Digital Image Analysis Algorithms Research Articles

Invasive lobular carcinoma of the breast: assessment of proliferative activity using automated Ki-67 immunostaining

Invasive lobular carcinoma (ILC) is almost always classified as Nottingham histological grade 2. Despite this, prognosis is markedly varied, with some ILCs behaving more akin to grade 3 invasive ductal carcinoma (IDC). Methods to separate these aggressive ILCs are needed. Digital image analysis (DIA) of the Ki-67 biomarker has potential in this regard; thus, we sought to determine the feasibility of its use for automated evaluation of ILC. An initial pilot study demonstrated no ILC specific changes were required to our Ki-67 DIA algorithm for reproducible results. Subsequently, 42 consecutive cases of ILC were evaluated by visual mitosis counting in H&E stained sections and by DIA on Ki-67 stained sections. Ki-67 proliferative index (PI) DIA showed significant correlation with visual mitosis counting on H&E stained sections (rs=0.63; p<0.05), significant strong correlation (rs=0.78; p<0.05) and substantial agreement (κ=0.62) with manual/visual Ki-67 assessment and significant positive associations with grade, nodal status and 'pleomorphic' ILC subtype, and a wide stratification of values in classical/grade 2 ILC. In conclusion, DIA of Ki-67 PI in ILC is feasible, correlates with mitotic index, manual/visual Ki-67 PI and clinico-pathological variables. The broad stratification of Ki-67 PI in classical/grade 2 ILC supports its practicability as a biomarker with prognostic and predictive potential, although large studies with outcome data are required for validation.

Automated Estimation of Acute Infarct Volume from Noncontrast Head CT Using Image Intensity Inhomogeneity Correction.

Identification of early ischemic changes (EIC) on noncontrast head CT scans performed within the first few hours of stroke onset may have important implications for subsequent treatment, though early stroke is poorly delimited on these studies. Lack of sharp lesion boundary delineation in early infarcts precludes manual volume measures, as well as measures using edge-detection or region-filling algorithms. We wished to test a hypothesis that image intensity inhomogeneity correction may provide a sensitive method for identifying the subtle regional hypodensity which is characteristic of early ischemic infarcts. A digital image analysis algorithm was developed using image intensity inhomogeneity correction (IIC) and intensity thresholding. Two different IIC algorithms (FSL and ITK) were compared. The method was evaluated using simulated infarcts and clinical cases. For synthetic infarcts, measured infarct volumes demonstrated strong correlation to the true lesion volume (for 20% decreased density “infarcts,” Pearson r = 0.998 for both algorithms); both algorithms demonstrated improved accuracy with increasing lesion size and decreasing lesion density. In clinical cases (41 acute infarcts in 30 patients), calculated infarct volumes using FSL IIC correlated with the ASPECTS scores (Pearson r = 0.680) and the admission NIHSS (Pearson r = 0.544). Calculated infarct volumes were highly correlated with the clinical decision to treat with IV-tPA. Image intensity inhomogeneity correction, when applied to noncontrast head CT, provides a tool for image analysis to aid in detection of EIC, as well as to evaluate and guide improvements in scan quality for optimal detection of EIC.

Abstract 463: Integrated digital pathology and transcriptome analysis identifies molecular mediators of T cell exclusion in ovarian cancer

Abstract Background: Close proximity between cytotoxic T lymphocytes and tumor cells is required for effective immunotherapy. Three tumor-immune (TI) phenotypes, infiltrated, excluded and desert, have been previously described based on the infiltration patterns of CD8+ T cells. However, no quantitative methods exist to define these phenotypes robustly in human solid tumors. Importantly, the molecular features and mechanisms determining these phenotypes are not well understood. Here we report a novel integrated approach to classify and functionally dissect TI phenotypes in human ovarian cancer. Methods: CD8 IHC and RNAseq analysis were performed on 370 ovarian tumors from the ICON7 phase III clinical trial, a front-line trial testing the addition of bevacizumab to chemotherapies. A digital image analysis algorithm was developed to quantify the quantity and spatial distribution of CD8+ T cells. Coupling digital pathology with transcriptome analysis, a random forest machine learning algorithm was applied to identify genes associated with these two metrics using a training set (n=155). A gene expression-based classifier was developed for classifying TI phenotypes and validated using testing sets from ICON7 trial and a vendor collection. Functional characterization of key mediators promoting T cell exclusion were carried out by integrating in situ, in vitro and ex vivo analyses on ovarian tumor tissues, cancer associated fibroblasts (CAFs) and ovarian cancer cell lines. Anti-tumor activity of TGFβ blockade in combination with anti-PD-L1 was evaluated in the mouse BrKras ovarian cancer model in FVB background. Results: Integrating digital pathology and machine learning on large ovarian tumor cohorts, we developed and validated a 157-gene molecular classifier. We show the TI phenotypes are of biological and clinical importance in ovarian cancer. Two hallmarks of T cell exclusion were identified: 1) loss of MHC I on tumor cells and 2) upregulation of TGFβ/stromal activities. We show that MHC I in ovarian cancer cells is likely regulated by epigenetic mechanisms and TGFβ is a key mediator of T cell exclusion. TGFβ reduced MHC I expression in ovarian cancer cells and induced extracellular matrix and immunosuppressive molecules in human primary fibroblasts. Finally, we demonstrated that combining anti-TGFβ and anti-PD-L1 in the BrKras mouse model improved the anti-tumor efficacy and survival. Conclusion: This study provided the first systematic and in-depth characterization of the molecular features and mechanisms underlying the tumor-immune phenotypes in human ovarian cancer. We illuminated a multi-faceted role of TGFβ in mediating crosstalk between tumor cells and CAFs to shape the tumor-immune contexture. Our findings support that targeting the TGFβ pathway represents a promising therapeutic strategy to overcome T cell exclusion and optimize response to cancer immunotherapy. Citation Format: Melanie Desbois, Akshata Udyavar, Lisa Ryner, Cleopatra Kozlowski, Yinghui Guan, Milena Dürrbaum, Shan Lu, Jean-Philippe Fortin, Hartmut Koeppen, James Ziai, Ching-Wei Chang, Amy Lo, Shilpa Keerthivasan, Marie Plante, Richard Bourgon, Carlos Bais, Priti Hegde, Anneleen Daemen, Shannon Turley, Yulei Wang. Integrated digital pathology and transcriptome analysis identifies molecular mediators of T cell exclusion in ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 463.

Digital image analysis of pan-cytokeratin stained tumor slides for evaluation of tumor budding in pT1/pT2 colorectal cancer: Results of a feasibility study

Tumor budding is an independent prognostic factor in colorectal cancer. However, varying degrees of interobserver agreement and reproducibility challenges the use of tumor budding in diagnostics. Immunohistochemical staining of tumor slides with pan-cytokeratin visualizes the budding tumor cells and has been suggested to improve reproducibility. Here we demonstrate the methodology of tumor budding assessment using digital image analysis based on tumor slides stained for pan-cytokeratin, and investigate interobserver agreement, agreement between manual and digital assessment methods and digital reproducibility between users.Tumor slides from 126 patients with pT1/pT2 colorectal cancer were stained with pan-cytokeratin and tumor budding at the invasive tumor front was assessed by conventional manual microscopy. A digital image analysis algorithm for identification and quantification of budding tumor cells was developed and tested on the pan-cytokeratin stained slides.Manual assessment of tumor budding using pan-cytokeratin stained tumor slides exhibited high correlations (Spearman Rank 0.84-0.89, p < 0.001),excellent agreement between observers (Intra-class correlation coefficient (ICC): 0.86 -0.87) and 2.20 higher odds for regional metastases with increasing budding counts (p = 0.017). Digital image analysis correlated well to manual assessment (Spearman Rank 0.71-0.88) and agreement between the two methods was good (ICC 0.62-0.82). However, only a trend towards increased odds for metastatic progression was found for the adjusted digital estimates (p = 0.076). Digital estimates were higher than manual estimates, demonstrated by a systematic median difference of 3–4.5 buds. Image analysis was highly reproducible between users of the algorithm (ICC 0.98). In conclusion, assessment of tumor budding using pan-cytokeratin stained tumor slides is a method with high correlation and agreement between observers. Digital image analysis quantifies budding tumor cells in high agreement with manual estimates, but approval of the digital slides by a pathologist is mandatory. The method qualifies for further investigation.

Programmed death-ligand 1 expression by digital image analysis advances thyroid cancer diagnosis among encapsulated follicular lesions

Recognition of noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) that distinguishes them from invasive malignant encapsulated follicular variant of papillary thyroid carcinoma (EFVPTC) can prevent overtreatment of NIFTP patients. We and others have previously reported that programmed death-ligand 1 (PD-L1) is a useful biomarker in thyroid tumors; however, all reports to date have relied on manual scoring that is time consuming as well as subject to individual bias. Consequently, we developed a digital image analysis (DIA) protocol for cytoplasmic and membranous stain quantitation (ThyApp) and evaluated three tumor sampling methods [Systemic Uniform Random Sampling, hotspot nucleus, and hotspot nucleus/3,3′-Diaminobenzidine (DAB)]. A patient cohort of 153 cases consisting of 48 NIFTP, 44 EFVPTC, 26 benign nodules and 35 encapsulated follicular lesions/neoplasms with lymphocytic thyroiditis (LT) was studied. ThyApp quantitation of PD-L1 expression revealed a significant difference between invasive EFVPTC and NIFTP; but none between NIFTP and benign nodules. ThyApp integrated with hotspot nucleus tumor sampling method demonstrated to be most clinically relevant, consumed least processing time, and eliminated interobserver variance. In conclusion, the fully automatic DIA algorithm developed using a histomorphological approach objectively quantitated PD-L1 expression in encapsulated thyroid neoplasms and outperformed manual scoring in reproducibility and higher efficiency.

Abstract P5-03-08: Development of a histology-based digital pathology image analysis algorithm for assessment of tumor infiltrating lymphocytes in HER2+ breast cancer

Abstract Introduction: Anthracycline-based chemotherapy regimens have been shown to increase risk of cardiac toxicity and other side effects especially in combination with HER2-targeting agents such as trastuzumab. Identification of biomarkers that can predict similar patient benefit in the context of targeted therapy between anthracycline and non-anthracycline-based regimens is attractive for personalized care. Histology-based assessment of tumor infiltrating lymphocytes (TILs) as a surrogate of the host immune response has been shown to be prognostic and potentially chemopredictive in triple-negative and HER2-positive breast cancers; however, the inter-play of TILs, tumor cells, other microenvironment mediators, their spatial relationships, quantity, and other image-based features have yet to be determined exhaustively and systemically. In anticipation of analyzing these aspects in the context of chemo and targeted therapy response in patient sample cohorts, we developed a digital pathology image analysis algorithm to identify tumor, stromal, and lymphocyte cells and acquire respective histology-based image features from hematoxylin and eosin (H&amp;E) stained slides. Materials and Methods: An automated method involving cell detection, cell segmentation, feature extraction (capturing both local features and global context based features) and supervised machine learning (using a multi-class random forest based classifier, where a 3-class problem is represented using 3 1-vs-1 binary classifiers) were used to classify individual cells into the following 3 categories: tumor cells, stromal cells, and lymphocytes. Cell classification was compared against manually determined ground truth from three pathologists using simple confusion matrices. Results: From six H&amp;E breast cancer cases, two pathologists manually and independently annotated the same tumor cells (6,458), lymphocytes (2,491), and stromal cells (744) in fourteen field-of-views (˜ 0.3 mm2 in size). Manual concordance of tumor cells (99.4%, 1434/1442), lymphocytes (80.0%, 680/849), and stromal cells (68.8%, 53/77) between two pathologists was moderate to high. Comparing only cells where two pathologists agreed (4,736) and an independent set of single cell annotations (547) from a third pathologist, image analysis classification showed high concordance for tumor cell (92.9%, 1107/1191), lymphocyte (90.4%, 572/636), and stromal cell (94.3%, 66/70)classification categories. Approximately 242 image features grouped into 22 unique data families were extracted from each cell analyzed. Conclusion: A H&amp;E-derived TILs image analysis algorithm with associated feature extraction is feasible with preliminary findings of accurate cell classification. This tool will continue to be refined in anticipation of analysis in patient outcome cohorts. Citation Format: Barnes M, Sarkar A, Redman R, Bechert C, Srinivas C. Development of a histology-based digital pathology image analysis algorithm for assessment of tumor infiltrating lymphocytes in HER2+ breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P5-03-08.

Abstract P2-03-07: Digital image analysis of Ki67 in hot spots is superior to alternative proliferation associated markers in breast cancer

Abstract INTRODUCTION Proliferative activity is one of the most important prognostic parameters in cancer. During the pathological examination of breast tumors, it is routinely evaluated by a count of the number of mitoses. Adding immunohistochemical stains of the nuclear protein Ki67 provides extra prognostic and predictive information. However, the currently used methods for both of these evaluations battle imperfections, primarily in reproducibility. In this study, we make an equally broad and detailed evaluation of mitoses, Ki67 and the more recently described Phosphohistone H3 (PHH3) in primary breast cancer using digital image analysis (DIA). Furthermore, we aim to investigate the prognostic and predictive value of proliferation-associated biomarkers in breast cancer stromal cells in relation to patient outcome. MATERIALS AND METHODS Two cohorts of primary breast cancer specimens (total n=297) with clinicopathological data including &amp;gt;10 years survival data, were sectioned and stained for Ki67, PHH3 and pancytokeratin (CKMNF116) and all glass slides were digitally scanned at x20. The DIA software used was the Visiopharm Integrator System (VIS) by Visiopharm A/S, Hoersholm, Denmark. VIS operates by a 'digital fusion' method that automatically excludes non-epithelial tissue restricting the analysis of the biomarkers (Ki67 and PHH3) to CKMNF116 positive cells. Both manual and DIA scores were compared for sensitivity and specificity for the gene expression based Luminal B versus A subtype, for high versus low transcriptomic grade as well as for their prognostic value in terms of Cox regression hazard ratios and breast cancer specific and overall survival. Further, we investigated whether the expression of Ki67 in the tumors' hot spots, invasive edges or as an average across all regions should be assessed for maximum power in relation to these outcomes. In addition, by inverting the DIA algorithm run by the VIS on the same cohorts, the expression of Ki67 and PHH3 was evaluated in the tumor stromal compartment. RESULTS Regardless of tumor region, DIA of Ki67 outperformed the other markers in sensitivity and specificity for gene expression subtypes and transcriptomic grade. In contrast to mitotic counts, tumors with high expression of Ki67 as defined by DIA, had significantly increased hazard ratio for all-cause mortality within 10 years from diagnosis. DIA of Ki67 was superior to manual Ki67 and PHH3 evaluations as well as to mitotic counts in terms of separation of patients with poor versus relatively good survival. Finally, we replaced the manual mitotic counts with DIA of Ki67 in hot spots as the marker for proliferation when determining histological grade. This increased the differences in estimated mean overall survival between the highest and lowest grades and added significantly more prognostic information to the classic Nottingham histological grade. CONCLUSIONS We conclude that digital image analysis of Ki67 in hot spots should be suggested as the marker of choice for proliferative activity in breast cancer. Citation Format: Robertson S, Stålhammar G, Wedlund L, Gholizadeh S, Lippert M, Rantaleinen M, Bergh J, Hartman J. Digital image analysis of Ki67 in hot spots is superior to alternative proliferation associated markers in breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P2-03-07.

Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium

ABSTRACTBackground: Microscopy remains the gold standard in the diagnosis of neglected tropical diseases. As resource limited, rural areas often lack laboratory equipment and trained personnel, new diagnostic techniques are needed. Low-cost, point-of-care imaging devices show potential in the diagnosis of these diseases. Novel, digital image analysis algorithms can be utilized to automate sample analysis.Objective: Evaluation of the imaging performance of a miniature digital microscopy scanner for the diagnosis of soil-transmitted helminths and Schistosoma haematobium, and training of a deep learning-based image analysis algorithm for automated detection of soil-transmitted helminths in the captured images.Methods: A total of 13 iodine-stained stool samples containing Ascaris lumbricoides, Trichuris trichiura and hookworm eggs and 4 urine samples containing Schistosoma haematobium were digitized using a reference whole slide-scanner and the mobile microscopy scanner. Parasites in the images were identified by visual examination and by analysis with a deep learning-based image analysis algorithm in the stool samples. Results were compared between the digital and visual analysis of the images showing helminth eggs.Results: Parasite identification by visual analysis of digital slides captured with the mobile microscope was feasible for all analyzed parasites. Although the spatial resolution of the reference slide-scanner is higher, the resolution of the mobile microscope is sufficient for reliable identification and classification of all parasites studied. Digital image analysis of stool sample images captured with the mobile microscope showed high sensitivity for detection of all helminths studied (range of sensitivity = 83.3–100%) in the test set (n = 217) of manually labeled helminth eggs.Conclusions: In this proof-of-concept study, the imaging performance of a mobile, digital microscope was sufficient for visual detection of soil-transmitted helminths and Schistosoma haematobium. Furthermore, we show that deep learning-based image analysis can be utilized for the automated detection and classification of helminths in the captured images.

An optimized image analysis algorithm for detecting nuclear signals in digital whole slides for histopathology.

Nuclear estrogen receptor (ER), progesterone receptor (PR) and Ki-67 protein positive tumor cell fractions are semiquantitatively assessed in breast cancer for prognostic and predictive purposes. These biomarkers are usually revealed using immunoperoxidase methods resulting in diverse signal intensity and frequent inhomogeneity in tumor cell nuclei, which are routinely scored and interpreted by a pathologist during conventional light-microscopic examination. In the last decade digital pathology-based whole slide scanning and image analysis algorithms have shown tremendous development to support pathologists in this diagnostic process, which can directly influence patient selection for targeted- and chemotherapy. We have developed an image analysis algorithm optimized for whole slide quantification of nuclear immunostaining signals of ER, PR, and Ki-67 proteins in breast cancers. In this study, we tested the consistency and reliability of this system both in a series of brightfield and DAPI stained fluorescent samples. Our method allows the separation of overlapping cells and signals, reliable detection of vesicular nuclei and background compensation, especially in FISH stained slides. Detection accuracy and the processing speeds were validated in routinely immunostained breast cancer samples of varying reaction intensities and image qualities. Our technique supported automated nuclear signal detection with excellent efficacy: Precision Rate/Positive Predictive Value was 90.23 ± 4.29%, while Recall Rate/Sensitivity was 88.23 ± 4.84%. These factors and average counting speed of our algorithm were compared with two other open source applications (QuPath and CellProfiler) and resulted in 6-7% higher Recall Rate, while 4- to 30-fold higher processing speed. In conclusion, our image analysis algorithm can reliably detect and count nuclear signals in digital whole slides or any selected large areas i.e. hot spots, thus can support pathologists in assessing clinically important nuclear biomarkers with less intra- and interlaboratory bias inherent of empirical scoring. © 2017 International Society for Advancement of Cytometry.

Measurements of reference ISO nozzles by high-speed imaging

Agricultural spray characteristics determine the efficiency of a pesticide application because size and velocity affect droplet trajectory and impact behavior. At present, the relevance of different characterization techniques remains controversial since discrepancies may be significant between measurements performed in different laboratories.A digital image acquisition technique and analysis algorithm is proposed for droplet size and velocimetry measurements as an alternative to well-established techniques such as the Phase Doppler Particle Analyzer (PDPA) or laser diffraction spectrometry (LDS). The algorithm requires double exposed shadow images acquired in a back-lighted arrangement with a Particle Image Velocimetry (PIV) camera and a pulsed light emitting diode (LED). Spatial illumination heterogeneities are corrected by subtracting from each image a mean background acquired on several images without any particle. The algorithm accuracy is ensured by the rejection of out-of-focus particles using a focus parameter depending on gradient intensity at the particle edges. Thresholds for focus particle selection were determined by studying the evolution of the focus parameter and the error on particle size measurements from images containing droplets with uniform size at various distance of the object plane. Selected droplets were identified on both pairs of images to determine their size and velocity. Droplet size distributions were corrected to account for the uneven sampling probability caused by the volumetric method.Droplet size distributions of a set of reference nozzle/pressure combinations defined in the ISO/DIS 25358 were measured. The image technique was able to distinguish each of the reference sprays well. Comparison with PDPA measurements showed that the imaging technique tends to measure an equivalent Dv50, a lower Dv10 and a higher Dv90 leading therefore to a higher relative span factor. Velocity measurements showed good agreement between both techniques except for one nozzle/pressure combination.

3D carotid reconstructions: imaging, pathology, algorithms and pipelines

Background: Whole-slide scanning of tissue sections spatially informed by imaging studies offers the opportunity to reconstruct specimens for co-registration to 3D imaging data. Digital image analysis algorithms can be designed to analyze and reconstruct such specimens via electronic “pipelines”. Methods: A goal of the Canadian Atherosclerosis Imaging Network (CAIN) is to improve the assessment of carotid atheromatous disease through studies that inform clinical imaging with gold-standard data (plaque pathology). To achieve this, sectioned atheromas are manually annotated and analyzed by electronic algorithm for pathological features of interest. Resulting images are then reassembled in 3D for registration to ultrasound, CT, PET-CT and MRI studies. Results: Carotid endarterectomy specimens were sub-serially sectioned, stained, digitized and annotated manually and by electronic algorithms. Resulting 2D images were successfully rendered, reassembled and analyzed in 3D using ex-vivo micro-CT as a spatial reference. Furthermore, histology quantification using colour deconvolution was found to be preferred over hue-saturation-intensity methods 94.7-100% of the time in a blinded multiple rater study. Conclusion: Automated “pipelines” greatly facilitate 3D reconstruction in comparison to traditional slice-by-slice methods. Transformations spatially guided by pre-existing imaging data is not only faster, but has superior objectivity and fidelity. With embedded annotations, 3D pathology maps become a rich, micron-level, permanent digital pathological database for correlative studies.

IHC Profiler: an open source plugin for the quantitative evaluation and automated scoring of immunohistochemistry images of human tissue samples.

In anatomic pathology, immunohistochemistry (IHC) serves as a diagnostic and prognostic method for identification of disease markers in tissue samples that directly influences classification and grading the disease, influencing patient management. However, till today over most of the world, pathological analysis of tissue samples remained a time-consuming and subjective procedure, wherein the intensity of antibody staining is manually judged and thus scoring decision is directly influenced by visual bias. This instigated us to design a simple method of automated digital IHC image analysis algorithm for an unbiased, quantitative assessment of antibody staining intensity in tissue sections. As a first step, we adopted the spectral deconvolution method of DAB/hematoxylin color spectra by using optimized optical density vectors of the color deconvolution plugin for proper separation of the DAB color spectra. Then the DAB stained image is displayed in a new window wherein it undergoes pixel-by-pixel analysis, and displays the full profile along with its scoring decision. Based on the mathematical formula conceptualized, the algorithm is thoroughly tested by analyzing scores assigned to thousands (n = 1703) of DAB stained IHC images including sample images taken from human protein atlas web resource. The IHC Profiler plugin developed is compatible with the open resource digital image analysis software, ImageJ, which creates a pixel-by-pixel analysis profile of a digital IHC image and further assigns a score in a four tier system. A comparison study between manual pathological analysis and IHC Profiler resolved in a match of 88.6% (P<0.0001, CI = 95%). This new tool developed for clinical histopathological sample analysis can be adopted globally for scoring most protein targets where the marker protein expression is of cytoplasmic and/or nuclear type. We foresee that this method will minimize the problem of inter-observer variations across labs and further help in worldwide patient stratification potentially benefitting various multinational clinical trial initiatives.

Analysis of an Image-Based Method to Quantify the Size and Shape of Sand Particles

Sand response depends on particle morphology (size and shape). In geotechnical research and practice, size is typically assessed by sieve analysis and particle shapes are qualitatively described. Technological developments mean that digital images of sand particles can easily be obtained, enabling shape to be quantified. The complexity associated with many digital image analysis algorithms seems to have restricted their use to fundamental research studies. This study introduces a pragmatic approach for quantitative shape analysis that has the potential to be broadly adopted in geotechnical engineering research and practice. The approach generates three shape measures (convexity, sphericity, and aspect ratio) that can easily be calculated from digital images. Following an analysis of these shape measures and the imaging method used here, a database of 36 sands, including many of the sands commonly used in geotechnical research, is presented. The subjective nature of qualitative description is clear from the discrepancies that were found in published shape assessments of these sands. Convexity, sphericity, and aspect ratio data for each of the 36 sands are presented. The relevance of these parameters to geotechnical engineering is established by comparing them with widely used qualitative descriptions.

Abstract 42: Expression of interleukin-7 and its signalling intermediates in human neuroblastoma tumours.

Abstract Neuroblastoma (NB) is the most common extracranial childhood malignant solid tumour which shows remarkable biological heterogeneity. It has been speculated that schwann cells influence NB tumour growth, by secreting as yet to be identified ‘soluble factors’ that serve as anti-proliferative and differentiation signals for neuronal cells. Several gene expression studies indicate that interleukin 7 (IL7) has increased expression in NB specifically in tumours with a better prognosis, making IL7 a key candidate for this soluble factor. In this study, the expression of IL7, IL7 receptor (IL7R) and its downstream signalling proteins, including the Janus Kinases (JAK1 and JAK3), Signal Transducers and Activators of Transcription (STAT5) and Phosphorylated STAT5 (pSTAT5) were analysed using immunohistochemical stains in a cohort of 100 patients with stroma poor NB and 24 with stroma rich ganglioneuroma (GN). The expression of these proteins were correlated to the expression of the diagnostic markers S100 (stromal marker), NB84 (neuroblast marker) and CD99 (negative marker for NB). The immunohistochemical analysis showed that IL7 expression was strongly positive exclusively in schwannian stroma in both favourable and unfavourable histology whilst staining ganglionic cells as well. The levels of expression, as quantified by specialised digital image analysis algorithms (Aperio, USA), revealed IL7, IL7R, JAK1, STAT5 and pSTAT5 were all increased in GNs compared to NBs. There was no correlated change in expression levels of JAK3 in the tumours. Expression of pStat5 was found to be significantly reduced (t-test, p=0.00013) in stroma poor NB compared to GN. Our findings implicate IL7 within the schwannian stroma of the tumour architecture as having a paracrine signalling effect on neighbouring neuroblasts which may provide the anti-proliferative and differentiation signals postulated. Further investigation of the importance of IL7 in neuroblastoma will define the biological significance of the IL7-Stat5 signalling cascade and whether targeting the specific IL7 signalling pathway proteins will raise novel treatment strategies. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 42. doi:1538-7445.AM2012-42

Identification Of Food Grains And Its Quality Using Pattern Classification

The research work deals with an approach to perform texture and morphological based retrieval on a corpus of food grain images. The work has been carried out using Image Warping and Image analysis approach. The method has been employed to normalize food grain images and hence eliminating the effects of orientation using image warping technique with proper scaling. The images have been properly enhanced to reduce noise and blurring in image. Finally image has segmented applying proper segmentation methods so that edges may be detected effectively and thus rectification of the image has been done. The approach has been tested on sufficient number of food grain images of rice based on intensity, position and orientation. A digital image analysis algorithm based on color, morphological and textural features was developed to identify the six varieties rice seeds which are widely planted in Chhattisgarh region. Nine color and nine morphological and textural features were used for discriminant analysis. A back propagation neural network-based classifier was developed to identify the unknown grain types. The color and textural features were presented to the neural network for training purposes. The trained network was then used to identify the unknown grain types.

Membrane connectivity estimated by digital image analysis of HER2 immunohistochemistry is concordant with visual scoring and fluorescence in situ hybridization results: algorithm evaluation on breast cancer tissue microarrays

IntroductionThe human epidermal growth factor receptor 2 (HER2) is an established biomarker for management of patients with breast cancer. While conventional testing of HER2 protein expression is based on semi-quantitative visual scoring of the immunohistochemistry (IHC) result, efforts to reduce inter-observer variation and to produce continuous estimates of the IHC data are potentiated by digital image analysis technologies.MethodsHER2 IHC was performed on the tissue microarrays (TMAs) of 195 patients with an early ductal carcinoma of the breast. Digital images of the IHC slides were obtained by Aperio ScanScope GL Slide Scanner. Membrane connectivity algorithm (HER2-CONNECT™, Visiopharm) was used for digital image analysis (DA). A pathologist evaluated the images on the screen twice (visual evaluations: VE1 and VE2). HER2 fluorescence in situ hybridization (FISH) was performed on the corresponding sections of the TMAs. The agreement between the IHC HER2 scores, obtained by VE1, VE2, and DA was tested for individual TMA spots and patient's maximum TMA spot values (VE1max, VE2max, DAmax). The latter were compared with the FISH data. Correlation of the continuous variable of the membrane connectivity estimate with the FISH data was tested.ResultsThe pathologist intra-observer agreement (VE1 and VE2) on HER2 IHC score was almost perfect: kappa 0.91 (by spot) and 0.88 (by patient). The agreement between visual evaluation and digital image analysis was almost perfect at the spot level (kappa 0.86 and 0.87, with VE1 and VE2 respectively) and at the patient level (kappa 0.80 and 0.86, with VE1max and VE2max, respectively). The DA was more accurate than VE in detection of FISH-positive patients by recruiting 3 or 2 additional FISH-positive patients to the IHC score 2+ category from the IHC 0/1+ category by VE1max or VE2max, respectively. The DA continuous variable of the membrane connectivity correlated with the FISH data (HER2 and CEP17 copy numbers, and HER2/CEP17 ratio).ConclusionHER2 IHC digital image analysis based on membrane connectivity estimate was in almost perfect agreement with the visual evaluation of the pathologist and more accurate in detection of HER2 FISH-positive patients. Most immediate benefit of integrating the DA algorithm into the routine pathology HER2 testing may be obtained by alerting/reassuring pathologists of potentially misinterpreted IHC 0/1+ versus 2+ cases.

Wettability changes of TiO2 nanotube surfaces

This study examines the effect of environmental and experimental conditions, such astemperature and time, on the wettability properties of titania nanotube (TNT) surfacesfabricated by anodization. The fabricated TNTs are 60–130 nm inner diameter and 7–10 µm height. One-microliter water droplets were used to define the wettability of the TNTsurfaces by measuring the contact angles. A digital image analysis algorithm was developedto obtain contact angles, contact radii and center heights of the droplets on the TNTsurfaces. Bare titanium foil is inherently less hydrophilic with approximately60°–80° contact angle. The as-anodized TNT surfaces are more hydrophilic and annealing furtherincreases this hydrophilic property. Furthermore, it was found that the TNT surfacebecame more hydrophobic when aged in air over a period of three months. It is believedthat the surface wettability can be changed due to alkane contamination and organiccontaminants in an ambient atmosphere. This work can provide guidelines to better specifythe environmental conditions that changes surface properties of TNT surfaces and thereforeaffect their desirable function in specific applications such as orthopedic implants.

An experimental study of the effect of collision properties on spout fluidized bed dynamics

In this paper we experimentally study the effect of collision properties of different particle systems on the bed dynamics of a spout fluidized bed. This is done for different flow regimes: the spout-fluidization regime (case A), the jet-in-fluidized-bed regime (case B) and the spouting-with-aeration regime (case C). The considered particle systems comprise glass beads, γ-alumina oxide and zeolite 4A particles, which are all classified as Geldart D. A non-intrusive measurement technique is used, viz. particle image velocimetry (PIV) to measure the particle flow field in a pseudo two-dimensional (2D) spout fluidized bed. Additionally, digital images are analyzed using a newly developed digital image analysis (DIA) algorithm to evaluate the particle volume fraction. It is demonstrated that the new proposed DIA algorithm provides reliable information on the particle volume fraction distribution, showing that it is a powerful tool when combined with PIV. The added value of DIA is confirmed by comparing the particle velocity fields and volumetric particle fluxes. The effect of the collision properties for glass beads, γ-alumina oxide and zeolite 4A particles has been studied in three flow regimes, i.e. the intermediate/spout-fluidization regime (case A), the jet-in-fluidized-bed regime (case B) and the spouting-with-aeration regime (case C). For each flow regime, the particle volume fraction shows small differences between the different particle systems. For the γ-alumina oxide and zeolite 4A particles, the spout channel is less stable for the cases A and C. The particle fluxes display also small differences between the particle systems for each flow regime.

The evaporation and wetting dynamics of sessile water droplets on submicron-scale patterned silicon hydrophobic surfaces

The evaporation characteristics of 1 µl sessile water droplets on hydrophobic surfaces are experimentally examined. The proposed hydrophobic surfaces are composed of submicron diameter and 4.2 µm height silicon post arrays. A digital image analysis algorithm was developed to obtain time-dependent contact angles, contact diameters, and center heights for both non-patterned polydimethylsiloxane (PDMS) surfaces and patterned post array surfaces, which have the same hydrophobic contact angles. While the contact angles exhibit three distinct stages during evaporation in the non-patterned surface case, those in the patterned silicon post array surface case decrease linearly. In the case of post array hydrophobic surfaces, the initial contact diameter remains unchanged until the portion of the droplet above the posts completely dries out. The edge shrinking velocity of the droplet shows nonlinear characteristics, and the velocity magnitude increases rapidly near the last stage of evaporation.

137 Ki-67 as a long-term prognostic factor in lobular breast cancer

Invasive lobular carcinoma (ILC) is almost always classified as Nottingham histological grade 2. Despite this, prognosis is markedly varied, with some ILCs behaving more akin to grade 3 invasive ductal carcinoma (IDC). Methods to separate these aggressive ILCs are needed. Digital image analysis (DIA) of the Ki-67 biomarker has potential in this regard; thus, we sought to determine the feasibility of its use for automated evaluation of ILC. An initial pilot study demonstrated no ILC specific changes were required to our Ki-67 DIA algorithm for reproducible results. Subsequently, 42 consecutive cases of ILC were evaluated by visual mitosis counting in H&E stained sections and by DIA on Ki-67 stained sections. Ki-67 proliferative index (PI) DIA showed significant correlation with visual mitosis counting on H&E stained sections (rs=0.63; p<0.05), significant strong correlation (rs=0.78; p<0.05) and substantial agreement (κ=0.62) with manual/visual Ki-67 assessment and significant positive associations with grade, nodal status and ‘pleomorphic’ ILC subtype, and a wide stratification of values in classical/grade 2 ILC. In conclusion, DIA of Ki-67 PI in ILC is feasible, correlates with mitotic index, manual/visual Ki-67 PI and clinico-pathological variables. The broad stratification of Ki-67 PI in classical/grade 2 ILC supports its practicability as a biomarker with prognostic and predictive potential, although large studies with outcome data are required for validation.