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    Label-free optical microscopy: Photon Absorption Remote Sensing (PARS) and other methods for label-free histopathological imaging of tissues
    (University of Waterloo, 2026-01-08) Ecclestone, Benjamin
    Emerging label-free microscopy methods offer promising new avenues to view cells and tissues in their native environment, minimizing external influences. These label-free techniques are an exciting departure from gold standard methods for visualizing microscopic cellular and tissue structures, which rely on centuries-old chemical staining processes. In current practice, chemical labelling can unavoidably interfere with specimens’ physical and biochemical integrity. As a result, samples are effectively consumed by staining with only a single stain set normally applied to each sample. This limitation is especially impactful in applications such as clinical oncology and medical histopathology. In these settings, irreversible staining processes can severely limit the diagnostic utility of samples; especially when there is limited sample volume (e.g., brain tumor biopsies). As an alternative, label-free imaging techniques offer a potential avenue to visualize subcellular tissue anatomy while preserving samples in their entirety. Subsequently label-free microscopy methods have significant potential to greatly increase the diagnostic utility of each specimen, thereby enhancing patient outcomes. This thesis focuses on developing new methods for label-free microscopy, specifically emphasizing techniques for label-free histopathology. As a starting point the targeted objective is to develop a label-free analog to chemical hematoxylin and eosin (H&E) staining. This objective is chosen as H&E represents the gold standard contrast applied in effectively every clinical diagnostic case. Subsequent developments in this thesis can be broken into three major sections, which focus on (1) developing label-free microscopy methods for H&E-like imaging, (2) exploring the biomolecular specificity of developed methods to validate the label-free H&E-like contrast, and (3) producing a label-free microscopy architecture capable of meeting the imaging requirements necessary for clinical adoption. The first collection of works explores the development of a range of label-free microscopy methods. These studies establish new variations and combinations of optical absorption and scattering microscopes to visualize microscopic tissue anatomy label-free. These efforts ultimately resulted in the development of a new optical absorption microscopy modality, Photon Absorption Remote Sensing (PARS). This comprehensive technique provides biomolecule-specific visualizations characterizing the dominant photophysical effects caused when photons are absorbed by a biomolecule. As a direct result, novel PARS specific contrasts are developed as the total absorption (TA) and quantum efficiency ratio (QER). These PARS measurements may provide unique views into biomolecules’ excited state dynamics, accessing characteristics related to the quantum yield. By specifically probing specimens’ response to the absorption of deep ultraviolet light, PARS is shown to provide label-free contrast directly reminiscent of gold standard chemical H&E staining methods. As a proof of concept, the initial PARS architecture is applied to capture submicron resolution images of key H&E-like diagnostic markers across a variety of human and animal tissue specimens. The second section of this thesis expands the basis for PARS histopathology by validating PARS capacity to produce H&E-like visualizations. Two main avenues of exploration are pursued in this effort. The first endeavor explores the underlying biomolecular contrast of the PARS measurements. Established statistical methods are applied to develop characteristic PARS profiles for biomolecules. These PARS signatures are then applied to map the abundance of molecules label-free inside complex specimens. As a proof of concept, key diagnostic features including nuclei, red blood cells, and connective tissues are directly characterized and unmixed label-free. Resulting statistical abundance mappings are directly validated against chemically stained ground truth counterparts. The second endeavor introduces an end-to-end pipeline which uses deep learning-based image-to-image transforms to emulate chemical H&E visualizations from label-free PARS data. Resulting PARS emulated H&E-like visualizations are validated against chemical H&E staining through a clinical concordance study. In this diagnostic validation study, statistical analysis is applied to determine if pathologists produce the same diagnoses on both PARS and chemical H&E images. In this preliminary test, the PARS-based virtual staining method achieves > 90% concordance with very high statistical confidence (Kappa > 0.7) across all measured diagnostic tests. The final thesis section develops a new PARS architecture which achieves pragmatic imaging performance, nearing the requirements for clinical diagnostic settings. The presented system features a hybrid opto-mechanical scanning architecture which allows for high-speed MHz rate imaging. This results in imaging speeds which are more than an order of magnitude faster than earlier PARS embodiments developed in the PhotoMedicine Labs (at the University of Waterloo). This work simultaneously develops an end-to-end control system and imaging workflow which enables fully automated PARS imaging of whole specimens. Deep learning methods are applied to the resulting PARS images to produce virtual H&E-like visualizations. Qualitative and quantitative methods are applied to validate the imaging performance across a range of human and animal tissue samples. Results indicate the PARS virtual H&E images are largely indistinguishable from chemically H&E-stained ground truth images. Notably, the presented system forms the basis for a commercially available clinically ready prototype for label-free PARS histopathology imaging. In total, the findings presented across this thesis encompass the development of a new variation of microscopy technique (PARS). This method provides unique views into the absorption and scattering characteristics of specimens opening a new avenue of label-free contrast. For the presented histopathology application, PARS can provide powerful H&E-like images which may circumvent key challenges of chemical staining. In clinical histopathology, this method could enhance the diagnostic utility of tissue specimens directly improving patient outcomes. Beyond histopathology, the principles of PARS may be directly applicable to a wide range of imaging applications spanning material science, biological research, and clinical diagnostics. Overall, the methods developed in this thesis lays the groundwork for new label-free optical absorption microscopy techniques, which are already achieving real-world commercial and clinical success in histopathology applications.
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    Advancing Semi-Supervised Domain Adaptive Semantic Segmentation Through Effective Source Integration Strategies
    (University of Waterloo, 2026-01-08) Kurien, Joshua
    Semantic segmentation is a highly valuable visual recognition task with applications across fields such as medical imaging, remote sensing, and manufacturing. However, training segmentation models is challenging because it requires large-scale, densely labeled data specific to the target. Semi-supervised learning (SSL) addresses this challenge by leveraging unlabeled data alongside limited labeled data, reducing the reliance on fully labeled datasets. Semi-supervised domain adaptation (SSDA) further mitigates this issue by incorporating labeled data from a source domain alongside minimally labeled target data. While existing SSDA methods often underperform compared to fully supervised approaches, recent SSL methods that utilize foundation models achieve near fully supervised performance. Given the strength of current SSL methods using foundation models, this thesis investigates effective strategies for integrating source-domain data from a different distribution into existing pipelines to improve segmentation performance. First, we explore a simple source transfer mechanism that merges target and source data into a single unified labeled set for SSL pipelines. Our analysis demonstrates the accuracy benefits of this setup while also highlighting some downsides, particularly in terms of training efficiency. We also examine the use of ensembling SSL and SSDA models to enhance target-domain performance. This ensemble combines a model trained solely on target data with a source-transferred SSDA model. We find that ensembling can improve performance in certain cases but is less effective in others, and training efficiency remains suboptimal due to the need to train two models. Given the training inefficiencies of simple source transfer and ensembling, we propose a dual-curriculum source integration strategy to address and improve these limitations. This approach consists of two complementary learning strategies: curriculum retrieval, which progressively samples source examples from easy to hard, and curriculum pasting, which increases the diversity of target-labeled data. Across our experiments, we compare against and outperform state-of-the-art SSL and SSDA methods on a variety of benchmarks, including synthetic-to-real and real-to-real scenarios. Our findings highlight the benefits of effective source data integration into modern SSL pipelines for boosting segmentation performance, opening a new avenue for label-efficient semantic segmentation.
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    REMind: A Robot Role-Playing Game To Promote Bystander Intervention
    (University of Waterloo, 2026-01-07) Sanoubari, Elaheh
    Peer bullying is a pervasive social problem, with bystanders' inaction being a critical challenge despite widespread disapproval of bullying. Effective intervention strategies must move beyond explanation-based instruction to facilitate embodied situated learning. This dissertation explores how social robots can serve as mediators for applied drama to foster prosocial bystander intervention in the context of peer bullying. It introduces Robot-Mediated Applied Drama (RMAD): an innovative framework that integrates drama-based pedagogy with social robotics to create safe, reflective, and embodied learning experiences. Using a Research through Design (RtD) methodology, this work advances through an iterative sequence of design studies that culminate in the development and evaluation of REMind (short for Robots Empowering Minds): a mixed-reality role-playing game where children engage in dramatized bullying scenarios performed by social robots. In REMind, three robots enact a conflict involving a bully, a victim, and a passive bystander. Players are invited to assume control of robotic avatar, reflect on the unfolding narrative, and improvise an intervention by using the robot as a proxy in order to change the story’s outcome. Through this structure, children rehearse bystander intervention strategies within a psychologically safe, yet emotionally engaging environment. The iterative design process of REMind unfolded across complementary empirical inquiries. A crowdsourced feasibility study first established that observers perceive aggression toward robots as morally wrong, validating the viability of using robots in the intervention. A narrative co-design study with children revealed storytelling patterns such as preferences for emotionally expressive and customizable robot characters. Interviews with teachers grounded the design in classroom realities, identifying gaps in existing programs. A game design focus group study further examined what makes educational robot role-play games pleasurable for children, leading to identifying concrete design elements that informed REMind’s interactive components such as core mechanics, use of tangible props, world aesthetics and narrative structure. This dissertation presents the resulting artifact, REMind, as a system consisting of five interconnected components: Learning Goals, Mechanics, Narrative, Technology, and Aesthetics. The learning goals were defined through consultation with subject-matter experts to ensure grounding in evidence-based best practices. By deliberate aligning the game pleasures identified in prior studies with the learning objectives, REMind introduces a suite of game mechanics that scaffold socio-emotional skills (such as robot-mediated spect-actorship or "puppet mode" for moral intervention, interpretation of immersive affective displays for empathy-training and perspective taking, and custom-made logic-gate puzzles for moral reasoning). Narrative design is scaffolded by borrowing a five-step cognitive model of bystander intervention from social psychology. The technical implementation is realized through StorySync, a novel spreadsheet-based scripting toolkit developed to synchronize multimodal cues (including multiple robots, graphical interfaces, ambient lighting, and sound) and manage narrative branching for live interactive robot drama. Finally, the aesthetic elements leverages emotional design, ambient cues, and digital scenography to create an emotionally resonant learning experience. This concrete high-fidelity prototype serves as a proof of concept for RMAD. This research contributes a theoretical and practical foundation for designing robot-mediated experiential learning systems, offering RMAD as a new direction for social robotics and educational technology. It further illustrates how embodied storytelling and interactive systems design might cultivate reflective, prosocial action in a complex domain of social-emotional learning. More broadly, it advocates for a shift in Human-Robot Interaction (HRI) research toward systems thinking, positioning game design as a powerful systems lens for creating and analyzing holistic user experiences.
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    Two-sample Inference, Order Determination, and Data Integration for Functional Data
    (University of Waterloo, 2026-01-07) Zhang, Chi
    Functional data analysis has gained increasing prominence in modern statistics, largely due to advancements in data collection technologies. It provides a nonparametric framework for analyzing discrete observations obtained from realizations of a continuous random function, often defined over time or space. In this thesis, we focus on three distinct problems, each reflecting a different aspect of functional data analysis. In Chapter 2, we address the problem of comparing mean functions between two groups of sparse functional data within the framework of a reproducing kernel Hilbert space. The proposed method is well-suited for sparsely and irregularly sampled functional data. Traditional approaches often assume homogeneous covariance structures across groups, an assumption that is difficult to justify in practice. To circumvent this limitation, we first develop a novel linear approximation for the mean estimator, which naturally leads to its desirable pointwise limiting distributions. Furthermore, we establish the weak convergence of the mean estimator, enabling the construction of a test statistic for the mean differences. The finite-sample performance of our method is demonstrated through extensive simulations and two real-world applications. In Chapter 3, we study the problem of determining the number of eigenpairs to retain in functional principal component analysis---a problem commonly referred to as order determination. When a covariance function admits a finite representation, the challenge becomes estimating the rank of the corresponding covariance operator. While this problem is straightforward when the full trajectories of functional data are available, in practice, functional data are typically collected discretely and are subject to measurement error contamination. Such contamination introduces a ridge in the empirical covariance function, obscuring the true rank. We develop a novel procedure to identify the true rank of the covariance operator by leveraging the information of eigenvalues and eigenfunctions. By incorporating smoothing techniques to accommodate the nonparametric nature of functional data, the method is applicable to functional data collected at random, subject-specific points. Extensive simulation studies demonstrate the excellent performance of our approach across a wide range of settings, outperforming commonly used information-criterion-based methods and maintaining effectiveness even in high-noise scenarios. We further illustrate our method with two real-world data examples. In Chapter 4, we investigate the integration of multi-source functional data to extract a subspace that captures the variation shared across sources. In practice, data collection procedures often follow source-specific protocols. Directly averaging sample covariance operators across sources implicitly assumes homogeneity, which may lead to biased recovery of both shared and source-specific variation structures. To address this issue, we propose a projection-based data integration method that explicitly separates the shared and source-specific subspaces. The method first estimates source-specific projection operators via smoothing to accommodate the nonparametric nature of functional data. The shared subspace is then isolated by examining the eigenvalues of the averaged projection operator across all sources. If a source-specific subspace is of interest, we re-project the associated source-specific covariance estimator onto the subspace orthogonal to the estimated shared subspace, and estimate the source-specific subspace from the resulting projection. We further establish the asymptotic properties of both the shared and source-specific subspace estimators. Extensive simulation studies demonstrate the effectiveness of the proposed method across a wide range of settings. Finally, we illustrate its practical utility with an example of air pollutants data.
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    Pretending Architecture: The Journey Towards Verne Station
    (University of Waterloo, 2026-01-07) Ferreira, Jamie Verdell
    This is a record of pretend architecture, a journey of fabricating fantasy in the form of a virtual environment that is an authentic fake. This is an exploration inspired by the many fictional stories that I have encountered in order to create an interpretation of a space station. Framed by the harsh reality of space and contrasted by idealistic viewpoints in film, literature and video games, the end result presented is a far cry from initial expectations. It is a means to an end; a way to explore architecture in outer space with the use of constructs. Verne Station exists as fragments of experiences; attempts to understand and discover the intoxicating ideals of a limitless frontier ruled by the harshest of living conditions. By use of the machine, one has the ability to create complex virtual environments to simulate and visualize space architecture concepts; a field that has historically been inaccessible to many. By simulating different scales of artificial gravity design, the real-time exploration of designed spaces can facilitate a clearer understanding and more effective visual feedback of potential space architecture designs. This is a thesis about coming to terms with not arriving at your original destination, the one you imagine and expect to reach, but instead, the real one which you never quite anticipated.