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

Rapid high-resolution mid-IR imaging for molecular spectral histopathological diagnosis of oesophageal cancers

This thesis is written as part of Marie-Curie international training network called Mid-TECH. Mid-TECH is devoted to improve mid-infrared (MIR) technologies and consists of 15 PhD projects across European universities. This thesis aims to evaluate new technologies and concepts developed by the project partners for their applicability in a biomedical setting. The clinical problem to diagnose oesophageal cancers serves as an example case for this. The thesis consists of three projects all aimed to further the understanding of MIR hyperspectral imaging. The first project discussed in chapter 5 demonstrates the use of an new design of the United States Airforce resolution test chart. The new test chart is developed to evaluate spatial resolution of MIR hyperspectral imaging systems. The use of different materials is discussed and the new iteration of the thes chart is evaluated using a state of the art MIR imaging system. The second project discussed in chapter 6 evaluates the technical differences and their practical implications of discrete frequency MIR imaging systems compared to continuum source systems. A comparison of the two system types is drawn for imaging paraffin embedded sections of oesophageal tissue. Furthermore the effect of chemically removing the paraffin from the sample is compared to a mathematical correction algorithm. The system performance is compared based on their ability to differentiate healthy from cancerous tissue. The third project discussed in chapter 7 evaluates the potential of a new MIR detection scheme called upconversion in combination with a novel MIR laser source. It is a prove of concept study demonstrating that those two technologies can be deployed to do hyperspectral imaging in the MIR.European Commissio

Applications of advanced spectroscopic imaging to biological tissues

The objectives of this research were to develop experimental approaches that can be applied to classify different stages of malignancy in routine formalin-fixed and paraffin-embedded tissues and to optimise the imaging approaches using novel implementations. It is hoped that the approach developed in this research may be applied for early cancer diagnostics in clinical settings in the future in order to increase cancer survival rates. Infrared spectroscopic imaging has recently shown to have great potential as a powerful method for the spatial visualization of biological tissues. This spectroscopic technique does not require sample labelling because its chemical specificity allows the differentiation of biocomponents to be achieved based on their chemical structures. Experiments were performed on 3-µm thick prostate and colon tissues that were deposited on 2 mm-calcium fluoride (CaF2) which were subsequently deparaffinised. The samples were measured under IR microscopes, in both transmission and attenuated total reflection (ATR) mode. In transmission, thermo-spectroscopic imaging of the prostate samples was first carried out to investigate the potential of thermography to complement the information obtained from IR spectral. Spectroscopic imaging has made the acquisition of chemical map of a sample possible within a short time span since this approach facilitates the simultaneous acquisition of thousands of spatially resolved infrared spectra. Spectral differences in the lipid region (3000 -2800 cm-1) were identified between cancer and benign regions within prostate tissues. The governing spectral band for classification was anti-symmetric stretching of CH2 (2921 cm-1) from PCA analysis. Nonetheless, the difference in tissue emissivity at room temperature was minimal, thus the contrast in the thermal image is low for intra-tissue classification. Besides, the thermal camera could only capture IR light between 3333-2000 cm-1. To record spectral data between 3900 - 900 cm-1 (mid-IR), Fourier transform infrared (FTIR) spectroscopic imaging was used to classify the different stages of colon disease. An automated processing framework was developed, that could achieve an overall classification accuracy of 92.7%. The processing steps included unsupervised k-means clustering of lipid bands, followed by Random Forest (RF) classification using the ‘fingerprint’ region of the data. The implementation of a correcting lens and the effect of the RMieS-EMSC correction on the tissue spectra were also investigated, which showed that computational RMieS-EMSC correction was more effective at removing spectral artefacts than the correcting lens. Furthermore, the effect of the fluctuations of surrounding humidity where the experiments were carried out was studied by using various supersaturated salt solutions. Significant peak changes of the phosphate band were observed, most notably the peak shift of the anti-symmetric stretching of phosphate bands from 1230 cm-1 to 1238 cm-1 was observed. By regulating and controlling humidity at its lowest, the classification accuracy of the colon specimens was improved without having to resort to alteration on the RF machine learning algorithm. In the ATR mode, additional apertures were introduced to the FTIR microscope, as a novel means of depth profiling the prostate tissue samples by changing the angle of incidence of IR light beam. Despite the successful attempts in capturing the qualitative information on the change of tissue morphology with the depth of penetration (dp), the spectral data were not suitable for further processing with machine learning as dp changes with wavelengths. Apart from the apertures, a ‘large-area’ germanium (Ge) crystal was introduced to enable simultaneous mapping and imaging of the colon tissue samples. Many advantages of this new implementation were observed, which included improvement in signal-to-noise ratio, uniform distribution, and no impression left on the sample. The research done in this thesis set a groundwork for clinical diagnosis and the novel implementations were transferable to studies of other samples.Open Acces

Histopathological image analysis: a review,”

Abstract-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

Quantitative chemical imaging: A top-down systems pathology approach to predict colon cancer patient survival

Colon cancer is the second deadliest cancer, affecting the quality of life in older patients. Prognosis is useful in developing an informed disease management strategy, which can improve mortality as well as patient comfort. Morphometric assessment provides diagnosis, grade, and stage information. However, it is subjective, requires multi-step sample processing, and annotations by pathologists. In addition, morphometric techniques offer minimal molecular information that can be crucial in determining prognosis. The interaction of the tumor with its surrounding stroma, comprised of several biomolecular factors and cells is a critical determinant of the behavior of the disease. To evaluate this interaction objectively, we need biomolecular profiling in spatially specific context. In this work, we achieved this by analyzing tissue microarrays using infrared spectroscopic imaging. We developed supervised classification algorithms that were used to reliably segment colon tissue into histological components, including differentiation of normal and desmoplastic stroma. Thus, infrared spectroscopic imaging enabled us to map the stromal changes around the tumor. This supervised classification achieved >0.90 area under the curve of the receiver operating characteristic curve for pixel level classification. Using these maps, we sought to define evaluation criteria to assess the segmented colon images to determine prognosis. We measured the interaction of tumor with the surrounding stroma containing activated fibroblast in the form of mathematical functions that took into account the structure of tumor and the prevalence of reactive stroma. Using these functions, we found that the interaction effect of large tumor size in the presence of a high density of activated fibroblasts provided patients with worse outcome. The overall 6-year probability of survival in patient groups that were classified as “low-risk” was 0.73 whereas in patients that were “high-risk” was 0.54 at p-value <0.0003. Remarkably, the risk score defined in this work was independent of patient risk assessed by stage and grade of the tumor. Thus, objective evaluation of prognosis, which adds to the current clinical regimen, was achieved by a completely automated analysis of unstained patient tissue to determine the risk of 6-year death. In this work, we demonstrate that quantitative chemical imaging using infrared spectroscopic imaging is an effective method to measure tumor-tumor microenvironment interactions. As a top-down systems pathology approach, our work integrated morphometry based spatial constraints and biochemistry based stromal changes to identify markers that gave us mechanistic insights into the tumor behavior. Our work shows that while the tumor microenvironment changes are prognostic, an interaction model that takes into account both the extent of microenvironment modifications, as well as the tumor morphology, is a better predictor of prognosis. Finally, we also developed automated tumor grade determination using deep learning based infrared image analysis. Thus, the computational models developed in this work provide an objective, processing-free and automated way to predict tumor behavior

Artificial Intelligence in Oral Health

This Special Issue is intended to lay the foundation of AI applications focusing on oral health, including general dentistry, periodontology, implantology, oral surgery, oral radiology, orthodontics, and prosthodontics, among others

Applications and limits of raman spectroscopy in the study of colonic and pulmonary malformations.

2006/2007Questa tesi nasce dalla collaborazione tra il Dipartimento di Materiali e Risorse Naturali dell’Università degli Studi di Trieste ed il Dipartimento di Chirurgia Pediatrica dell’Ospedale Infantile Burlo Garofalo. L’obiettivo del nostro gruppo di studio era di valutare le possibili applicazioni della spettroscopia Raman nello studio dei tessuti umani, con particolare attenzione verso i tessuti affetti da malformazioni congenite. L’interesse verso la spettroscopia Raman, che è una spettroscopia vibrazionale basata sullo scattering inelastico di fotoni, nasce dal fatto che questa tecnica può fornire dettagli precisi sulla composizione chimica e sulla struttura molecolare di cellule e tessuti. Durante lo svolgersi del progetto, sono state standardizzate le procedure di conservazione e preparazione dei campioni, i quali sono stati prelevati, con il consenso parentale, durante interventi chirurgici. Mediante l’uso di uno spettrometro Raman equipaggiato con un laser emittente luce monocromatica a 785 nm, sono stati analizzati campioni di colon e polmone normale, rappresentativi di un tessuto prevalentemente stratificato e di un parenchima omogeneo. Sono stati studiati anche tessuti polmonari malformati affetti da Malformazione Adenomatoide Cistica (CCAM) e Sequestri Broncopolmonari (BPS). Vengono descritte le procedure di acquisizione e processazione dei dati. Dopo applicazione di un’analisi multivariata come la k-means cluster analisi, sono state ottenute delle pseudo-mappe Raman colorate, che sono state poi confrontate con gli stessi campioni nativi non colorati, apposti su vetrino. Da ogni cluster sono stati estratti gli spettri Raman medi, che sono stati confrontati per evidenziare differenze fra aree diverse del campione. L’assegnazione delle principali bande alle diverse specie chimiche è stata fatta secondo la letteratura. L’analisi Raman è stata in grado di differenziare i diversi strati del colon ( sierosa, muscolatura, sottomucosa, mucosa, plessi nervosi), evidenziando strutture subcellulari in elementi nervosi quali i gangli. Sezioni normali e malformate di polmone hanno dimostrato una clusterizzazione e degli spettri medi diversi, permettendo una differenziazione tra CCAM e BPS, tanto che in un caso la nostra analisi, non concordando con la diagnosi del patologo, ha indotto una revisione dei vetrini e una riformulazione della diagnosi. Gli effetti di ampliamento del segnale Raman per risonanza di cromofori quali il gruppo eme dell’emoglobina con la radiazione a 785 nm sono stati discussi ed abbiamo proposto un metodo per minimizzare il contributo spettrale di questa molecola. Abbiamo inoltre confrontato i dati Raman con i dati ottenuti sugli stessi campioni presso l’Istituto di Chimica Analitica dell’ Università di Dresda mediante un’altra spettroscopia vibrazionale quale la spettroscopia ad Infrarosso. Ci è stata accordata per la discussione di questa tesi la possibilità di presentare i dati, per un confronto fra le due tecniche in relazione a tempi di acquisizione, risoluzione spaziale e spettrale.This thesis originates from a collaboration of the Department of Materials and Natural Resources of the University of Trieste with the Department of Pediatric Surgery of Burlo Garofalo Hospital for Sick Children. The goal of our research group was to evaluate the possible applications of Raman spectroscopy to the study of biological tissue with particular interest toward tissues affected by congenital malformations. The interest toward Raman spectroscopy, which is a vibrational spectroscopy based on inelastic scattering of photons, arises from the fact that this spectroscopic technique can provide details of the chemical composition and molecular structures of cells and tissues. A standard procedure was defined for the preparation and preservation of samples, which were collected during surgical operations. Samples of normal colon and lung, therefore representing a stratified and homogeneous tissue types, were evaluated using a Raman spectrometer, equipped with a 785 nm emitting laser. Lung specimens affected by congenital malformations such as Congenital Cystic Adenomatoid Malformation (CCAM) and Bronchopulmonary Sequestration (BPS) were studied as well. Data acquisition and data processing procedures are described. After application of a multivariate analysis such as k-means clustering, pseudo-color Raman maps of the samples were obtained and compared with the native unstained specimens. Average spectra from the clusters were compared to extract differences among areas within the samples. Assignments of major bands in the spectra to chemical compounds were performed according to previous literature. Raman analysis was able to discriminate between the different layers of colon (serosa, muscles, mucosa, submucosa, nervous plexi), evidencing up to subcellular features in nervous elements such as ganglia. Normal and affected lung specimens showed different predominant clusters and average spectra. Raman spectroscopy could distinguish between CCAM and BPS and in one case prompted our pathologist to review and correct her previous diagnosis. Resonance enhanced Raman signals from chromophors such as the heme group of hemoglobin are evidenced and discussed and a method to minimize spectral contribution of this molecule is proposed. Comparison with another vibrational spectroscopy such as Infrared Spectroscopy showed that these two techniques are complementary rather than alternative spectroscopies. Infrared data from the same samples studied by Raman were acquired at the Institute of Analytical Chemistry of the Dresden University and granted for this dissertation. Acquisition times, spatial and spectral resolution between the two techniques are discussed.XIX Ciclo197