archives@tulane.edu / This dissertation is rooted in clinical pathology research, and the main character is addressing limitations in current pathology evaluation workflows. Diagnostic procedures for cancer are typically conducted via core needle biopsy procedures; however, tissue sampling limitations often result in a low yield of samples containing cancer – there are no reliable intraoperative methods to determine if the “lesion is in the needle”. If biopsy procedures result in a diagnosis of cancer, surgical removal of the tumor is often the frontline curative therapy for many cancers. Importantly, histologic evaluation following the whole resected organ is necessary to determine the presence of residual cancer, yet current methods do not allow efficient determination of tumor removal completeness intraoperatively. To address limitations of current histopathology methods, what is critically needed is a point-of-procedure fresh tissue evaluation system that facilitates 1) rapid on-site imaging and evaluation, 2) less destruction, and 3) more complete assessment of tumor content in fresh specimens. A novel microscopy system using video-rate structured illumination (VR-SIM), has been developed with the intent of rapid, point-of-procedure histological screening of intact biopsy and whole surgical specimens. VR-SIM leverages widefield imaging, rapid acting fluorescent stains, and optical sectioning to provide high contrast digital images of tissue with histological relevance. The method is to replicate the standard approach as closely as possible, but replace the physical section with an optically sectioned digital image. The overall goal of this work is to perform technological and methodological refinements necessary to translate VR-SIM as a clinical tool for histologic evaluation of fresh tissue in diagnostic procedures, biobanking, and tumor margin assessment. This project will lay the groundwork for quantitative evaluation of VR-SIM as a clinical tool – with the goal of leading toward industrial design of a VR-SIM as a medical device for hospital use. Developing a new framework for integration of high throughput microscopy into the clinical and research workflow, as well as developing new methods for quantification and evaluation of clinical effectiveness these tools will be presented and discussed in the context of patient outcomes and economic impact. / 1 / David Tulman

No description available.

Yes / Digital pathology offers the potential for significant benefits in diagnostic pathology, but currently the efficiency of slide viewing is a barrier to adoption. We hypothesised that presenting digital slides for simultaneous viewing of multiple sections of tissue for comparison, as in those with immunohistochemical panels, would allow pathologists to review cases more quickly. Novel software was developed to view synchronised parallel tissue sections on a digital pathology workstation. Sixteen histopathologists reviewed three liver biopsy cases including an immunohistochemical panel using the digital microscope, and three different liver biopsy cases including an immunohistochemical panel using the light microscope. The order of cases and interface was fully counterbalanced. Time to diagnosis was recorded and mean times are presented as data approximated to a normalised distribution. Mean time to diagnosis was 4 min 3 s using the digital microscope and 5 min 24 s using the light microscope, saving 1 min 21 s (95% CI 16 s to 2 min 26 s; p=0.02), using the digital microscope. Overall normalised mean time to diagnosis was 85% on the digital pathology workstation compared with 115% on the microscope, a relative reduction of 26%. With appropriate interface design, it is quicker to review immunohistochemical slides using a digital microscope than the conventional light microscope, without incurring any major diagnostic errors. As digital pathology becomes more integrated with routine clinical workflow and pathologists increase their experience of the technology, it is anticipated that other tasks will also become more time-efficient.

Colorectal cancer (CRC) patients are stratified by the Tumour, Node and Metastasis (TNM) staging system for clinical decision making. Additional genomic markers have a limited utility in some cases where precise targeted therapy may be available. Thus, classical clinical pathological staging remains the mainstay of the assessment of this disease. Surgical resection is generally considered curative for Stage II patients, however 20-30% of these patients experience disease recurrence and disease specific death. It is imperative to identify these high risk patients in order to assess if further treatment or detailed follow up could be beneficial to their overall survival. The aim of the thesis was to categorise Stage II CRC patients into high and low risk of disease specific death through novel image based analysis algorithms. Firstly, an image analysis algorithm was developed to quantify and assess the prognostic value of three histopathological features through immuno-fluorescence: lymphatic vessel density (LVD), lymphatic vessel invasion (LVI) and tumour budding (TB). Image analysis provides the ability to standardise their quantification and negates observer variability. All three histopathological features were found to be predictors of CRC specific death within the training set (n=50); TB (HR =5.7; 95% CI, 2.38-13.8), LVD (HR =5.1; 95% CI, 2.04-12.99) and LVI (HR =9.9; 95% CI, 3.57- 27.98). Only TB (HR=2.49; 95% CI, 1.03-5.99) and LVI (HR =2.46; 95%CI, 1 - 6.05), however, were significant predictors of disease specific death in the validation set (n=134). Image analysis was further employed to characterise TB and quantify intra-tumoural heterogeneity. Tumour subpopulations within CRC tissue sections were segmented for the quantification of differential biomarker expression associated with epithelial mesenchymal transition and aggressive disease. Secondly, a novel histopathological feature ‘Sum Area Large Tumour Bud’ (ALTB) was identified through immunofluorescence coupled to a novel tissue phenomics approach. The tissue phenomics approach created a complex phenotypic fingerprint consisting of multiple parameters extracted from the unbiased segmentation of all objects within a digitised image. Data mining was employed to identify the significant parameters within the phenotypic fingerprint. ALTB was found to be a more significant predictor of disease specific death than LVI or TB in both the training set (HR = 20.2; 95% CI, 4.6 – 87.9) and the validation set (HR = 4; 95% CI, 1.5 – 11.1). Finally, ALTB was combined with two parameters, ‘differentiation’ and ‘pT stage’, which were exported from the original patient pathology report to form an integrative pathology score. The integrative pathology score was highly significant at predicting disease specific death within the validation set (HR = 7.5; 95% CI, 3 – 18.5). In conclusion, image analysis allows the standardised quantification of set histopathological features and the heterogeneous expression of biomarkers. A novel image based histopathological feature combined with classical pathology allows the highly significant stratification of Stage II CRC patients into high and low risk of disease specific death.

archives@tulane.edu / For decades, histopathology and cytology have provided the reference standard for cancer diagnosis, prognosis prediction and treatment decisions. However, they are limited to 2D slices, which are created via cutting and/or smearing, thus not faithfully representing the true 3D structures of the cellular or tissue material. Multiple imaging methods have been utilized for non-destructive histologic imaging of tissues, but are usually limited by varying combinations of low resolution, low penetration depth, or a relatively slow imaging speed, and all suffer from anisotropic resolution, which could distort 3D tissue architectural renderings and thus hinder new work to analyze and quantify 3D tissue microarchitecture. Therefore, there is a clear need for a non-destructive imaging tool that can accurately represent the 3D structures of the tissue or cellular architecture, with comparable qualities and features as traditional histopathology. In this work, dual-view inverted selective plane illumination microscopy (diSPIM) has been customized and optimized for fast, 3D imaging of large biospecimens. Imaging contrast of highly scattering samples has been further improved by adding confocal detection and/or structured illumination (SI) as additional optional imaging modes. A pipeline of dual-view imaging and processing has also been developed to achieve more isotropic 3D resolution, specifically on DRAQ5 and eosin (D&E) stained large (millimeter to centimeter size) biopsies. To determine the impact of 3D, high-resolution imaging on clinical diagnostic endpoints, multiple prostate cancer (PCa) biopsies have been collected, imaged with diSPIM, and evaluated by pathologists. It has been found that the pathologist is “equally” confident on the PCa diagnosis from viewing 3D volumes and 2D slices, and the diagnostic agreement between 3D volumes is significantly higher than 2D slices. The high-resolution and large-volume coverage of diSPIM may also help verify results from other lower-resolution modalities by serving as a 3D histology surrogate. Tissue correlations have been found between images acquired by diSPIM and photo-acoustic imaging, or by diSPIM and biodynamic imaging, proving diSPIM as a useful tool to aid in validation of lower-resolution imaging tools. The potential of diSPIM imaging has also been demonstrated in other applications, such as in the study of in-vitro neural models. / 1 / Bihe Hu

No description available.

The quality of clinical pathology is a critical index for evaluating a nation's healthcare level. Recently developed digital pathology techniques have the capability to transform pathological slides into digital whole slide images (WSI). This transformation facilitates data storage, online transmission, real-time viewing, and remote consultations, significantly elevating clinical diagnosis. The effectiveness and efficiency of digital pathology imaging often hinge on the precision and speed of autofocusing. However, achieving autofocusing of pathological images presents challenges under constraints including uneven focus distribution and limited Depth of Field (DoF). Current autofocusing methods, such as those relying on image stacks, need to use more time and resources for capturing and processing images. Moreover, autofocusing based on reflective hardware systems, despite its efficiency, incurs significant hardware costs and suffers from a lack of system compatibility. Finally, machine learning-based autofocusing can circumvent repetitive mechanical movements and camera shots. However, a simplistic end-to-end implementation that does not account for the imaging process falls short of delivering satisfactory focus prediction and in-focus image restoration. In this thesis, we present three distinct autofocusing techniques for defocus pathology images: (1) Aberration-aware Focal Distance Prediction leverages the asymmetric effects of optical aberrations, making it ideal for focus prediction within focus map scenarios; (2) Dual-shot Deep Autofocusing with a Fixed Offset Prior is designed to merge two images taken at different defocus distances with fixed positions, ensuring heightened accuracy in in-focus image restoration for fast offline situations; (3) Semi-blind Deep Restoration of Defocus Images utilizes multi-task joint prediction guided by PSF, enabling high-efficiency, single-pass scanning for offline in-focus image restoration. / Thesis / Doctor of Philosophy (PhD)

No description available.

Tuberculosis causes more deaths worldwide than any other curable infectious disease. This is the case despite tuberculosis appearing to be on the verge of eradication midway through the last century. Efforts at reversing the spread of tuberculosis have intensified since the early 1990s. Since then, microscopy has been the primary frontline diagnostic. In this dissertation, advances in clinical microscopy towards array microscopy for digital detection of Mycobacterium tuberculosis are presented. Digital array microscopy separates the tasks of microscope operation and pathogen detection and will reduce the specialization needed in order to operate the microscope. Distributing the work and reducing specialization will allow this technology to be deployed at the point of care, taking the front-line diagnostic for tuberculosis from the microscopy center to the community health center. By improving access to microscopy centers, hundreds of thousands of lives can be saved. For this dissertation, a lens was designed that can be manufactured as 4×6 array of microscopes. This lens design is diffraction limited, having less than 0.071 waves of aberration (root mean square) over the entire field of view. A total area imaged onto a full-frame digital image sensor is expected to be 3.94 mm2, which according to tuberculosis microscopy guidelines is more than sufficient for a sensitive diagnosis. The design is tolerant to single point diamond turning manufacturing errors, as found by tolerance analysis and by fabricating a prototype. Diamond micro-milling, a fabrication technique for lens array molds, was applied to plastic plano-concave and plano-convex lens arrays, and found to produce high quality optical surfaces. The micro-milling technique did not prove robust enough to produce bi-convex and meniscus lens arrays in a variety of lens shapes, however, and it required lengthy fabrication times. In order to rapidly prototype new lenses, a new diamond machining technique was developed called 4-axis single point diamond machining. This technique is 2-10x faster than micro-milling, depending on how advanced the micro-milling equipment is. With array microscope fabrication still in development, a single prototype of the lens designed for an array microscope was fabricated using single point diamond turning. The prototype microscope objective was validated in a pre-clinical trial. The prototype was compared with a standard clinical microscope objective in diagnostic tests. High concordance, a Fleiss’s kappa of 0.88, was found between diagnoses made using the prototype and standard microscope objectives and a reference test. With the lens designed and validated and an advanced fabrication process developed, array microscopy technology is advanced to the point where it is feasible to rapidly prototype an array microscope for detection of tuberculosis and translate array microscope from an innovative concept to a device that can save lives.

The 12th International St Gallen conference on breast cancer (2011) proposed patient categorization for systemic therapy, based on intrinsic breast cancer subtypes, defined by imunohistochemistry (IHC) test results. Since this classification is based on semi-quantitative expression of IHC biomarker expression, an issue of defining and applying cutoff values remains. Essential improvement in the IHC testing has become possible with digital image analysis tools enabling quantitative evaluation of the IHC data. This study explores data obtained by digital image analysis methods applied to evaluate a comprehensive biomarker dataset (p53, AR, p16, BCL2, SATB1, HIF1) along with well established (ER, PR, HER2, Ki67) biomarkers. Also, an extensive set of genetic and epigenetic biomarkers has been tested. For the first time, the dataset of 10 IHC biomarkers, evaluated by digital analysis was explored by the means of factor analysis to establish the intrinsic factors of biological variation and informative value of IHC biomarkers and their combinatiions. The results also provided insights into the significance and combinatorial effects of the established and relatively new biomarkers (p16, SATB1, HIF1, Ki67/BCL2, etc.). / XII tarptautinėje St Gallen krūties vėžio konferencijoje (2011) Ekspertų komisijos priimta nauja pacientų klasifikacija sisteminei terapijai atlikti, paremta biologiniais krūties vėžio subtipais, kurie apibrėžiami imunohistocheminiu tyrimu (IHC). Tačiau ši nauja navikų klasifikacija iš esmės pagrįsta pusiau kiekybiniu biožymenų raiškos vertinimu, todėl išlieka aktuali ribinių verčių nustatymo problematika. Esminiai pokyčiai IHC tyrimų srityje galimi atsiradus skaitmeninio vaizdinimo technologijoms, leidžiančiomis IHC tyrimų rezultatus analizuoti kiekybiniais parametrais. Darbe naudojant skaitmeninį vaizdo analizės metodą atliktas išsamus biologinių žymenų tyrimas leido palyginti svarbių, tačiau nepakankamai ištirtų (p53, AR, p16, BCL2, SATB1, HIF1) IHC žymenų informatyvumą su esamų prognozinių žymenų (ER, PR, HER2, Ki67) rodikliais. Ištirtas platus genetinių ir epigenetinių krūties vėžio žymenų spektras. Pirmą kartą 10 IHC žymenų rinkinio, įvertinto skaitmeninės analizės būdu, duomenys panaudoti faktorinės analizės metodu nustatyti jų variacijų vidinius veiksnius, atskleidžiančius biologinius dėsningumus ir IHC žymenų bei jų derinių informatyvumą. Šios analizės rezultatai leido naujai įvertinti publikuotų krūties vėžio IHC žymenų bei jų derinių (p16, SATB1, HIF1, Ki67/BCL2 ir kt.) informatyvumą.