Vision-based retargeting for endoscopic navigation

Endoscopy is a standard procedure for visualising the human gastrointestinal tract. With the advances in biophotonics, imaging techniques such as narrow band imaging, confocal laser endomicroscopy, and optical coherence tomography can be combined with normal endoscopy for assisting the early diagnosis of diseases, such as cancer. In the past decade, optical biopsy has emerged to be an effective tool for tissue analysis, allowing in vivo and in situ assessment of pathological sites with real-time feature-enhanced microscopic images. However, the non-invasive nature of optical biopsy leads to an intra-examination retargeting problem, which is associated with the difficulty of re-localising a biopsied site consistently throughout the whole examination. In addition to intra-examination retargeting, retargeting of a pathological site is even more challenging across examinations, due to tissue deformation and changing tissue morphologies and appearances. The purpose of this thesis is to address both the intra- and inter-examination retargeting problems associated with optical biopsy. We propose a novel vision-based framework for intra-examination retargeting. The proposed framework is based on combining visual tracking and detection with online learning of the appearance of the biopsied site. Furthermore, a novel cascaded detection approach based on random forests and structured support vector machines is developed to achieve efficient retargeting. To cater for reliable inter-examination retargeting, the solution provided in this thesis is achieved by solving an image retrieval problem, for which an online scene association approach is proposed to summarise an endoscopic video collected in the first examination into distinctive scenes. A hashing-based approach is then used to learn the intrinsic representations of these scenes, such that retargeting can be achieved in subsequent examinations by retrieving the relevant images using the learnt representations. For performance evaluation of the proposed frameworks, extensive phantom, ex vivo and in vivo experiments have been conducted, with results demonstrating the robustness and potential clinical values of the methods proposed.Open Acces

Study of Computational Image Matching Techniques: Improving Our View of Biomedical Image Data

Image matching techniques are proven to be necessary in various fields of science and engineering, with many new methods and applications introduced over the years. In this PhD thesis, several computational image matching methods are introduced and investigated for improving the analysis of various biomedical image data. These improvements include the use of matching techniques for enhancing visualization of cross-sectional imaging modalities such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), denoising of retinal Optical Coherence Tomography (OCT), and high quality 3D reconstruction of surfaces from Scanning Electron Microscope (SEM) images. This work greatly improves the process of data interpretation of image data with far reaching consequences for basic sciences research. The thesis starts with a general notion of the problem of image matching followed by an overview of the topics covered in the thesis. This is followed by introduction and investigation of several applications of image matching/registration in biomdecial image processing: a) registration-based slice interpolation, b) fast mesh-based deformable image registration and c) use of simultaneous rigid registration and Robust Principal Component Analysis (RPCA) for speckle noise reduction of retinal OCT images. Moving towards a different notion of image matching/correspondence, the problem of view synthesis and 3D reconstruction, with a focus on 3D reconstruction of microscopic samples from 2D images captured by SEM, is considered next. Starting from sparse feature-based matching techniques, an extensive analysis is provided for using several well-known feature detector/descriptor techniques, namely ORB, BRIEF, SURF and SIFT, for the problem of multi-view 3D reconstruction. This chapter contains qualitative and quantitative comparisons in order to reveal the shortcomings of the sparse feature-based techniques. This is followed by introduction of a novel framework using sparse-dense matching/correspondence for high quality 3D reconstruction of SEM images. As will be shown, the proposed framework results in better reconstructions when compared with state-of-the-art sparse-feature based techniques. Even though the proposed framework produces satisfactory results, there is room for improvements. These improvements become more necessary when dealing with higher complexity microscopic samples imaged by SEM as well as in cases with large displacements between corresponding points in micrographs. Therefore, based on the proposed framework, a new approach is proposed for high quality 3D reconstruction of microscopic samples. While in case of having simpler microscopic samples the performance of the two proposed techniques are comparable, the new technique results in more truthful reconstruction of highly complex samples. The thesis is concluded with an overview of the thesis and also pointers regarding future directions of the research using both multi-view and photometric techniques for 3D reconstruction of SEM images

Interactive Learning for the Analysis of Biomedical and Industrial Imagery

In der vorliegenden Dissertation werden Methoden des überwachten Lernens untersucht und auf die Analyse und die Segmentierung digitaler Bilddaten angewendet, die aus diversen Forschungsgebieten stammen. Die Segmentierung und die Klassifikation spielen eine wichtige Rolle in der biomedizinischen und industriellen Bildverarbeitung, häufig basiert darauf weitere Erkennung und Quantifikation. Viele problemspezifische Ansätze existieren für die unterschiedlichsten Fragestellungen und nutzen meist spezifisches Vorwissen aus den jeweiligen Bilddaten aus. In dieser Arbeit wird ein überwachtes Lernverfahren vorgestellt, das mehrere Objekte und deren Klassen gleichzeitig segmentieren und unterscheiden kann. Die Methode ist generell genug um einen wichtigen Bereich von Anwendungen abzudecken, für deren Lösung lokale Merkmale eine Rolle spielen. Segmentierungsergebnisse dieses Ansatzes werden auf verschiedenen Datensätzen mit unterschiedlichen Problemstellungen gezeigt. Die Resultate unterstreichen die Anwendbarkeit der Lernmethode für viele biomedizinische und industrielle Anwendungen, ohne dass explizite Kenntnisse der Bildverarbeitung und Programmierung vorausgesetzt werden müssen. Der Ansatz basiert auf generellen Merkmalsklassen, die es erlauben lokal Strukturen wie Farbe, Textur und Kanten zu beschreiben. Zu diesem Zweck wurde eine interaktive Software implementiert, welche, für gewöhnliche Bildgrößen, in Echtzeit arbeitet und es somit einem Domänenexperten erlaubt Segmentierungs- und Klassifikationsaufgaben interaktiv zu bearbeiten. Dafür sind keine Kenntnisse in der Bildverarbeitung nötig, da sich die Benutzerinteraktion auf intuitives Markieren mit einem Pinselwerkzeug beschränkt. Das interaktiv trainierte System kann dann ohne weitere Benutzerinteraktion auf viele neue Bilder angewendet werden. Der Ansatz ist auf Segmentierungsprobleme beschränkt, für deren Lösung lokale diskriminative Merkmale ausreichen. Innerhalb dieser Einschränkung zeigt der Algorithmus jedoch erstaunlich gute Resultate, die in einer applikationsspezifischen Prozedur weiter verbessert werden können. Das Verfahren unterstützt bis zu vierdimensionale, multispektrale Bilddaten in vereinheitlichter Weise. Um die Anwendbar- und Übertragbarkeit der Methode weiter zu illustrieren wurden mehrere echte Anwendungsfälle, kommend aus verschiedenen bildgebenden Bereichen, untersucht. Darunter sind u. A. die Segmentierung von Tumorgewebe, aufgenommen mittelsWeitfeldmikroskopie, die Quantifikation von Zellwanderungen in konfokalmikroskopischen Aufnahmen für die Untersuchung der adulten Neurogenese, die Segmentierung von Blutgefäßen in der Retina des Auges, das Verfolgen von Kupferdrähten in einer Anwendung zur Produktauthentifikation und die Qualitätskontrolle von Mikroskopiebildern im Kontext von Hochdurchsatz-Experimenten. Desweiteren wurde eine neue Klassifikationsmethode basierend auf globalen Frequenzschätzungen für die Prozesskontrolle des Papieranlegers an Druckmaschinen entwickelt

Aerospace Medicine and Biology: A cumulative index to the 1981 issues

The aeromedical research reported considers the safety of the human component in manned space flight. The effects of spacecraft environment, radiation and weightlessness on human biological and psychological processes are covered

Sox10 regulates enteric neural crest cell migration in the developing gut

Concurrent Sessions 1: 1.3 - Organs to organisms: Models of Human Diseases: abstract no. 1417th ISDB 2013 cum 72nd Annual Meeting of the Society for Developmental Biology, VII Latin American Society of Developmental Biology Meeting and XI Congreso de la Sociedad Mexicana de Biologia del Desarrollo. The Conference's web site is located at http://www.inb.unam.mx/isdb/Sox10 is a HMG-domain containing transcription factor which plays important roles in neural crest cell survival and differentiation. Mutations of Sox10 have been identified in patients with Waardenburg-Hirschsprung syndrome, who suffer from deafness, pigmentation defects and intestinal aganglionosis. Enteric neural crest cells (ENCCs) with Sox10 mutation undergo premature differentiation and fail to colonize the distal hindgut. It is unclear, however, whether Sox10 plays a role in the migration of ENCCs. To visualize the migration behaviour of mutant ENCCs, we generated a Sox10NGFP mouse model where EGFP is fused to the N-terminal domain of Sox10. Using time-lapse imaging, we found that ENCCs in Sox10NGFP/+ mutants displays lower migration speed and altered trajectories compared to normal controls. This behaviour was cell-autonomous, as shown by organotypic grafting of Sox10NGFP/+ gut segments onto control guts and vice versa. ENCCs encounter different extracellular matrix (ECM) molecules along the developing gut. We performed gut explant culture on various ECM and found that Sox10NGFP/+ ENCCs tend to form aggregates, particularly on fibronectin. Time-lapse imaging of single cells in gut explant culture indicated that the tightly-packed Sox10 mutant cells failed to exhibit contact inhibition of locomotion. We determined the expression of adhesion molecule families by qPCR analysis, and found integrin expression unaffected while L1-cam and selected cadherins were altered, suggesting that Sox10 mutation affects cell adhesion properties of ENCCs. Our findings identify a de novo role of Sox10 in regulating the migration behaviour of ENCCs, which has important implications for the treatment of Hirschsprung disease.postprin

Analysis of craniofacial defects in Six1/Eya1-associated Branchio-Oto-Renal Syndrome

Poster Session I - Morphogenesis: 205/B10117th ISDB 2013 cum 72nd Annual Meeting of the Society for Developmental Biology, 7th Latin American Society of Developmental Biology Meeting and 11th Congreso de la Sociedad Mexicana de Biologia del Desarrollo.Branchio-Oto-Renal (BOR) syndrome patients exhibit craniofacial and renal anomalies as well as deafness. BOR syndrome is caused by mutations in Six1 or Eya1, both of which regulate cell proliferation and differentiation. The molecular mechanism underlying the craniofacial and branchial arch (BA) defects in BOR syndrome is unclear. We have found that Hoxb3 is up-regulated in the second branchial arch (BA2) of Six1-/- mutants. Moreover, Hoxb3 over-expression in transgenic mice leads to BA abnormalities which are similar to the BA defects in Six1-/- or Eya1-/- mutants, suggesting a regulatory relationship among Six1, Eya1 and Hoxb3 genes. The aim of this study is to investigate the molecular mechanism underlying abnormal BA development in BOR syndrome using Six1 and Eya1 mutant mice. Two potential Six1 binding sites were identified on the Hoxb3 gene. In vitro and in vivo Chromatin IP assays showed that Six1 could directly bind to one of the sites specifically. Furthermore, using a chick in ovo luciferase assay we showed that Six1 could suppress gene expression through one of the specific binding sites. On the other hand, in Six1-/- mutants, we found that the Notch ligand Jag1 was up-regulated in BA2. Similarly, in Hoxb3 transgenic mice, ectopic expression of Jag1 could be also detected in BA2. To investigate the activation of Notch signaling pathway, we found that Notch intracellular domain (NICD), a direct indicator of Notch pathway activation, was up-regulated in BAs of Six1-/-; Eya1-/- double mutants. Our results indicate that Hoxb3 and Notch signaling pathway are involved in mediating the craniofacial defects of Six1/Eya1-associated Branchio-Oto-Renal Syndrome.postprin

Intraoperative Navigation Systems for Image-Guided Surgery

Recent technological advancements in medical imaging equipment have resulted in a dramatic improvement of image accuracy, now capable of providing useful information previously not available to clinicians. In the surgical context, intraoperative imaging provides a crucial value for the success of the operation. Many nontrivial scientific and technical problems need to be addressed in order to efficiently exploit the different information sources nowadays available in advanced operating rooms. In particular, it is necessary to provide: (i) accurate tracking of surgical instruments, (ii) real-time matching of images from different modalities, and (iii) reliable guidance toward the surgical target. Satisfying all of these requisites is needed to realize effective intraoperative navigation systems for image-guided surgery. Various solutions have been proposed and successfully tested in the field of image navigation systems in the last ten years; nevertheless several problems still arise in most of the applications regarding precision, usability and capabilities of the existing systems. Identifying and solving these issues represents an urgent scientific challenge. This thesis investigates the current state of the art in the field of intraoperative navigation systems, focusing in particular on the challenges related to efficient and effective usage of ultrasound imaging during surgery. The main contribution of this thesis to the state of the art are related to: Techniques for automatic motion compensation and therapy monitoring applied to a novel ultrasound-guided surgical robotic platform in the context of abdominal tumor thermoablation. Novel image-fusion based navigation systems for ultrasound-guided neurosurgery in the context of brain tumor resection, highlighting their applicability as off-line surgical training instruments. The proposed systems, which were designed and developed in the framework of two international research projects, have been tested in real or simulated surgical scenarios, showing promising results toward their application in clinical practice

English for Strufents of Biological Departments

Структура пособия позволяет выбрать оптимальные способы организации работы для эффективного усвоения материала и аналитической обработки информации.Данное пособие предназначено для студентов I курса биологического факультета университета. В пособии представлены оригинальные тексты и упражнения к ним, способствующие закреплению лексического и грамматического материала