We present an LED light source for use with standard clinical endoscopes to enable visualization of tissues labeled with quantum dots (QDs). QD-assisted endoscopy may improve the outcome of surgical endoscopic procedures by identifying specific tissue types. QDs offer several advantages over current fluorescent stains due to their high target selectivity, long-lasting fluorescence, large excitation and narrow emission bands, and multiplexing capabilities. The prototype presented is compact, modular in design, and was built at low cost making it competitive with commercially available light sources. The device's efficiency is evaluated by measuring light intensity at discreet locations and by successfully illuminating a chicken tissue sample non-specifically labeled with a 250nM or 500nM QD solution. Ultimately, this device serves as a step towards incorporating QDs into real time, image-guided surgical procedures.

We propose a web interface that allows researchers to quantify and analyze microtubule confocal images online. Most analyses of microtubule confocal images are performed manually using very simple software or tools. Analysis results are stored locally within each collaborator with different styles and formats. This has limited the sharing of data and results when collaborating among different research parties. A web interface provides a simple way for users to process data online. It also allows easy sharing of both data and results among different participating groups. Analysis workflow of the interface is made similar to existing manual protocols. We demonstrate the integration of image processing algorithm in the current workflow to aid the analysis. Our design also allows integration of novel automated analysis algorithms and modules to re-evaluate existing data. This interface can provide a validation platform for new automated algorithm and allow collaboration on microtubule image analysis from different locations.

Microtubule (MT) dynamics quantification includes modeling of elongation, rapid shortening, and pauses. It indicates the effect of the cancer treatment drug paclitaxel because the drug causes MTs to bundle, which will in turn inhibit successful mitosis of cancerous cells. Thus, automatic MT dynamics analysis has been researched intensely because it allows for faster evaluation of potential cancer treatments and better understanding of drug effects on a cell. However, most current literatures still use manual initialization. In this work, we propose an automatic initialization algorithm that selects isolated and active tips for tracking. We use a Gaussian match filter to enhance the MT structures, and a novel technique called Pixel Nucleus Analysis (PNA) for isolated MT tip detection. To find dynamic tips, we applied a masked FFT in the temporal domain followed by K-means clustering. To evaluate the selected tips, we used a low level tip linking algorithm, and show the results of applying the algorithm to a model image and five MCF-7 breast cancer cell line images captured using fluorescent confocal microscopy. Finally, we compare tip selection criteria with existing automatic selection algorithms. We conclude that the proposed analysis is an effective technique based on three criteria which include outer region selection, separation, and MT dynamics.

During the analysis of microscopy images, researchers locate regions of interest (ROI) and extract relevant information within it. Identifying the ROI is mostly done manually and subjectively by pathologists. Computer algorithms could help in reducing their workload and improve reproducibility. In particular, we want to assess the validity of the folic acid receptor as a biomarker for head and neck cancer. We are only interested in folic acid receptors appearing in cancerous tissue. Therefore, the first step is to segment images into cancerous and noncancerous regions. We propose to use a spiral intensity profile for segmentation of light microscopy images. Many algorithms identify objects in an image by considering pixel intensity and spatial information separately. Our algorithm integrates intensity and spatial information by considering the change, or profile, of pixel intensity in a spiral fashion. Using a spiral intensity profile can also perform segmentation at different scales from cancer regions to nuclei cluster to individual nuclei. We compared our algorithm with manually segmented image and obtained a specificity of 83.7% and sensitivity of 61.1%. Spiral intensity profiles can be used as a feature to improve other segmentation algorithms. Segmentation of cancerous images at different scales allows effective quantification of folic acid receptor inside cancerous regions, nuclei clusters, or individual cells.

We present a generalized tool to mark and preprocess cancerous regions in an image. Currently, tissue biopsies are analyzed and graded manually by expert pathologists and thus can be time consuming and challenging due to variations in tissue morphology, inconsistencies in preparation of tissue specimen and errors in the image acquisition process. Our tool is designed to automatically standardize the variations in different images due to changing illumination and experimental conditions. Segregating cancerous regions from non-cancerous areas is a mandatory step before extracting relevant information from cancer images such as the number and size of nuclei and subsequently using it for classification and quantitative analysis. We tested our tool for two completely different cancers: Head and Neck Cancer (HNC) and Renal Cell Carcinoma (RCC). The tool enables the user to successfully segment the cancerous areas for both types of cancers and our results match with the manual validation by a pathologist.

We propose a two dimensional model to simulate microtubule dynamics. Microtubules are polymers that are important in many cell functions including cell division. In particular, chemotherapy targets microtubule dynamics in order to slow cancer cell reproduction. Traditional stochastic or chemical models for microtubule dynamics are one-dimensional, focusing on one variable such as length or concentration. We combine a traditional microtubule instability model and a chemical model and propose a two dimensional space for these models to interact. This gives a more realistic simulation of microtubule dynamics as it allows interaction of different microtubules. It can also simulate microtubule movement under different conditions. Our approach quantifies microtubule images and models microtubule dynamics within a synthesis-analysis framework.

Colorectal cancer, the second leading cause of cancer deaths in the United States, is a disease for which there are no known biomarkers of risk that can be used for predicting and preventing the disease. Based on new knowledge of the molecular basis of colorectal cancer, we developed and validated a panel of biomarkers of risk that can be measured in rectal biopsies. The goal of this work is to develop an integrated detection and image analysis quantification system for measuring and applying these biomarkers in clinical research and care. More importantly, the new system can process biopsy images from both traditional and bionanotechnology quantum dot-based IHC, and through a combination of novel and automated image analysis and quantification algorithms, it will significantly reduce processing time by detecting multiple biomarkers simultaneously on the same histologic sections. Clinical application of this novel process of detecting and quantifying biomarkers, coupled with decision support from the analysis of a biomarker quantification database, is expected to open new frontiers in the field of colorectal cancer prognosis and treatment.

A new method is proposed for tracking the dynamics of microtubules. It combines a salient point extraction mechanism for segmenting plus-end tips, a robust tracking method capable of locating the trajectories of a large number of feature points, and a classification algorithm capable of determining if the level of activity of a given microtubule video is typical of that of a treated or a control cell. Our method does not rely on the precise tracking of a single microtubule like many previous works, but instead focuses on the generalized movement of ending tips as a whole, which gives a more statistically reliable interpretation of the movement of microtubules. The proposed algorithm is tested using twenty videos of breast cancer microtubules - ten are treated with Taxol and ten are control. We are able to correctly classify those test videos 85% of the time, which is comparable in accuracy, but uses a less complex algorithm than other algorithms.

We propose to use particle filter [1], along with active contour [2] to track and model the plus-end tips of microtubules in confocal microscopy. Microtubules are polymers that change between states of growth, shortening, and pause. These events are critical to many cellular functions and are targets for successful cancer chemotherapy agents like Taxol. However, analyses are performed manually by researchers in most cases. Hence there is a need for a rapid and efficient quantification algorithm. In this paper, we propose to uses particle filter to track microtubule dynamics. While there are other algorithms that track microtubule movements, none of them uses inter-frame information. In our system, we use an open active contour to segment individual microtubule in each frame. Particle filter is used to track microtubule movements using information from previous frame. A simple motion and observation model is used to model the motion of microtubule movement. We show some of the results using MCF-7 breast cancer cell lines captured using fluorescent confocal microscopy and conclude that adding particle filter improves the accuracy of the system.

Bioconjugated quantum dots (QDs) provide a new class of biological labels for evaluating biomolecular signatures (biomarkers) on intact cells and tissue specimens. In particular, the use of multicolor QD probes in immunohistochemistry is considered one of the most important and clinically relevant applications. At present, however, clinical applications of QD-based immunohistochemistry have achieved only limited success. A major bottleneck is the lack of robust protocols to define the key parameters and steps. Here, we describe our recent experience, preliminary results and detailed protocols for QD-antibody conjugation, tissue specimen preparation, multicolor QD staining, image processing and biomarker quantification. The results demonstrate that bioconjugated QDs can be used for multiplexed profiling of molecular biomarkers, and ultimately for correlation with disease progression and response to therapy. In general, QD bioconjugation is completed within 1 day, and multiplexed molecular profiling takes 1-3 days depending on the number of biomarkers and QD probes used.

Microtubules are dynamic polymers that rapidly transition between states of growth, shortening, and pause. These dynamic events are critical for studying cellular processes such as the cancer drug effectiveness study. Typically, these events are quantified by imaging microtubule movements over time, which results in large data sets that require rigorous quantitative analysis. In most cases, the analysis was performed manually by the researcher. This process is tedious and prone to error and becomes a bottleneck in modern cancer research. Thus, an efficient, reliable, and rapid quantification method is in critical need. In this paper, we describe open contour-based tracking methods to automatically segment and track microtubule movements. We redefine the internal energy terms specifically for open snake, and examine different external energy terms for locating the end points of a microtubule. This algorithm has been validated using simulated images, untreated MCF-7 breast cancer cell lines, and cells treated with the microtubule-targeting chemotherapeutic agent, Taxol.

Microtubules (MT) are dynamic polymers that rapidly transition between states of growth, shortening, and pause. These dynamic events are critical for many microtubule functions such as intracellular trafficking and signaling. In addition, cancer chemotherapy drugs that target microtubules, such as the taxanes and the vinca alkaloids, are known to suppress microtubule dynamics at low doses, leading to mitotic arrest and cell death. Quantification of microtubule dynamics can be used as a read-out of anticancer-drug activity and can be a surrogate marker of drug sensitivity/resistance. The emerging nanotechnology such as quantum dots has provided properties such as less photo bleaching, higher probe imaging intensity, better specificity and sensitivity, which finally makes visualizing subcellular events over long enough time a possibility. But it also results in big increase in data acquisition. The traditional way of annotating MT manually is becoming a daunting task. Thus, the goal is to research and develop an efficient, reliable, and rapid MT tracking. In this paper, we describe active contour-based tracking methods to automatically track MT. We redefine the internal energy terms specifically for open snake, and examine different external energy terms for locating the end tips of a microtubule. This algorithm has been validated using simulated images, images of untreated MCF-7 breast cancer cells, and image of cells treated with the microtubule-targeting chemotherapeutic agent, Taxol

Colorectal cancer, the second leading cause of cancer deaths in the United States, is a molecular disease that is largely lifestyle determined and preventable. While heart disease has been sharply declining, in large part from widespread use of biological measurements that indicate risk ("biomarkers of risk"), such as blood cholesterol, to motivate and guide preventive treatment, colorectal cancer is a disease for which mortality rates have changed little and for which there have been no biomarkers of risk. Based on new knowledge about the molecular basis of colorectal cancer we developed and validated a panel of treatable biomarkers of risk that can be measured in rectal biopsies using automated immunohistochemistry and semi-automated image analysis. The methodology is now being made practical for clinical application through the use of 1) quantum dots, so that all of the biomarkers can be detected simultaneously on the same histologic sections (i.e., multiplexed), and 2) novel, automated image analysis algorithms to measure the quantities and tissue distributions of the biomarkers. Herein we summarize our methods, results, current directions, and progress

Microtubules are dynamic polymers that rapidly transition between states of growth, shortening, and pause. These dynamic events are critical for studying cellular processes such as the cancer drug effectiveness study. Typically, these events are quantified by imaging microtubule movements over time, which results in large data sets that require rigorous quantitative analysis. In most cases, the analysis was performed manually by the researcher. This process is tedious and prone to error and becomes a bottleneck in modern cancer research. Thus, an efficient, reliable, and rapid quantification method is in critical need. In this paper, we describe open contour-based tracking methods to automatically segment and track microtubule movements. We redefine the internal energy terms specifically for open snake, and examine different external energy terms for locating the end points of a microtubule. This algorithm has been validated using simulated images, untreated MCF-7 breast cancer cell lines, and cells treated with the microtubule-targeting chemotherapeutic agent, Taxol.

Microtubules are dynamic polymers that rapidly transition between states of growth, shortening, and pause. These dynamic events are critical for basic cellular processes, especially cell division. Typically, these events are quantified by imaging microtubule movements over time, which results in large data sets that require rigorous quantitative analysis. In most cases, these analyses are performed manually by the researcher. This process is both tedious and prone to error; thus an efficient and reliable computer-assisted quantification system would provide a rapid approach, suitable for high-throughput data analysis. In this paper, we describe methods to automatically segment and track microtubule movements. Our method is a snake based method [1]. Instead of a closed contour, we use an open contour to track individual microtubule. We redefine some of the internal energy terms specifically for open snake. A new external energy term for locating the end points of a microtubule is also defined. Testing is done using simulated images and untreated MCF-7 breast cancer cell lines as well as cells treated with the microtubule-targeting chemotherapeutic agent, Taxol.

We present a biomarker based computer assisted grading system for Squamous Cell Carcinoma of Head and Neck (SCCHN) immuno-histochemical (IHC) images. This system quantitatively evaluates biomarker expression in addition to cancerous and textural features. The stepwise grading approach includes: 1) image preprocessing to rectify variations due to illumination and acquisition conditions, 2) biomarker quantifi- cation using stain color references, 3) marking of cancerous and non-cancerous region of interest (ROI), and 4) grade evaluation based on the percentage of folate receptors (FR) expressed in the cancerous region along with nuclear and textural features. We analyze the grading efficacy of different feature types (biomarker, cancerous and textural) for computer assisted grading of SCCHN. Our methodology encompasses image based morphological tech- niques to provide new quantitative measures for typical cancer attributes (nuclear atypia, pleomorphism and necrosis) and allows usage of these attributes along with biomarker expression characteristics. We intend our system to assist pathologists in a clinical setting by correlating the FR expression to cancer grade. We obtain grading accuracy up to 94% for a heterogeneous image dataset and our results show that biomarker (FR) features result in considerable improvement in computer assisted grading of SCCHN as compared to cancerous and textural features.