Luminance adaptive biomarker detection in digital pathology images

Digital pathology is set to revolutionise traditional approaches diagnosing and researching diseases. To realise the full potential of digital pathology, accurate and robust computer techniques for automatically detecting biomarkers play an important role. Traditional methods transform the colour histopathology images into a gray scale image and apply a single threshold to separate positively stained tissues from the background. In this paper, we show that the colour distribution of the positive immunohis-tochemical stains varies with the level of luminance and that a single threshold will be impossible to separate positively stained tissues from other tissues, regardless how the colour pixels are transformed. Based on this, we propose two novel luminance adaptive biomarker detection methods. We present experimental results to show that the luminance adaptive approach significantly improves biomarker detection accuracy and that random forest based techniques have the best performances

Automated detection of regions of interest for tissue microarray experiments: an image texture analysis

BACKGROUND: Recent research with tissue microarrays led to a rapid progress toward quantifying the expressions of large sets of biomarkers in normal and diseased tissue. However, standard procedures for sampling tissue for molecular profiling have not yet been established. METHODS: This study presents a high throughput analysis of texture heterogeneity on breast tissue images for the purpose of identifying regions of interest in the tissue for molecular profiling via tissue microarray technology. Image texture of breast histology slides was described in terms of three parameters: the percentage of area occupied in an image block by chromatin (B), percentage occupied by stroma-like regions (P), and a statistical heterogeneity index H commonly used in image analysis. Texture parameters were defined and computed for each of the thousands of image blocks in our dataset using both the gray scale and color segmentation. The image blocks were then classified into three categories using the texture feature parameters in a novel statistical learning algorithm. These categories are as follows: image blocks specific to normal breast tissue, blocks specific to cancerous tissue, and those image blocks that are non-specific to normal and disease states. RESULTS: Gray scale and color segmentation techniques led to identification of same regions in histology slides as cancer-specific. Moreover the image blocks identified as cancer-specific belonged to those cell crowded regions in whole section image slides that were marked by two pathologists as regions of interest for further histological studies. CONCLUSION: These results indicate the high efficiency of our automated method for identifying pathologic regions of interest on histology slides. Automation of critical region identification will help minimize the inter-rater variability among different raters (pathologists) as hundreds of tumors that are used to develop an array have typically been evaluated (graded) by different pathologists. The region of interest information gathered from the whole section images will guide the excision of tissue for constructing tissue microarrays and for high throughput profiling of global gene expression

An end-to-end deep learning histochemical scoring system for breast cancer TMA

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham Prognostic Index Plus (NPI+) which uses breast cancer relevant biomarkers to stain tumour tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumour, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time consuming, imprecise and subjective process which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system which directly predicts the H-Score automatically. Our system imitates the pathologists’ decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumour and non-tumour), a second FCN to extract tumour nuclei region, and a multi-column convolutional neural network which takes the outputs of the first two FCNs and the stain intensity description image as input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as input and directly outputs a clinical score. We will present experimental results which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists’ scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists

Telepathology and Optical Biopsy

The ability to obtain information about the structure of tissue without taking a sample for pathology has opened the way for new diagnostic techniques. The present paper reviews all currently available techniques capable of producing an optical biopsy, with or without morphological images. Most of these techniques are carried out by physicians who are not specialized in pathology and therefore not trained to interpret the results as a pathologist would. In these cases, the use of telepathology or distant consultation techniques is essential

Breast Cancer Detection by Means of Artificial Neural Networks

Breast cancer is a fatal disease causing high mortality in women. Constant efforts are being made for creating more efficient techniques for early and accurate diagnosis. Classical methods require oncologists to examine the breast lesions for detection and classification of various stages of cancer. Such manual attempts are time consuming and inefficient in many cases. Hence, there is a need for efficient methods that diagnoses the cancerous cells without human involvement with high accuracies. In this research, image processing techniques were used to develop imaging biomarkers through mammography analysis and based on artificial intelligence technology aiming to detect breast cancer in early stages to support diagnosis and prioritization of high-risk patients. For automatic classification of breast cancer on mammograms, a generalized regression artificial neural network was trained and tested to separate malignant and benign tumors reaching an accuracy of 95.83%. With the biomarker and trained neural net, a computer-aided diagnosis system is being designed. The results obtained show that generalized regression artificial neural network is a promising and robust system for breast cancer detection. The Laboratorio de Innovacion y Desarrollo Tecnologico en Inteligencia Artificial is seeking collaboration with research groups interested in validating the technology being developed

Structure-Function Correlation of the Human Central Retina

The impact of retinal pathology detected by high-resolution imaging on vision remains largely unexplored. Therefore, the aim of the study was to achieve high-resolution structure-function correlation of the human macula in vivo.To obtain high-resolution tomographic and topographic images of the macula spectral-domain optical coherence tomography (SD-OCT) and confocal scanning laser ophthalmoscopy (cSLO), respectively, were used. Functional mapping of the macula was obtained by using fundus-controlled microperimetry. Custom software allowed for co-registration of the fundus mapped microperimetry coordinates with both SD-OCT and cSLO datasets. The method was applied in a cross-sectional observational study of retinal diseases and in a clinical trial investigating the effectiveness of intravitreal ranibizumab in macular telangietasia type 2. There was a significant relationship between outer retinal thickness and retinal sensitivity (p<0.001) and neurodegeneration leaving less than about 50 µm of parafoveal outer retinal thickness completely abolished light sensitivity. In contrast, functional preservation was found if neurodegeneration spared the photoreceptors, but caused quite extensive disruption of the inner retina. Longitudinal data revealed that small lesions affecting the photoreceptor layer typically precede functional detection but later cause severe loss of light sensitivity. Ranibizumab was shown to be ineffective to prevent such functional loss in macular telangietasia type 2.Since there is a general need for efficient monitoring of the effectiveness of therapy in neurodegenerative diseases of the retina and since SD-OCT imaging is becoming more widely available, surrogate endpoints derived from such structure-function correlation may become highly relevant in future clinical trials

Differently stained whole slide image registration technique with landmark validation

Abstract. One of the most significant features in digital pathology is to compare and fuse successive differently stained tissue sections, also called slides, visually. Doing so, aligning different images to a common frame, ground truth, is required. Current sample scanning tools enable to create images full of informative layers of digitalized tissues, stored with a high resolution into whole slide images. However, there are a limited amount of automatic alignment tools handling large images precisely in acceptable processing time. The idea of this study is to propose a deep learning solution for histopathology image registration. The main focus is on the understanding of landmark validation and the impact of stain augmentation on differently stained histopathology images. Also, the developed registration method is compared with the state-of-the-art algorithms which utilize whole slide images in the field of digital pathology. There are previous studies about histopathology, digital pathology, whole slide imaging and image registration, color staining, data augmentation, and deep learning that are referenced in this study. The goal is to develop a learning-based registration framework specifically for high-resolution histopathology image registration. Different whole slide tissue sample images are used with a resolution of up to 40x magnification. The images are organized into sets of consecutive, differently dyed sections, and the aim is to register the images based on only the visible tissue and ignore the background. Significant structures in the tissue are marked with landmarks. The quality measurements include, for example, the relative target registration error, structural similarity index metric, visual evaluation, landmark-based evaluation, matching points, and image details. These results are comparable and can be used also in the future research and in development of new tools. Moreover, the results are expected to show how the theory and practice are combined in whole slide image registration challenges. DeepHistReg algorithm will be studied to better understand the development of stain color feature augmentation-based image registration tool of this study. Matlab and Aperio ImageScope are the tools to annotate and validate the image, and Python is used to develop the algorithm of this new registration tool. As cancer is globally a serious disease regardless of age or lifestyle, it is important to find ways to develop the systems experts can use while working with patients’ data. There is still a lot to improve in the field of digital pathology and this study is one step toward it.Eri menetelmin värjättyjen virtuaalinäytelasien rekisteröintitekniikka kiintopisteiden validointia hyödyntäen. Tiivistelmä. Yksi tärkeimmistä digitaalipatologian ominaisuuksista on verrata ja fuusioida peräkkäisiä eri menetelmin värjättyjä kudosleikkeitä toisiinsa visuaalisesti. Tällöin keskenään lähes identtiset kuvat kohdistetaan samaan yhteiseen kehykseen, niin sanottuun pohjatotuuteen. Nykyiset näytteiden skannaustyökalut mahdollistavat sellaisten kuvien luonnin, jotka ovat täynnä kerroksittaista tietoa digitalisoiduista näytteistä, tallennettuna erittäin korkean resoluution virtuaalisiin näytelaseihin. Tällä hetkellä on olemassa kuitenkin vain kourallinen automaattisia työkaluja, jotka kykenevät käsittelemään näin valtavia kuvatiedostoja tarkasti hyväksytyin aikarajoin. Tämän työn tarkoituksena on syväoppimista hyväksikäyttäen löytää ratkaisu histopatologisten kuvien rekisteröintiin. Tärkeimpänä osa-alueena on ymmärtää kiintopisteiden validoinnin periaatteet sekä eri väriaineiden augmentoinnin vaikutus. Lisäksi tässä työssä kehitettyä rekisteröintialgoritmia tullaan vertailemaan muihin kirjallisuudessa esitettyihin algoritmeihin, jotka myös hyödyntävät virtuaalinäytelaseja digitaalipatologian saralla. Kirjallisessa osiossa tullaan siteeraamaan aiempia tutkimuksia muun muassa seuraavista aihealueista: histopatologia, digitaalipatologia, virtuaalinäytelasi, kuvantaminen ja rekisteröinti, näytteen värjäys, data-augmentointi sekä syväoppiminen. Tavoitteena on kehittää oppimispohjainen rekisteröintikehys erityisesti korkearesoluutioisille digitalisoiduille histopatologisille kuville. Erilaisissa näytekuvissa tullaan käyttämään jopa 40-kertaista suurennosta. Kuvat kudoksista on järjestetty eri menetelmin värjättyihin peräkkäisiin kuvasarjoihin ja tämän työn päämääränä on rekisteröidä kuvat pohjautuen ainoastaan kudosten näkyviin osuuksiin, jättäen kuvien tausta huomioimatta. Kudosten merkittävimmät rakenteet on merkattu niin sanotuin kiintopistein. Työn laatumittauksina käytetään arvoja, kuten kohteen suhteellinen rekisteröintivirhe (rTRE), rakenteellisen samankaltaisuuindeksin mittari (SSIM), sekä visuaalista arviointia, kiintopisteisiin pohjautuvaa arviointia, yhteensopivuuskohtia, ja kuvatiedoston yksityiskohtia. Nämä arvot ovat verrattavissa myös tulevissa tutkimuksissa ja samaisia arvoja voidaan käyttää uusia työkaluja kehiteltäessä. DeepHistReg metodi toimii pohjana tässä työssä kehitettävälle näytteen värjäyksen parantamiseen pohjautuvalle rekisteröintityökalulle. Matlab ja Aperio ImageScope ovat ohjelmistoja, joita tullaan hyödyntämään tässä työssä kuvien merkitsemiseen ja validointiin. Ohjelmointikielenä käytetään Pythonia. Syöpä on maailmanlaajuisesti vakava sairaus, joka ei katso ikää eikä elämäntyyliä. Siksi on tärkeää löytää uusia keinoja kehittää työkaluja, joita asiantuntijat voivat hyödyntää jokapäiväisessä työssään potilastietojen käsittelyssä. Digitaalipatologian osa-alueella on vielä paljon innovoitavaa ja tämä työ on yksi askel eteenpäin taistelussa syöpäsairauksia vastaan