abstract: Early detection and treatment of disease is paramount for improving human health and wellness. Micro-scale devices promote new opportunities for the rapid, cost-effective, and accurate identification of altered biological states indicative of disease early-onset; these devices function at a scale more sensitive to numerous biological processes. The application of Micro-Electro-Mechanical Systems (MEMS) in biomedical settings has recently emerged and flourished over course of the last two decades, requiring a deep understanding of material biocompatibility, biosensing sensitively/selectively, biological constraints for artificial tissue/organ replacement, and the regulations in place to ensure device safety. Capitalizing on the inherent physical differences between cancerous and healthy cells, our ultra-thin silicone membrane enables earlier identification of bladder cancer—with a 70% recurrence rate. Building on this breakthrough, we have devised an array to multiplex this sample-analysis in real-time as well as expanding beyond bladder cancer. The introduction of new materials—with novel properties—to augment current and create innovative medical implants requires the careful analysis of material impact on cellular toxicity, mutagenicity, reactivity, and stability. Finally, the achievement of replacing defective biological systems with implanted artificial equivalents that must function within the same biological constraints, have consistent reliability, and ultimately show the promise of improving human health as demonstrated by our hydrogel check valve. The ongoing proliferation, expanding prevalence, and persistent improvement in MEMS devices through greater sensitivity, specificity, and integration with biological processes will undoubtedly bolster medical science with novel MEMS-based diagnostics and therapeutics. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2018

L'objectif de cette thèse est de faciliter le diagnostic du cancer de la vessie. Durant une cystoscopie, un endoscope est introduit dans la vessie pour explorer la paroi interne de l'organe qui est visualisée sur un écran. Cependant, le faible champ de vue de l'instrument complique le diagnostic et le suivi des lésions. Cette thèse présente des algorithmes pour la création de cartes bi- et tridimensionnelles à large champ de vue à partir de vidéo-séquences cystoscopiques. En utilisant les avancées récentes dans le domaine de la minimisation d'énergies discrètes, nous proposons des fonctions coût indépendantes des transformations géométriques requises pour recaler de façon robuste et précise des paires d'images avec un faible recouvrement spatial. Ces transformations sont requises pour construire des cartes lorsque des trajectoires d'images se croisent ou se superposent. Nos algorithmes détectent automatiquement de telles trajectoires et réalisent une correction globale de la position des images dans la carte. Finalement, un algorithme de minimisation d'énergie compense les faibles discontinuités de textures restantes et atténue les fortes variations d'illuminations de la scène. Ainsi, les cartes texturées sont uniquement construites avec les meilleures informations (couleurs et textures) pouvant être extraites des données redondantes des vidéo-séquences. Les algorithmes sont évalués quantitativement et qualitativement avec des fantômes réalistes et des données cliniques. Ces tests mettent en lumière la robustesse et la précision de nos algorithmes. La cohérence visuelle des cartes obtenues dépassent celles des méthodes de cartographie de la vessie de la littérature / The aim of this thesis is to facilitate bladder cancer diagnosis. The reference clinical examination is cystoscopy, where an endoscope, inserted into the bladder, allows to visually explore the organ's internal walls on a monitor. The main restriction is the small field of view (FOV) of the instrument, which complicates lesion diagnosis, follow-up and treatment traceability.In this thesis, we propose robust and accurate algorithms to create two- and three-dimensional large FOV maps from cystoscopic video-sequences. Based on recent advances in the field of discrete energy minimization, we propose transformation-invariant cost functions, which allow to robustly register image pairs, related by large viewpoint changes, with sub-pixel accuracy. The transformations linking such image pairs, which current state-of-the-art bladder image registration techniques are unable to robustly estimate, are required to construct maps with several overlapping image trajectories. We detect such overlapping trajectories automatically and perform non-linear global map correction. Finally, the proposed energy minimization based map compositing algorithm compensates small texture misalignments and attenuates strong exposure differences. The obtained textured maps are composed by a maximum of information/quality available from the redundant data of the video-sequence. We evaluate the proposed methods both quantitatively and qualitatively on realistic phantom and clinical data sets. The results demonstrate the robustness of the algorithms, and the obtained maps outperform state-of-the-art approaches in registration accuracy and global map coherence