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

Identification of tumor epithelium and stroma in tissue microarrays using texture analysis

<p>Abstract</p> <p>Background</p> <p>The aim of the study was to assess whether texture analysis is feasible for automated identification of epithelium and stroma in digitized tumor tissue microarrays (TMAs). Texture analysis based on local binary patterns (LBP) has previously been used successfully in applications such as face recognition and industrial machine vision. TMAs with tissue samples from 643 patients with colorectal cancer were digitized using a whole slide scanner and areas representing epithelium and stroma were annotated in the images. Well-defined images of epithelium (n = 41) and stroma (n = 39) were used for training a support vector machine (SVM) classifier with LBP texture features and a contrast measure C (LBP/C) as input. We optimized the classifier on a validation set (n = 576) and then assessed its performance on an independent test set of images (n = 720). Finally, the performance of the LBP/C classifier was evaluated against classifiers based on Haralick texture features and Gabor filtered images.</p> <p>Results</p> <p>The proposed approach using LPB/C texture features was able to correctly differentiate epithelium from stroma according to texture: the agreement between the classifier and the human observer was 97 per cent (kappa value = 0.934, <it>P </it>< 0.0001) and the accuracy (area under the ROC curve) of the LBP/C classifier was 0.995 (CI95% 0.991-0.998). The accuracy of the corresponding classifiers based on Haralick features and Gabor-filter images were 0.976 and 0.981 respectively.</p> <p>Conclusions</p> <p>The method illustrates the capability of automated segmentation of epithelial and stromal tissue in TMAs based on texture features and an SVM classifier. Applications include tissue specific assessment of gene and protein expression, as well as computerized analysis of the tumor microenvironment.</p> <p>Virtual slides</p> <p>The virtual slide(s) for this article can be found here: <url>http://www.diagnosticpathology.diagnomx.eu/vs/4123422336534537</url></p

Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium

<p>Abstract</p> <p>Background</p> <p>Digital whole-slide scanning of tissue specimens produces large images demanding increasing storing capacity. To reduce the need of extensive data storage systems image files can be compressed and scaled down. The aim of this article is to study the effect of different levels of image compression and scaling on automated image analysis of immunohistochemical (IHC) stainings and automated tumor segmentation.</p> <p>Methods</p> <p>Two tissue microarray (TMA) slides containing 800 samples of breast cancer tissue immunostained against Ki-67 protein and two TMA slides containing 144 samples of colorectal cancer immunostained against EGFR were digitized with a whole-slide scanner. The TMA images were JPEG2000 wavelet compressed with four compression ratios: lossless, and 1:12, 1:25 and 1:50 lossy compression. Each of the compressed breast cancer images was furthermore scaled down either to 1:1, 1:2, 1:4, 1:8, 1:16, 1:32, 1:64 or 1:128. Breast cancer images were analyzed using an algorithm that quantitates the extent of staining in Ki-67 immunostained images, and EGFR immunostained colorectal cancer images were analyzed with an automated tumor segmentation algorithm. The automated tools were validated by comparing the results from losslessly compressed and non-scaled images with results from conventional visual assessments. Percentage agreement and kappa statistics were calculated between results from compressed and scaled images and results from lossless and non-scaled images.</p> <p>Results</p> <p>Both of the studied image analysis methods showed good agreement between visual and automated results. In the automated IHC quantification, an agreement of over 98% and a kappa value of over 0.96 was observed between losslessly compressed and non-scaled images and combined compression ratios up to 1:50 and scaling down to 1:8. In automated tumor segmentation, an agreement of over 97% and a kappa value of over 0.93 was observed between losslessly compressed images and compression ratios up to 1:25.</p> <p>Conclusions</p> <p>The results of this study suggest that images stored for assessment of the extent of immunohistochemical staining can be compressed and scaled significantly, and images of tumors to be segmented can be compressed without compromising computer-assisted analysis results using studied methods.</p> <p>Virtual slides</p> <p>The virtual slide(s) for this article can be found here: <url>http://www.diagnosticpathology.diagnomx.eu/vs/2442925476534995</url></p

Development and evaluation of a virtual microscopy application for automated assessment of Ki-67 expression in breast cancer

<p>Abstract</p> <p>Background</p> <p>The aim of the study was to develop a virtual microscopy enabled method for assessment of Ki-67 expression and to study the prognostic value of the automated analysis in a comprehensive series of patients with breast cancer.</p> <p>Methods</p> <p>Using a previously reported virtual microscopy platform and an open source image processing tool, ImageJ, a method for assessment of immunohistochemically (IHC) stained area and intensity was created. A tissue microarray (TMA) series of breast cancer specimens from 1931 patients was immunostained for Ki-67, digitized with a whole slide scanner and uploaded to an image web server. The extent of Ki-67 staining in the tumour specimens was assessed both visually and with the image analysis algorithm. The prognostic value of the computer vision assessment of Ki-67 was evaluated by comparison of distant disease-free survival in patients with low, moderate or high expression of the protein.</p> <p>Results</p> <p>1648 evaluable image files from 1334 patients were analysed in less than two hours. Visual and automated Ki-67 extent of staining assessments showed a percentage agreement of 87% and weighted kappa value of 0.57. The hazard ratio for distant recurrence for patients with a computer determined moderate Ki-67 extent of staining was 1.77 (95% CI 1.31-2.37) and for high extent 2.34 (95% CI 1.76-3.10), compared to patients with a low extent. In multivariate survival analyses, automated assessment of Ki-67 extent of staining was retained as a significant prognostic factor.</p> <p>Conclusions</p> <p>Running high-throughput automated IHC algorithms on a virtual microscopy platform is feasible. Comparison of visual and automated assessments of Ki-67 expression shows moderate agreement. In multivariate survival analysis, the automated assessment of Ki-67 extent of staining is a significant and independent predictor of outcome in breast cancer.</p

Long-Term Follow-Up After Cervical Laminectomy without Fusion for Cervical Spondylotic Myelopathy

OBJECTIVE: The objectives were to study the effect of cervical laminectomy without fusion on the incidence of further cervical surgeries, the risk for cervical misalign-ment, and current functional status. -METHODS: We retrospectively analyzed the clinical data of 340 patients who had undergone simple lam-inectomy for cervical spondylotic myelopathy (CSM) at Helsinki University Hospital between 2000 and 2011.RESULTS: Forty-one patients (12.1%) had later undergone another cervical surgery during the follow-up of a mean of 8.5 years (maximum, 17.5 years). The most common indi-cation for further surgery was residual stenosis at adjacent or other cervical levels (34%). Five patients (1%) required further surgery for correction of a sagittal balance problem. The mean Neck Disability Index was 28% at a median of 9.0 years after laminectomy. The mean EQ-5D (EuroQol 5 Dimension 3 Level) index score was 58.8 for patients and 77.2 for age-matched and gender-matched general popu-lation controls (P [ 0.000), indicating patients' reduced health-related quality of life. Worse preoperative condition in the Nurick score was related to a lower (i.e., worse) EQ-5D score. In an additional arm of the study with radio-graphic imaging (40 patients), the mean change in sagittal alignment was 4.0 degrees toward lordotic, and a newly developed kyphosis was found in 7.5% of patients.CONCLUSIONS: Because CSM is a serious degenerative progressive condition resulting in decreased health -related quality of life even after surgical treatment, the low rate of corrective surgery needed for alignment issues per se indicates that simple laminectomy can be a viable treatment option in treating multilevel CSM.Peer reviewe