Automatic Tumor-Stroma Separation in Fluorescence TMAs Enables the Quantitative High-Throughput Analysis of Multiple Cancer Biomarkers

The upcoming quantification and automation in biomarker based histological tumor evaluation will require computational methods capable of automatically identifying tumor areas and differentiating them from the stroma. As no single generally applicable tumor biomarker is available, pathology routinely uses morphological criteria as a spatial reference system. We here present and evaluate a method capable of performing the classification in immunofluorescence histological slides solely using a DAPI background stain. Due to the restriction to a single color channel this is inherently challenging. We formed cell graphs based on the topological distribution of the tissue cell nuclei and extracted the corresponding graph features. By using topological, morphological and intensity based features we could systematically quantify and compare the discrimination capability individual features contribute to the overall algorithm. We here show that when classifying fluorescence tissue slides in the DAPI channel, morphological and intensity based features clearly outpace topological ones which have been used exclusively in related previous approaches. We assembled the 15 best features to train a support vector machine based on Keratin stained tumor areas. On a test set of TMAs with 210 cores of triple negative breast cancers our classifier was able to distinguish between tumor and stroma tissue with a total overall accuracy of 88%. Our method yields first results on the discrimination capability of features groups which is essential for an automated tumor diagnostics. Also, it provides an objective spatial reference system for the multiplex analysis of biomarkers in fluorescence immunohistochemistry

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

Automated Image Analysis of Cancer Tissue Adapted for Virtual Microscopy

Emerging large-scale digitization of microscopic tissue samples (i.e. virtual microscopy) in biomarker research and clinical pathology enables rapid, objective and repeatable computational analysis of the images. Automated image analysis is likely to be especially useful in personalized medicine, where high-throughput analysis is required for risk prediction, advanced diagnostics and targeted treatment of patients. Malignant tumors are profiled in detail to identify clinically relevant mutations and aberrant protein expression levels. Human observers are still predominantly visually interpreting the increasing number of biomarker assays with fluorescence in situ hybridization (FISH) and immunohistochemical (IHC) stainings. To aid in these quantification tasks, novel applications for automated image analysis of cancer tissues are needed. Virtual microscopy samples require large digital storage space, and image size reduction techniques should be addressed prior to archiving of the images. In this thesis, tools for high-throughput biomarker research in a digital microscopy environment were developed, assessed and adapted to a virtual microscopy setting. The first algorithm developed is intended for automated quantitative assessment of FISH signals to determine the HER2 gene amplification status in breast cancer tissue images, and proved to be comparable to visual scoring. The extent of Ki-67 staining determined in breast cancer tissue images by the second automated algorithm was a significant predictor of patient outcome in both uni- and multivariate analyses. The third algorithm for automated segmentation of tissue images divided the colorectal cancer images into epithelial and stromal compartments with high accuracy. In addition, image compression and scaling led to significant reductions in image sizes without compromising the results of the second and third algorithms introduced previously. The algorithms developed in this thesis are freely accessible to be used by the research community, facilitating external validation of the algorithms. After further validation studies, the algorithms can potentially be applied in clinical pathology especially within diagnostics, risk prediction and targeted treatment of cancer patients in a personalized medicine setting.Patologin työ sisältää runsaasti mikroskooppinäytteiden tulkintaa. Perinteisesti näytteitä on katsottu tavallisella mikroskoopilla, ja näytteistä tarvittavat merkkiaineiden laskennat on tehty silmämääräisesti. Varsinkin laajojen tutkimusaineistojen tulkintaan tarvitaan vaihtoehtoinen menetelmä, sillä työ vaatii paljon aikaa, ja patologien määrä on rajallinen. Tietokoneiden ja digitaalisen kuvantamisen kehittyminen on mahdollistanut virtuaalimikroskopian eli mikroskooppinäytteiden kuvaamisen kokonaisuudessaan suurella tarkkuudella. Kuvattuja näytteitä voidaan katsella internetin välityksellä tavallisella verkkoselaimella. Digitoidut mikroskooppinäytteet mahdollistavat myös kuvien automaattisen tulkinnan tietokoneavusteisella konenäöllä. Yksi virtuaalimikroskopian suurimmista haasteista on kuvien vaatima suuri tallennustila. Tässä väitöskirjassa kehitettiin kolme erilaista menetelmää digitoitujen mikroskooppinäytteiden automaattiseen tulkintaan. Tavoitteena oli päästä patologin tulkintaa vastaaviin tuloksiin. Näytteet olivat peräisin laajoista rinta- ja paksusuolisyöpäsarjoista, ja potilaista oli saatavilla taustatiedot pitkältä seuranta-ajalta. Patologin tekemiä laskentoja verrattiin tilastollisesti konenäön tuottamiin tuloksiin. Väitöskirjatyössä tutkittiin myös kuvapakkauksen ja -skaalauksen vaikutusta konenäön antamiin tuloksiin. Kehitetyt konenäkömenetelmät kykenivät toistamaan luotettavasti patologin antamien laskentojen tulokset. Yksi menetelmistä kykeni jopa ennustamaan potilaan selviytymistä paremmin kuin patologin laskennan perusteella voitiin ennustaa. Konenäkömenetelmät sietivät kohtalaisen suurta kuvapakkausta ja -skaalausta ilman menetelmien tuottamien tulosten heikkenemistä. Väitöskirja osoittaa, että esitetyt automaattiset konenäkömenetelmät ovat luotettavia ja niillä voidaan korvata työläitä silmämääräisiä laskentoja tutkimuskäytössä. Menetelmät pitää vielä luotettavasti validoida jatkotutkimuksissa, jotta niitä voitaisiin hyödyntää myös potilastyössä. Menetelmien avulla voidaan kohdentaa rajallisia patologiresursseja työläistä laskennoista muualle. Konenäkö työskentelee väsymättömästi, joten esimerkiksi yöajat voidaan hyödyntää mikroskooppikuvien automaattiseen tulkintaan. Virtuaalimikroskopian vaatimaa tallennustilaa tietokoneilla voidaan vähentää käyttämällä kuvapakkausta ja -skaalausta, joiden ei todettu heikentävän tässä väitöskirjassa esiteltyjen konenäkömenetelmien toimintaa

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