LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

Medical Image Registration Using Deep Neural Networks

Registration is a fundamental problem in medical image analysis wherein images are transformed spatially to align corresponding anatomical structures in each image. Recently, the development of learning-based methods, which exploit deep neural networks and can outperform classical iterative methods, has received considerable interest from the research community. This interest is due in part to the substantially reduced computational requirements that learning-based methods have during inference, which makes them particularly well-suited to real-time registration applications. Despite these successes, learning-based methods can perform poorly when applied to images from different modalities where intensity characteristics can vary greatly, such as in magnetic resonance and ultrasound imaging. Moreover, registration performance is often demonstrated on well-curated datasets, closely matching the distribution of the training data. This makes it difficult to determine whether demonstrated performance accurately represents the generalization and robustness required for clinical use. This thesis presents learning-based methods which address the aforementioned difficulties by utilizing intuitive point-set-based representations, user interaction and meta-learning-based training strategies. Primarily, this is demonstrated with a focus on the non-rigid registration of 3D magnetic resonance imaging to sparse 2D transrectal ultrasound images to assist in the delivery of targeted prostate biopsies. While conventional systematic prostate biopsy methods can require many samples to be taken to confidently produce a diagnosis, tumor-targeted approaches have shown improved patient, diagnostic, and disease management outcomes with fewer samples. However, the available intraoperative transrectal ultrasound imaging alone is insufficient for accurate targeted guidance. As such, this exemplar application is used to illustrate the effectiveness of sparse, interactively-acquired ultrasound imaging for real-time, interventional registration. The presented methods are found to improve registration accuracy, relative to state-of-the-art, with substantially lower computation time and require a fraction of the data at inference. As a result, these methods are particularly attractive given their potential for real-time registration in interventional applications

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

New geometric algorithms and data structures for collision detection of dynamically deforming objects

Any virtual environment that supports interactions between virtual objects and/or a user and objects, needs a collision detection system to handle all interactions in a physically correct or plausible way. A collision detection system is needed to determine if objects are in contact or interpenetrates. These interpenetrations are resolved by a collision handling system. Because of the fact, that in nearly all simulations objects can interact with each other, collision detection is a fundamental technology, that is needed in all these simulations, like physically based simulation, robotic path and motion planning, virtual prototyping, and many more. Most virtual environments aim to represent the real-world as realistic as possible and therefore, virtual environments getting more and more complex. Furthermore, all models in a virtual environment should interact like real objects do, if forces are applied to the objects. Nearly all real-world objects will deform or break down in its individual parts if forces are acted upon the objects. Thus deformable objects are becoming more and more common in virtual environments, which want to be as realistic as possible and thus, will present new challenges to the collision detection system. The necessary collision detection computations can be very complex and this has the effect, that the collision detection process is the performance bottleneck in most simulations. Most rigid body collision detection approaches use a BVH as acceleration data structure. This technique is perfectly suitable if the object does not change its shape. For a soft body an update step is necessary to ensure that the underlying acceleration data structure is still valid after performing a simulation step. This update step can be very time consuming, is often hard to implement and in most cases will produce a degenerated BVH after some simulation steps, if the objects generally deform. Therefore, the here presented collision detection approach works entirely without an acceleration data structure and supports rigid and soft bodies. Furthermore, we can compute inter-object and intraobject collisions of rigid and deformable objects consisting of many tens of thousands of triangles in a few milliseconds. To realize this, a subdivision of the scene into parts using a fuzzy clustering approach is applied. Based on that all further steps for each cluster can be performed in parallel and if desired, distributed to different GPUs. Tests have been performed to judge the performance of our approach against other state-of-the-art collision detection algorithms. Additionally, we integrated our approach into Bullet, a commonly used physics engine, to evaluate our algorithm. In order to make a fair comparison of different rigid body collision detection algorithms, we propose a new collision detection Benchmarking Suite. Our Benchmarking Suite can evaluate both the performance as well as the quality of the collision response. Therefore, the Benchmarking Suite is subdivided into a Performance Benchmark and a Quality Benchmark. This approach needs to be extended to support soft body collision detection algorithms in the future.Jede virtuelle Umgebung, welche eine Interaktion zwischen den virtuellen Objekten in der Szene zulässt und/oder zwischen einem Benutzer und den Objekten, benötigt für eine korrekte Behandlung der Interaktionen eine Kollisionsdetektion. Nur dank der Kollisionsdetektion können Berührungen zwischen Objekten erkannt und mittels der Kollisionsbehandlung aufgelöst werden. Dies ist der Grund für die weite Verbreitung der Kollisionsdetektion in die verschiedensten Fachbereiche, wie der physikalisch basierten Simulation, der Pfadplanung in der Robotik, dem virtuellen Prototyping und vielen weiteren. Auf Grund des Bestrebens, die reale Umgebung in der virtuellen Welt so realistisch wie möglich nachzubilden, steigt die Komplexität der Szenen stetig. Fortwährend steigen die Anforderungen an die Objekte, sich realistisch zu verhalten, sollten Kräfte auf die einzelnen Objekte ausgeübt werden. Die meisten Objekte, die uns in unserer realen Welt umgeben, ändern ihre Form oder zerbrechen in ihre Einzelteile, wenn Kräfte auf sie einwirken. Daher kommen in realitätsnahen, virtuellen Umgebungen immer häufiger deformierbare Objekte zum Einsatz, was neue Herausforderungen an die Kollisionsdetektion stellt. Die hierfür Notwendigen, teils komplexen Berechnungen, führen dazu, dass die Kollisionsdetektion häufig der Performance-Bottleneck in der jeweiligen Simulation darstellt. Die meisten Kollisionsdetektionen für starre Körper benutzen eine Hüllkörperhierarchie als Beschleunigungsdatenstruktur. Diese Technik ist hervorragend geeignet, solange sich die Form des Objektes nicht verändert. Im Fall von deformierbaren Objekten ist eine Aktualisierung der Datenstruktur nach jedem Schritt der Simulation notwendig, damit diese weiterhin gültig ist. Dieser Aktualisierungsschritt kann, je nach Hierarchie, sehr zeitaufwendig sein, ist in den meisten Fällen schwer zu implementieren und generiert nach vielen Schritten der Simulation häufig eine entartete Hüllkörperhierarchie, sollte sich das Objekt sehr stark verformen. Um dies zu vermeiden, verzichtet unsere Kollisionsdetektion vollständig auf eine Beschleunigungsdatenstruktur und unterstützt sowohl rigide, wie auch deformierbare Körper. Zugleich können wir Selbstkollisionen und Kollisionen zwischen starren und/oder deformierbaren Objekten, bestehend aus vielen Zehntausenden Dreiecken, innerhalb von wenigen Millisekunden berechnen. Um dies zu realisieren, unterteilen wir die gesamte Szene in einzelne Bereiche mittels eines Fuzzy Clustering-Verfahrens. Dies ermöglicht es, dass alle Cluster unabhängig bearbeitet werden und falls gewünscht, die Berechnungen für die einzelnen Cluster auf verschiedene Grafikkarten verteilt werden können. Um die Leistungsfähigkeit unseres Ansatzes vergleichen zu können, haben wir diesen gegen aktuelle Verfahren für die Kollisionsdetektion antreten lassen. Weiterhin haben wir unser Verfahren in die Physik-Engine Bullet integriert, um das Verhalten in dynamischen Situationen zu evaluieren. Um unterschiedliche Kollisionsdetektionsalgorithmen für starre Körper korrekt und objektiv miteinander vergleichen zu können, haben wir eine Benchmarking-Suite entwickelt. Unsere Benchmarking- Suite kann sowohl die Geschwindigkeit, für die Bestimmung, ob zwei Objekte sich durchdringen, wie auch die Qualität der berechneten Kräfte miteinander vergleichen. Hierfür ist die Benchmarking-Suite in den Performance Benchmark und den Quality Benchmark unterteilt worden. In der Zukunft wird diese Benchmarking-Suite dahingehend erweitert, dass auch Kollisionsdetektionsalgorithmen für deformierbare Objekte unterstützt werden

12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

No abstract available

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Physically-based 6-DoF Nodes Deformable Models: Application to Connective Tissues Simulation and Soft-Robots Control

The medical simulation is an increasingly active research field. Yet, despite the promising advance observed over the past years, the complete virtual patient’s model is yet to come. There are still many avenues for improvements, especially concerning the mechanical modeling of boundary conditions on anatomical structures.So far, most of the work has been dedicated to organs simulation, which are generally simulated alone. This raises a real problem as the role of the surrounding organs in the boundary conditions is neglected. However, these interactions can be complex, involving contacts but also mechanical links provided by layers of soft tissues. The latter are known as connective tissues or fasciae. As a consequence, the mutual influences between the anatomical structures are generally simplified, weakening the realism of the simulations.This thesis aims at studying the importance of the connective tissues, and especially of a proper modeling of the boundary conditions. To this end, the role of the ligaments during laparoscopic liver surgery has been investigated. In order to enhance the simulations’ realism, a mechanical model dedicated to the connective tissues has been worked out. This has led to the development of a physically-based method relying on material points that can, not only translate, but also rotate themselves. The goal of this model is to enable the simulation of multiple organs linked by complex interactions.In addition, the work on the connective tissues model has been derived to be used in soft robotics. Indeed, the principle of relying on orientable material points has been used to developed a reduced model that can reproduce the behavior of more complex structures. The objective of this work is to provide the means to control – in real-time – a soft robot made of a deformable arm.La simulation médicale est un domaine de recherche de plus en plus actif. Cependant, malgré les avancées prometteuses observées ces dernières années, le modèle complet du patient virtuel reste un objectif ambitieux. Il existe encore de nombreuses opportunités de recherche, notamment concernant la modélisation mécanique des conditions aux limites des organes.Jusqu'à présent, la majorité des travaux était consacrée à la simulation d'organes, ces derniers étant généralement simulés seuls. Cette situation pose un réel problème car l'influence qu'ont les organes environnants sur les conditions aux limites est négligée. Ces interactions peuvent être complexes, impliquant des contacts mais aussi des liaisons mécaniques dues à des couches de tissus connus sous le nom de tissus conjonctifs ou fasciae. Pour cette raison, les influences mutuelles entre les structures anatomiques sont généralement simplifiées, diminuant le réalisme des simulations.Cette thèse visé à étudier l'importance des tissus conjonctifs, et plus particulièrement d'une bonne modélisation des conditions aux limites. Dans ce but, le rôle des ligaments lors d'une intervention chirurgicale sur la foie par laparoscopie a été étudié. Afin d'améliorer le réalisme des simulations, un modèle mécanique dédié aux tissus conjonctifs a été mis au point. Ainsi, une méthode basée sur la mécanique des milieux continus et un ensemble de nœuds à 6 degrés de liberté a été développée. L'objectif de ce modèle étant de permettre la simulation simultanée de plusieurs organes liés par des interaction complexes.En outre, les travaux sur les tissus conjonctifs ont donné lieu à la mise au point d'une méthode de modélisation utilisée dans le cadre des robots déformables. Cette méthode permet un contrôle précis, et temps-réel, d'un bras robotisé déformable. En effet, l'utilisation de nœuds orientables a permis de développer un modèle a nombre de degrés de liberté réduit, qui permet de reproduire le comportement de structures plus complexes

Multiscale modeling of metal nanoparticles for biotechnological applications

Functionalized metal nanoparticles are supramolecular assemblies that are gaining increasing attention in biomedicine due to their broad-spectrum applicability. In this context, understanding the nano-biointerface is critical for implementing nanoparticles into medical practices, yet the structure-function relation of functionalized metal nanoparticles remains puzzling. This work discusses the design of metal nanoparticles with targeted applications from three focal points: structural modeling, method development, and biomolecular interactions. First, the NanoModeler webserver is introduced for the standardized building and parametrizing of metal nanoparticles for simulations at atomistic and coarse-grained resolutions. Second, a theoretical model is formulated to characterize the surface of charged nanoparticles, which, when combined with mesoscale simulations, clarifies the fundamental principles that enable colloidal stability at physiological conditions. Third, atomistic and coarse-grained simulations were combined to describe, at the molecular level, the non- disruptive cellular permeabilization induced by membranotropic nanoparticles to facilitate intracellular cargo delivery. The multilayered work presented here comprehends new online tools, physics-based methods, and molecular insights that expand our understanding of the structure-function relation in metal nanoparticles and contribute to the design of safe and effective nanoparticle-based therapeutic agents

Imaging as a tool to study leaf development in Arabidopsis thaliana

In contrast to humans and animals, the body plan of a plant is not completely defined within the embryonic stages. Organ formation continues throughout plant development and this iterative and modular process is continuously controlled by environmental cues such as light, gravity, temperature, humidity and chemicals. In most plant species, the above-ground plant body is dominated by leaves, the organs specialized in photosynthesis. This process converts carbon dioxide into organic components utilizing energy from sunlight; making leaves the energy production site and the growth engine of plants. In addition, in many cases the majority of a plant’s biomass consists of leaves, also making them important organs for the production of food, feed and bio-energy. The final leaf size is determined by the total number of cells and the average cell size that result from cell division and cell expansion, respectively. During leaf development of dicotyledonous species, a cell proliferation phase, characterized by actively dividing cells, is followed by a cell expansion phase, characterized by cell growth and differentiation. After expansion, cells mature and the final leaf size is reached. At the proliferation-to-expansion phase transition, cell division ceases along a longitudinal gradient from leaf tip to base. In this thesis, we set out to gain further insight in these developmental processes affecting leaf size, assisted by the use of imaging technology and automated image analysis. For these studies we used the model species Arabidopsis thaliana, focusing primarily on the epidermis of the developing leaves as divisions there are strictly anticlinal. Moreover this layer is thought to be the main tissue layer controlling leaf growth. As a first step, we developed different image analysis tools to allow for a better and more efficient analysis of the leaf developmental process. In the first place we developed an online framework, designated Leaf Image Analysis Interface (LIMANI), in which venation patterns are automatically segmented and measured on dark-field images. Image segmentation may be manually corrected through use of an interactive interface, allowing supervision and rectification steps in the automated image analysis pipeline and ensuring high-fidelity analysis. We subsequently used this framework to study vascular differentiation during leaf development and to analyze the venation pattern in transgenic lines with contrasting cellular and leaf size traits. A major conclusion from this work was that, as vascular differentiation occurs relatively late in development, the influence of a fully functional and differentiated venation pattern on final leaf size is rather limited. Furthermore, we describe a proof-of-concept to automate the kinematic analysis of leaf growth based on DIC pictures, by a sophisticated image processing chain and a data analysis pipeline. Next, we also developed imaging scripts to extract complete seedlings grown on soil and on Petri dishes and integrated those into three phenotyping platforms which monitor plant growth. Finally, we investigated the potential of emerging imaging technologies, particularly X-ray computed tomography, for future applications in plant growth analysis. The newly developed kinematic analysis tools allowed us to show that the transcription factors, SHORT-ROOT (SHR) and SCARECROW (SCR), next to their specific roles in cortex/endodermis differentiation and stem cell maintenance in the root, primarily function as general regulators of cell proliferation in leaves. The analysis of leaf growth revealed how these proteins affect the cellular growth dynamics and formed the basis to unravel the molecular mechanism controlling this. It turned out that they promote leaf growth mainly by the down-regulation of cell cycle inhibitors, known to restrain the activity of the transcription factor, E2Fa, stimulating S-phase progression. Although the dynamics of cell division and cell expansion processes can be analyzed rigorously by the leaf growth kinematics, knowledge of cell cycle duration, cell expansion, and their interaction at the individual cell level is still poorly understood, not only because of technical obstacles to study these phenomena, but also because the processes are intimately intertwined, shown by the fact that a reduced cell proliferation is often compensated by an increase in cell size and vice versa. A mathematical model fitted to detailed cellular measurements retrieved by automated image analysis of microscopic drawings of the leaf epidermis, revealed that average cell cycle duration remains constant throughout leaf development. Surprisingly, no evidence for a maximum cell size threshold for cell division of pavement cells was found in this analysis. We could estimate the division and expansion parameters of pavement and guard cell populations within the growing leaf separately and the model predicted that neighboring cells of different sizes within the epidermis expand at distinctly different relative rates. We could finally verify this by direct observations using live imaging. The mathematical model helped us to gain a better and more detailed insight into the processes that define leaf growth. But the transition from cell proliferation to cell expansion was a developmental time point that was still not characterized in detail. Differences in the timing of this transition strongly affects the number of cells formed and therefore potentially also serves as a control point determining mature leaf size. Several genes have been identified that alter leaf size by affecting the transition from primary to secondary morphogenesis. We characterized the progression of the transition on the morphological and molecular level using transcriptome analysis and imaging algorithms to visualize and quantify the size and shape of pavement cells along the proximal-distal axis of the leaf during transition. Both analyses showed that the transition from cell proliferation to expansion was established and abolished abruptly. Furthermore, the establishment of the cell cycle arrest front occurs simultaneously with the onset of photomorphogenesis. We provide evidence that retrograde signaling from chloroplasts can affect the onset of transition, revealing a previously unknown level of regulatory complexity during the transition from primary to secondary morphogenesis