Jenna L. Mueller

Optimization of a widefield structured illumination microscope for non-destructive assessment and quantification of nuclear features in tumor margins of a primary mouse model of sarcoma.

Cancer is associated with specific cellular morphological changes, such as increased nuclear size and crowding from rapidly proliferating cells. In situ tissue imaging using fluorescent stains may be useful for intraoperative detection of residual cancer in surgical tumor margins. We developed a widefield fluorescence structured illumination microscope (SIM) system with a single-shot FOV of 2.1×1.6 mm (3.4 mm2) and sub-cellular resolution (4.4 µm). The objectives of this work were to measure the relationship between illumination pattern frequency and optical sectioning strength and signal-to-noise ratio in turbid (i.e. thick) samples for selection of the optimum frequency, and to determine feasibility for detecting residual cancer on tumor resection margins, using a genetically engineered primary mouse model of sarcoma. The SIM system was tested in tissue mimicking solid phantoms with various scattering levels to determine impact of both turbidity and illumination frequency on two SIM metrics, optical section thickness and modulation depth. To demonstrate preclinical feasibility, ex vivo 50 µm frozen sections and fresh intact thick tissue samples excised from a primary mouse model of sarcoma were stained with acridine orange, which stains cell nuclei, skeletal muscle, and collagenous stroma. The cell nuclei were segmented using a high-pass filter algorithm, which allowed quantification of nuclear density. The results showed that the optimal illumination frequency was 31.7 µm−1 used in conjunction with a 4×0.1 NA objective ( = 0.165). This yielded an optical section thickness of 128 µm and an 8.9×contrast enhancement over uniform illumination. We successfully demonstrated the ability to resolve cell nuclei in situ achieved via SIM, which allowed segmentation of nuclei from heterogeneous tissues in the presence of considerable background fluorescence. Specifically, we demonstrate that optical sectioning of fresh intact thick tissues performed equivalently in regards to nuclear density quantification, to physical frozen sectioning and standard microscopy.

Quantitative Segmentation of Fluorescence Microscopy Images of Heterogeneous Tissue: Application to the Detection of Residual Disease in Tumor Margins

Purpose To develop a robust tool for quantitative in situ pathology that allows visualization of heterogeneous tissue morphology and segmentation and quantification of image features. Materials and Methods Tissue excised from a genetically engineered mouse model of sarcoma was imaged using a subcellular resolution microendoscope after topical application of a fluorescent anatomical contrast agent: acriflavine. An algorithm based on sparse component analysis (SCA) and the circle transform (CT) was developed for image segmentation and quantification of distinct tissue types. The accuracy of our approach was quantified through simulations of tumor and muscle images. Specifically, tumor, muscle, and tumor+muscle tissue images were simulated because these tissue types were most commonly observed in sarcoma margins. Simulations were based on tissue characteristics observed in pathology slides. The potential clinical utility of our approach was evaluated by imaging excised margins and the tumor bed in a cohort of mice after surgical resection of sarcoma. Results Simulation experiments revealed that SCA+CT achieved the lowest errors for larger nuclear sizes and for higher contrast ratios (nuclei intensity/background intensity). For imaging of tumor margins, SCA+CT effectively isolated nuclei from tumor, muscle, adipose, and tumor+muscle tissue types. Differences in density were correctly identified with SCA+CT in a cohort of ex vivo and in vivo images, thus illustrating the diagnostic potential of our approach. Conclusion The combination of a subcellular-resolution microendoscope, acriflavine staining, and SCA+CT can be used to accurately isolate nuclei and quantify their density in anatomical images of heterogeneous tissue.

A quantitative microscopic approach to predict local recurrence based on in vivo intraoperative imaging of sarcoma tumor margins

The goal of resection of soft tissue sarcomas located in the extremity is to preserve limb function while completely excising the tumor with a margin of normal tissue. With surgery alone, one‐third of patients with soft tissue sarcoma of the extremity will have local recurrence due to microscopic residual disease in the tumor bed. Currently, a limited number of intraoperative pathology‐based techniques are used to assess margin status; however, few have been widely adopted due to sampling error and time constraints. To aid in intraoperative diagnosis, we developed a quantitative optical microscopy toolbox, which includes acriflavine staining, fluorescence microscopy, and analytic techniques called sparse component analysis and circle transform to yield quantitative diagnosis of tumor margins. A series of variables were quantified from images of resected primary sarcomas and used to optimize a multivariate model. The sensitivity and specificity for differentiating positive from negative ex vivo resected tumor margins was 82 and 75%. The utility of this approach was tested by imaging the in vivo tumor cavities from 34 mice after resection of a sarcoma with local recurrence as a bench mark. When applied prospectively to images from the tumor cavity, the sensitivity and specificity for differentiating local recurrence was 78 and 82%. For comparison, if pathology was used to predict local recurrence in this data set, it would achieve a sensitivity of 29% and a specificity of 71%. These results indicate a robust approach for detecting microscopic residual disease, which is an effective predictor of local recurrence.

A Fluorescence-Guided Laser Ablation System for Removal of Residual Cancer in a Mouse Model of Soft Tissue Sarcoma

The treatment of soft tissue sarcoma (STS) generally involves tumor excision with a wide margin. Although advances in fluorescence imaging make real-time detection of cancer possible, removal is limited by the precision of the human eye and hand. Here, we describe a novel pulsed Nd:YAG laser ablation system that, when used in conjunction with a previously described molecular imaging system, can identify and ablate cancer in vivo. Mice with primary STS were injected with the protease-activatable probe LUM015 to label tumors. Resected tissues from the mice were then imaged and treated with the laser using the paired fluorescence-imaging/ laser ablation device, generating ablation clefts with sub-millimeter precision and minimal underlying tissue damage. Laser ablation was guided by fluorescence to target tumor tissues, avoiding normal structures. The selective ablation of tumor implants in vivo improved recurrence-free survival after tumor resection in a cohort of 14 mice compared to 12 mice that received no ablative therapy. This prototype system has the potential to be modified so that it can be used during surgery to improve recurrence-free survival in patients with cancer.

Tissue quantification in photon-limited microendoscopy

This paper explores the use of Poisson sparse decomposition methods for computationally separating tumor nuclei from normal tissue structures in photon-limited microendoscopic images. Sparse decomposition tools are a natural fit for this application with promising preliminary results. However, there are significant the tradeoffs among different algorithms used for Poisson sparse decomposition which are described in detail and demonstrated via simulation.

Correlation of breast tissue histology and optical signatures to improve margin assessment techniques

Abstract. Optical spectroscopy is sensitive to morphological composition and has potential applications in intraoperative margin assessment. Here, we evaluate ex vivo breast tissue and corresponding quantified hematoxylin & eosin images to correlate optical scattering signatures to tissue composition stratified by patient characteristics. Adipose sites (213) were characterized by their cell area and density. All other benign and malignant sites (181) were quantified using a grid method to determine composition. The relationships between mean reduced scattering coefficient (〈μs′〉), and % adipose, % collagen, % glands, adipocyte cell area, and adipocyte density were investigated. These relationships were further stratified by age, menopausal status, body mass index (BMI), and breast density. We identified a positive correlation between 〈μs′〉 and % collagen and a negative correlation between 〈μs′〉 and age and BMI. Increased collagen corresponded to increased 〈μs′〉 variability. In postmenopausal women, 〈μs′〉 was similar regardless of fibroglandular content. Contributions from collagen and glands to 〈μs′〉 were independent and equivalent in benign sites; glands showed a stronger positive correlation than collagen to 〈μs′〉 in malignant sites. Our data suggest that scattering could differentiate highly scattering malignant from benign tissues in postmenopausal women. The relationship between scattering and tissue composition will support improved scattering models and technologies to enhance intraoperative optical margin assessment.

Structured Illumination Microscopy and a Quantitative Image Analysis for the Detection of Positive Margins in a Pre-Clinical Genetically Engineered Mouse Model of Sarcoma.

Intraoperative assessment of surgical margins is critical to ensuring residual tumor does not remain in a patient. Previously, we developed a fluorescence structured illumination microscope (SIM) system with a single-shot field of view (FOV) of 2.1×1.6 mm (3.4 mm2) and sub-cellular resolution (4.4 μm). The goal of this study was to test the utility of this technology for the detection of residual disease in a genetically engineered mouse model of sarcoma. Primary soft tissue sarcomas were generated in the hindlimb and after the tumor was surgically removed, the relevant margin was stained with acridine orange (AO), a vital stain that brightly stains cell nuclei and fibrous tissues. The tissues were imaged with the SIM system with the primary goal of visualizing fluorescent features from tumor nuclei. Given the heterogeneity of the background tissue (presence of adipose tissue and muscle), an algorithm known as maximally stable extremal regions (MSER) was optimized and applied to the images to specifically segment nuclear features. A logistic regression model was used to classify a tissue site as positive or negative by calculating area fraction and shape of the segmented features that were present and the resulting receiver operator curve (ROC) was generated by varying the probability threshold. Based on the ROC curves, the model was able to classify tumor and normal tissue with 77% sensitivity and 81% specificity (Youden’s index). For an unbiased measure of the model performance, it was applied to a separate validation dataset that resulted in 73% sensitivity and 80% specificity. When this approach was applied to representative whole margins, for a tumor probability threshold of 50%, only 1.2% of all regions from the negative margin exceeded this threshold, while over 14.8% of all regions from the positive margin exceeded this threshold.

An integrated strategy for improving contrast, durability, and portability of a Pocket Colposcope for cervical cancer screening and diagnosis

Introduction We have previously developed a portable Pocket Colposcope for cervical cancer screening in resource-limited settings. In this manuscript we report two different strategies (cross-polarization and an integrated reflector) to improve image contrast levels achieved with the Pocket Colposcope and evaluate the merits of each strategy compared to a standard-of-care digital colposcope. The desired outcomes included reduced specular reflection (glare), increased illumination beam pattern uniformity, and reduced electrical power budget. In addition, anti-fogging and waterproofing features were incorporated to prevent the Pocket Colposcope from fogging in the vaginal canal and to enable rapid disinfection by submersion in chemical agents. Methods Cross-polarization (Generation 3 Pocket Colposcope) and a new reflector design (Generation 4 Pocket Colposcope) were used to reduce glare and improve contrast. The reflector design (including the angle and height of the reflector sidewalls) was optimized through ray-tracing simulations. Both systems were characterized with a series of bench tests to assess specular reflection, beam pattern uniformity, and image contrast. A pilot clinical study was conducted to compare the Generation 3 and 4 Pocket Colposcopes to a standard-of-care colposcope (Leisegang Optik 2). Specifically, paired images of cervices were collected from the standard-of-care colposcope and either the Generation 3 (n = 24 patients) or the Generation 4 (n = 32 patients) Pocket Colposcopes. The paired images were blinded by device, randomized, and sent to an expert physician who provided a diagnosis for each image. Corresponding pathology was obtained for all image pairs. The primary outcome measures were the level of agreement (%) and κ (kappa) statistic between the standard-of-care colposcope and each Pocket Colposcope (Generation 3 and Generation 4). Results Both generations of Pocket Colposcope had significantly higher image contrast when compared to the standard-of-care colposcope. The addition of anti-fog and waterproofing features to the Generation 3 and 4 Pocket Colposcope did not impact image quality based on qualitative and quantitative metrics. The level of agreement between the Generation 3 Pocket Colposcope and the standard-of-care colposcope was 75.0% (kappa = 0.4000, p = 0.0028, n = 24). This closely matched the level of agreement between the Generation 4 Pocket Colposcope and the standard-of-care colposcope which was also 75.0% (kappa = 0.4941, p = 0.0024, n = 32). Conclusion Our results indicate that the Generation 3 and 4 Pocket Colposcopes perform comparably to the standard-of-care colposcope, with the added benefit of being low-cost and waterproof, which is ideal for use in resource-limited settings. Additionally, the reflector significantly reduces the electrical requirements of the Generation 4 Pocket Colposcope enhancing portability without altering performance compared to the Generation 3 system.

Algorithms for differentiating between images of heterogeneous tissue across fluorescence microscopes.

Fluorescence microscopy can be used to acquire real-time images of tissue morphology and with appropriate algorithms can rapidly quantify features associated with disease. The objective of this study was to assess the ability of various segmentation algorithms to isolate fluorescent positive features (FPFs) in heterogeneous images and identify an approach that can be used across multiple fluorescence microscopes with minimal tuning between systems. Specifically, we show a variety of image segmentation algorithms applied to images of stained tumor and muscle tissue acquired with 3 different fluorescence microscopes. Results indicate that a technique called maximally stable extremal regions followed by thresholding (MSER + Binary) yielded the greatest contrast in FPF density between tumor and muscle images across multiple microscopy systems.

Development of algorithms for automated detection of cervical pre-cancers with a low-cost, point-of-care, Pocket Colposcope

The World Health Organization recommends visual inspection with acetic acid(VIA) and/or Lugol’s iodine(VILI) for cervical cancer screening in low-resource settings. Human interpretation of diagnostic indicators from visual inspection is qualitative, subjective and has high inter-observer discordance, which could lead both to adverse outcomes for the patient and unnecessary follow-ups. In this work, we propose methods for (1) automatic feature extraction and classification for VIA and VILI cervigrams and (2) combining features of VIA/VILI cervigrams for improved performance. Cervix images (cervigrams) were acquired with a low-cost, miniature, digital colposcope. We describe algorithms to pre-process pathology-labeled cervigrams and extract simple but powerful color and textural-based features. The features are used to train a support vector machine(SVM) to classify cervigrams based on pathology for VIA, VILI, and combination of the two contrasts. The proposed framework achieved a sensitivity, specificity, and accuracy of 81.3%, 78.6%, and 80.0%, respectively when used to distinguish cervical intraepithelial neoplasia (CIN+) relative to normal and benign tissues. This is superior to the average values achieved by expert physicians on the same data set for discriminating normal/benign from CIN+ (sensitivity=77%, specificity=51%, accuracy=63%). The results suggest that utilizing simple color-and textural-based features from VIA and VILI images may provide unbiased automation of cervigrams, enabling automated, expert-level diagnosis of cervical pre-cancer at the point-of-care.