported. The possibility of carrying out immunohistochemical or molecular biology investigations was analyzed. All lesions were results malignant in 10/10 samples in the Zmax areas; in the Zmin areas, malignant cells were found in 7/10 samples. Technical success was achieved in 100% of cases for Zmax sampling and in 70% for Zmin sampling (p-value: 0.2105). The biomolecular profile was detected in 9/10 (90%) cases in Zmax areas, while in 4/10 (40%) cases in Zmin areas (p-value: 0.0573). The advantage of Z-effective imaging would be to identify a region of the lesion that is highly vascularized and probably richer in neoplastic cells, thus decreasing the risk of obtaining a non-diagnostic biopsy sample

Quantitative mRNA detection with advanced nonlinear microscopy

Cell-specific information on quantity and localization of key mRNA transcripts in single-cell level are critical to the assessment of cancer risk, therapy efficacy, and effective prevention strategies. While current techniques are not capable to visualize single mRNA transcript beyond the diffraction limit. In this thesis, two nonlinear technologies, second harmonic super-resolution microscopy (SHaSM) and transient absorption microscopy (TAM), are developed to detect and quantify single Human edimer receptor 2 (Her2) mRNA transcripts. The SHaSM is used to detect single mRNA transcript beyond the diffraction limit, while the TAM is employed to detect mRNA without the interference of fluorescence background. The thesis presents the fundamental study on the probes used in SHaSM, the concept and instrumental layout of the two technologies, and the detection as well as quantification of mRNA transcript in cells and tissues by super resolution microscopy and background-free detection microscopy. The first part of my dissertation focuses on the introduction of available mRNA detection methods and nonlinear imaging techniques. In chapter 2, I mainly characterize the SHG emission behavior of individual BTO nanocrystals via time-resolved single molecule spectroscopy, correlation spectroscopy, and confocal microscopy. High-intensity stable emission is collected from individual BTO nanocrystals with a high signal-to-noise ratio; the polar-dependent emission behavior of individual BTO NCs was also investigated theoretically and experimentally; and the dynamics of individual BTO in turbid medium is studied by an improved autocorrelation spectroscopy. The third chapter develops a novel second harmonic super-resolution microscopy (SHaSM), which is capable of detecting individual BTO nanocrystals with the lateral resolution as high as 30 nm. Motivated by the capability of SHaSM to visualize single BTO nanocrystals beyond the diffraction limit, we develop a dimer configuration of BTO nanocrystals for detecting single mRNA transcript beyond the diffraction limit. We validate our SHaSM to resolve single mRNA transcript first in vitro. Preformed BTO dimers are detected and differentiated by the SHaSM and by the SEM as the control. Expression level and localization patterns of Her2 mRNA transcript in single SKBR3, MCF7, and HeLa cell are investigated with the SHaSM. SHaSM can successfully differentiate the Her2 mRNA from the nonspecific BTO monomers, and identify more than one transcript in a diffraction-limited spot for SKBR3 cells. Quantification results agree well with the theoretical estimation and the RNA FISH results, and in addition it shows that the SHaSM has more accurate quantification when detecting over-expressed mRNA transcript. Furthermore we applied the SHG probes and SHaSM to study the heterogeneity of Her2 mRNA transcript in breast cancer tissues. High-specific binding of the SHG probes is observed and high penetration detection can be realized. In addition to the SHaSM, I also develop a background-free method to detect and quantify mRNA transcript. A femto-second transient absorption microscopy (TAM) is developed in the lab. It starts with the theoretical description of the TAM process, and then introduce the fundamental optical properties of the gold nanoparticles in TAM. By chemically treating the gold nanoparticles and conjugating with ODN probes, the gold nanoparticles hybridize to the mRNA molecules and are visualized in the TAM, together with label-free images of cells obtained in the SRS microscopy. mRNA is quantified with single copy sensitivity and is validated by the FISH approach. Super resolution microscopy of Her2 mRNA transcript in single cells will provide more accurate quantification in single cells; what\u27s more, it can be potentially employed to investigate the dynamics of single mRNA transcript beyond the diffraction limit, which is extremely significant in basic biology. TAM microscopy promotes the detection of mRNA transcript at a high speed without fluorescence background, which can be further utilized to investigate the dynamics of RNA regulation. Both these two methods will promote our understandings of the expression level and localization patterns of mRNA transcript in single cells, provide a route to employ mRNA transcript as a marker or indicator for cancer diagnosis and therapy

SEGMENTATION AND INFORMATICS IN MULTIDIMENSIONAL FLUORESCENCE OPTICAL MICROSCOPY IMAGES

Recent advances in the field of optical microscopy have enabled scientists to observe and image complex biological processes across a wide range of spatial and temporal resolution, resulting in an exponential increase in optical microscopy data. Manual analysis of such large volumes of data is extremely time consuming and often impossible if the changes cannot be detected by the human eye. Naturally it is essential to design robust, accurate and high performance image processing and analysis tools to extract biologically significant results. Furthermore, the presentation of the results to the end-user, post analysis, is also an equally challenging issue, especially when the data (and/or the hypothesis) involves several spatial/hierarchical scales (e.g., tissues, cells, (sub)-nuclear components). This dissertation concentrates on a subset of such problems such as robust edge detection, automatic nuclear segmentation and selection in multi-dimensional tissue images, spatial analysis of gene localization within the cell nucleus, information visualization and the development of a computational framework for efficient and high-throughput processing of large datasets. Initially, we have developed 2D nuclear segmentation and selection algorithms which help in the development of an integrated approach for determining the preferential spatial localization of certain genes within the cell nuclei which is emerging as a promising technique for the diagnosis of breast cancer. Quantification requires accurate segmentation of 100 to 200 cell nuclei in each patient tissue sample in order to draw a statistically significant result. Thus, for large scale analysis involving hundreds of patients, manual processing is too time consuming and subjective. We have developed an integrated workflow that selects, following 2D automatic segmentation, a sub-population of accurately delineated nuclei for positioning of fluorescence in situ hybridization labeled genes of interest in tissue samples. Application of the method was demonstrated for discriminating normal and cancerous breast tissue sections based on the differential positioning of the HES5 gene. Automatic results agreed with manual analysis in 11 out of 14 cancers, all 4 normal cases and all 5 non-cancerous breast disease cases, thus showing the accuracy and robustness of the proposed approach. As a natural progression from the 2D analysis algorithms to 3D, we first developed a robust and accurate probabilistic edge detection method for 3D tissue samples since several down stream analysis procedures such as segmentation and tracking rely on the performance of edge detection. The method based on multiscale and multi-orientation steps surpasses several other conventional edge detectors in terms of its performance. Subsequently, given an appropriate edge measure, we developed an optimal graphcut-based 3D nuclear segmentation technique for samples where the cell nuclei are volume or surface labeled. It poses the problem as one of finding minimal closure in a directed graph and solves it efficiently using the maxflow-mincut algorithm. Both interactive and automatic versions of the algorithm are developed. The algorithm outperforms, in terms of three metrics that are commonly used to evaluate segmentation algorithms, a recently reported geodesic distance transform-based 3D nuclear segmentation method which in turns was reported to outperform several other popular tools that segment 3D nuclei in tissue samples. Finally, to apply some of the aforementioned methods to large microscopic datasets, we have developed a user friendly computing environment called MiPipeline which supports high throughput data analysis, data and process provenance, visual programming and seamlessly integrated information visualization of hierarchical biological data. The computational part of the environment is based on LONI Pipeline distributed computing server and the interactive information visualization makes use of several javascript based libraries to visualize an XML-based backbone file populated with essential meta-data and results

Biomedical Sensing and Imaging

This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor

A cell level automated approach for quantifying antibody staining in immunohistochemistry images. A structural approach for quantifying antibody staining in colonic cancer spheroid images by integrating image processing and machine learning towards the implementation of computer aided scoring of cancer markers.

Immunohistological (IHC) stained images occupy a fundamental role in the pathologist¿s diagnosis and monitoring of cancer development. The manual process of monitoring such images is a subjective, time consuming process that typically relies on the visual ability and experience level of the pathologist. A novel and comprehensive system for the automated quantification of antibody inside stained cell nuclei in immunohistochemistry images is proposed and demonstrated in this research. The system is based on a cellular level approach, where each nucleus is individually analyzed to observe the effects of protein antibodies inside the nuclei. The system provides three main quantitative descriptions of stained nuclei. The first quantitative measurement automatically generates the total number of cell nuclei in an image. The second measure classifies the positive and negative stained nuclei based on the nuclei colour, morphological and textural features. Such features are extracted directly from each nucleus to provide discriminative characteristics of different stained nuclei. The output generated from the first and second quantitative measures are used collectively to calculate the percentage of positive nuclei (PS). The third measure proposes a novel automated method for determining the staining intensity level of positive nuclei or what is known as the intensity score (IS). The minor intensity features are observed and used to classify low, intermediate and high stained positive nuclei. Statistical methods were applied throughout the research to validate the system results against the ground truth pathology data. Experimental results demonstrate the effectiveness of the proposed approach and provide high accuracy when compared to the ground truth pathology data

INVESTIGATING INVASION IN DUCTAL CARCINOMA IN SITU WITH TOPOGRAPHICAL SINGLE CELL GENOME SEQUENCING

Synchronous Ductal Carcinoma in situ (DCIS-IDC) is an early stage breast cancer invasion in which it is possible to delineate genomic evolution during invasion because of the presence of both in situ and invasive regions within the same sample. While laser capture microdissection studies of DCIS-IDC examined the relationship between the paired in situ (DCIS) and invasive (IDC) regions, these studies were either confounded by bulk tissue or limited to a small set of genes or markers. To overcome these challenges, we developed Topographic Single Cell Sequencing (TSCS), which combines laser-catapulting with single cell DNA sequencing to measure genomic copy number profiles from single tumor cells while preserving their spatial context. We applied TSCS to sequence 1,293 single cells from 10 synchronous DCIS patients. We also applied deep-exome sequencing to the in situ, invasive and normal tissues for the DCIS-IDC patients. Previous bulk tissue studies had produced several conflicting models of tumor evolution. Our data support a multiclonal invasion model, in which genome evolution occurs within the ducts and gives rise to multiple subclones that escape the ducts into the adjacent tissues to establish the invasive carcinomas. In summary, we have developed a novel method for single cell DNA sequencing, which preserves spatial context, and applied this method to understand clonal evolution during the transition between carcinoma in situ to invasive ductal carcinoma