236 research outputs found

    Semi-automated learning strategies for large-scale segmentation of histology and other big bioimaging stacks and volumes

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    Labelled high-resolution datasets are becoming increasingly common and necessary in different areas of biomedical imaging. Examples include: serial histology and ex-vivo MRI for atlas building, OCT for studying the human brain, and micro X-ray for tissue engineering. Labelling such datasets, typically, requires manual delineation of a very detailed set of regions of interest on a large number of sections or slices. This process is tedious, time-consuming, not reproducible and rather inefficient due to the high similarity of adjacent sections. In this thesis, I explore the potential of a semi-automated slice level segmentation framework and a suggestive region level framework which aim to speed up the segmentation process of big bioimaging datasets. The thesis includes two well validated, published, and widely used novel methods and one algorithm which did not yield an improvement compared to the current state-of the-art. The slice-wise method, SmartInterpol, consists of a probabilistic model for semi-automated segmentation of stacks of 2D images, in which the user manually labels a sparse set of sections (e.g., one every n sections), and lets the algorithm complete the segmentation for other sections automatically. The proposed model integrates in a principled manner two families of segmentation techniques that have been very successful in brain imaging: multi-atlas segmentation and convolutional neural networks. Labelling every structure on a sparse set of slices is not necessarily optimal, therefore I also introduce a region level active learning framework which requires the labeller to annotate one region of interest on one slice at the time. The framework exploits partial annotations, weak supervision, and realistic estimates of class and section-specific annotation effort in order to greatly reduce the time it takes to produce accurate segmentations for large histological datasets. Although both frameworks have been created targeting histological datasets, they have been successfully applied to other big bioimaging datasets, reducing labelling effort by up to 60−70% without compromising accuracy

    Brain Computations and Connectivity [2nd edition]

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    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

    Deep Reinforcement Learning in Surgical Robotics: Enhancing the Automation Level

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    Surgical robotics is a rapidly evolving field that is transforming the landscape of surgeries. Surgical robots have been shown to enhance precision, minimize invasiveness, and alleviate surgeon fatigue. One promising area of research in surgical robotics is the use of reinforcement learning to enhance the automation level. Reinforcement learning is a type of machine learning that involves training an agent to make decisions based on rewards and punishments. This literature review aims to comprehensively analyze existing research on reinforcement learning in surgical robotics. The review identified various applications of reinforcement learning in surgical robotics, including pre-operative, intra-body, and percutaneous procedures, listed the typical studies, and compared their methodologies and results. The findings show that reinforcement learning has great potential to improve the autonomy of surgical robots. Reinforcement learning can teach robots to perform complex surgical tasks, such as suturing and tissue manipulation. It can also improve the accuracy and precision of surgical robots, making them more effective at performing surgeries

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    Simulation Method for the Physical Deformation of a Three-Dimensional Soft Body in Augmented Reality-Based External Ventricular Drainage

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    Objectives Intraoperative navigation reduces the risk of major complications and increases the likelihood of optimal surgical outcomes. This paper presents an augmented reality (AR)-based simulation technique for ventriculostomy that visualizes brain deformations caused by the movements of a surgical instrument in a three-dimensional brain model. This is achieved by utilizing a position-based dynamics (PBD) physical deformation method on a preoperative brain image. Methods An infrared camera-based AR surgical environment aligns the real-world space with a virtual space and tracks the surgical instruments. For a realistic representation and reduced simulation computation load, a hybrid geometric model is employed, which combines a high-resolution mesh model and a multiresolution tetrahedron model. Collision handling is executed when a collision between the brain and surgical instrument is detected. Constraints are used to preserve the properties of the soft body and ensure stable deformation. Results The experiment was conducted once in a phantom environment and once in an actual surgical environment. The tasks of inserting the surgical instrument into the ventricle using only the navigation information presented through the smart glasses and verifying the drainage of cerebrospinal fluid were evaluated. These tasks were successfully completed, as indicated by the drainage, and the deformation simulation speed averaged 18.78 fps. Conclusions This experiment confirmed that the AR-based method for external ventricular drain surgery was beneficial to clinicians

    Grounded Semantic Reasoning for Robotic Interaction with Real-World Objects

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    Robots are increasingly transitioning from specialized, single-task machines to general-purpose systems that operate in unstructured environments, such as homes, offices, and warehouses. In these real-world domains, robots need to manipulate novel objects while adapting to changes in environments and goals. Semantic knowledge, which concisely describes target domains with symbols, can potentially reveal the meaningful patterns shared between problems and environments. However, existing robots are yet to effectively reason about semantic data encoding complex relational knowledge or jointly reason about symbolic semantic data and multimodal data pertinent to robotic manipulation (e.g., object point clouds, 6-DoF poses, and attributes detected with multimodal sensing). This dissertation develops semantic reasoning frameworks capable of modeling complex semantic knowledge grounded in robot perception and action. We show that grounded semantic reasoning enables robots to more effectively perceive, model, and interact with objects in real-world environments. Specifically, this dissertation makes the following contributions: (1) a survey providing a unified view for the diversity of works in the field by formulating semantic reasoning as the integration of knowledge sources, computational frameworks, and world representations; (2) a method for predicting missing relations in large-scale knowledge graphs by leveraging type hierarchies of entities, effectively avoiding ambiguity while maintaining generalization of multi-hop reasoning patterns; (3) a method for predicting unknown properties of objects in various environmental contexts, outperforming prior knowledge graph and statistical relational learning methods due to the use of n-ary relations for modeling object properties; (4) a method for purposeful robotic grasping that accounts for a broad range of contexts (including object visual affordance, material, state, and task constraint), outperforming existing approaches in novel contexts and for unknown objects; (5) a systematic investigation into the generalization of task-oriented grasping that includes a benchmark dataset of 250k grasps, and a novel graph neural network that incorporates semantic relations into end-to-end learning of 6-DoF grasps; (6) a method for rearranging novel objects into semantically meaningful spatial structures based on high-level language instructions, more effectively capturing multi-object spatial constraints than existing pairwise spatial representations; (7) a novel planning-inspired approach that iteratively optimizes placements of partially observed objects subject to both physical constraints and semantic constraints inferred from language instructions.Ph.D

    Control and Estimation Methods Towards Safe Robot-assisted Eye Surgery

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    Vitreoretinal surgery is among the most delicate surgical tasks in which physiological hand tremor may severely diminish surgeon performance and put the eye at high risk of injury. Unerring targeting accuracy is required to perform precise operations on micro-scale tissues. Tool tip to tissue interaction forces are usually below human tactile perception, which may result in exertion of excessive forces to the retinal tissue leading to irreversible damages. Notable challenges during retinal surgery lend themselves to robotic assistance which has proven beneficial in providing a safe steady-hand manipulation. Efficient assistance from the robots heavily relies on accurate sensing and intelligent control algorithms of important surgery states and situations (e.g. instrument tip position measurements and control of interaction forces). This dissertation provides novel control and state estimation methods to improve safety during robot-assisted eye surgery. The integration of robotics into retinal microsurgery leads to a reduction in surgeon perception of tool-to-tissue forces at sclera. This blunting of human tactile sensory input, which is due to the inflexible inertia of the robot, is a potential iatrogenic risk during robotic eye surgery. To address this issue, a sensorized surgical instrument equipped with Fiber Bragg Grating (FBG) sensors, which is capable of measuring the sclera forces and instrument insertion depth into the eye, is integrated to the Steady-Hand Eye Robot (SHER). An adaptive control scheme is then customized and implemented on the robot that is intended to autonomously mitigate the risk of unsafe scleral forces and excessive insertion of the instrument. Various preliminary and multi-user clinician studies are then conducted to evaluate the effectiveness of the control method during mock retinal surgery procedures. In addition, due to inherent flexibility and the resulting deflection of eye surgical instruments as well as the need for targeting accuracy, we have developed a method to enhance deflected instrument tip position estimation. Using an iterative method and microscope data, we develop a calibration- and registration-independent (RI) framework to provide online estimates of the instrument stiffness (least squares and adaptive). The estimations are then combined with a state-space model for tip position evolution obtained based on the forward kinematics (FWK) of the robot and FBG sensor measurements. This is accomplished using a Kalman Filtering (KF) approach to improve the instrument tip position estimation during robotic surgery. The entire framework is independent of camera-to-robot coordinate frame registration and is evaluated during various phantom experiments to demonstrate its effectiveness

    Encyclopedia of In — Betweenness

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    The dissertation Encyclopedia of In-Betweenness. An Exploration of a Collective Artistic Research Practice presents art as a socially prominent phenomenon that is always in a state of becoming. It suggests that art is on the front line perceiving new emerging ideas and ideologies while it also impacts and creates them. This means that art is obliged to seek what we, in fact, cannot yet know. The thesis has two main research questions. It explores how art can be a way to approach the unknown, and how it can be a tool for societal research and change. By creating art, we create societies. This is an immense task, and this dissertation explores the possibilities and responsibilities of it. Working towards the new always means working with the unknown, being in process and in an in-between state of becoming. To present in-betweenness, processes and becoming - things that are not known to us - new forms and methodologies are needed. Mapping the entanglements of the contemporary art world the thesis provides new perspectives on the relational nature of our being and ends up documenting a turn in the contemporary art world: how collective practices, site-specific and process-led approaches have emerged from the margins to the mainstream. The thesis documents how collective art projects can function as research platforms providing new knowledge and places for encounters. This study, positioned in the field of artistic research, uses exhibition making and curating as methods. By creating a network of varied knowledge, from the analysis of past projects to the diversification of theoretical and philosophical references, this dissertation intends to present how everything is in process and everything is symbiotic. The form of the dissertation – an encyclopedia with taxonomic colour-coding – is part of the methodology. It adds one layer to the domino effect of projects playing with known forms and questions of the unknown. Because artistic research operates with forms and experiences, these methods are part of the mediation: the in-betweenness and process-oriented approach defines the form and reading. The form follows the logic of the over 20 collaborative projects realized by the author during the past ten years presented and analyzed in the thesis. Deconstructing familiar concepts from “biennial” to “world expo” and “encyclopedia” helps to explore the unfamiliar and makes hidden structures visible. The thematically colour-coded entries map the current discourses, but they also point out the hierarchical conception of knowledge itself and the absurdity of taxonomic processes. It leads to the question of control, and the fact that eventually one can never control how something is encountered, experienced, and interpreted.Encyclopedia of In-Betweenness. An Exploration of a Collective Artistic Research Practice vĂ€itöskirja kĂ€sittelee taidetta sosiaalisesti merkittĂ€vĂ€nĂ€ ilmiönĂ€, jonka olemus perustuu jatkuvalle muutokselle ja sille, mikĂ€ on vasta tuloillaan. Teos ehdottaa, ettĂ€ taide on aina etulinjassa aistimassa uusia aatteita, ideoita ja ilmiöitĂ€, samalla luoden niitĂ€ ja vaikuttaen niiden syntyyn. TĂ€mĂ€ tarkoittaa, ettĂ€ taide vĂ€istĂ€mĂ€ttĂ€ etsii sitĂ€, mitĂ€ itseasiassa emme edes voi vielĂ€ tietÀÀ. Tutkimus lĂ€htee liikkeelle kahdesta tutkimuskysymyksestĂ€: miten taide voi lĂ€hestyĂ€ tuntematonta, ja miten se voi tuottaa ja tutkia sosiaalisia tilanteita ja muutoksia. Luomalla taidetta luomme yhteiskuntia. TĂ€mĂ€ on haaste, johon vĂ€itöskirja vastaa kĂ€sittelemĂ€llĂ€ taiteen mahdollisuuksia ja velvollisuuksia. Uutta kohti toimiminen tarkoittaa aina tuntemattoman kanssa työskentelyĂ€, prosessissa ja vĂ€litilassa olemista. TĂ€llaisten tuloillaan olevien ja vĂ€lissĂ€ olevien asioiden, jotka eivĂ€t vielĂ€ ole tiedossamme, tunnettuja tai meille tuttuja, esittĂ€minen vaatii uudenlaisia tutkimuksen menetelmiĂ€ ja muotoja. Kartoittaessaan tĂ€llaisia nykytaiteen kentĂ€n ilmiöitĂ€ tutkimus tarjoaa uusia nĂ€kökulmia olemassaolomme suhteellisuuteen – miten se aina tapahtuu suhteessa toisiin – pÀÀtyen samalla dokumentoimaan kuinka kollektiiviset, paikkasidonnaiset, ja prosessilĂ€htöiset tekemisen tavat ovat nousseet marginaalista keskiöön. Tutkimus dokumentoi sitĂ€, miten kollektiiviset taideprojektit voivat toimia tutkimusalustoina, tuottaa uutta tietoa, ja mahdollisuuksia kohtaamisille. Taiteellisen tutkimuksen alueelle sijoittuva vĂ€itös kĂ€yttÀÀ kuratointia ja nĂ€yttelyiden tekemistĂ€ tutkimusmenetelminÀÀn. Se luo erilaisten tietĂ€misen tapojen ja teorioiden verkoston yhdistĂ€mĂ€llĂ€ taiteellisen työskentelyn kautta hankittua tietoa teoreettiseen ja filosofiseen aineistoon, osoittaen, kuinka kaikki on prosessissa ja symbioottista. VĂ€itöskirjan muoto, vĂ€rikoodattua taksonomiaa hyödyntĂ€vĂ€ ensyklopedia, lisÀÀ uuden kerroksen projektien dominoefektiin, jossa yksi kysymys synnyttÀÀ seuraavan ja jĂ€nnite syntyy tunnetun ja tuntemattoman vĂ€lissĂ€ toimimisesta. Taiteellinen tutkimus toimii muodon ja kokemuksen alueella, joten nĂ€mĂ€ metodit ovat osa sitĂ€, miten tutkimus tulee vĂ€litetyksi. Muutos ja prosessilĂ€htöisyys vaikuttavat lukemiseen ja muotoon, joka noudattaa tutkimuksessa esitettyjen ja analysoitujen projektien logiikkaa. Tuttuja kĂ€sitteitĂ€ ja konsepteja, kuten ”biennaali”, ”maailmannĂ€yttely” tai ”ensyklopedia” dekonstruoimalla hankkeet ovat mahdollistaneet tuntemattoman lĂ€hestymisen ja piilotettujen rakenteiden nĂ€kyvĂ€ksi tekemisen. Viimeisen kymmenen vuoden aikana kollektiivisesti toteutetut yli kaksikymmentĂ€ kokeellista ja tutkimuksellista projektia muodostavat jatkumon, jonka kautta taiteellisen tutkimuksen mahdollisuuksia tarkastellaan niiden tekijĂ€positiosta kĂ€sin. Temaattisesti vĂ€rikoodatut luvut tallentavat ajankohtaisia keskusteluja, mutta samalla osoittavat tiedon hierarkkisen luonteen ja luokittelun absurdiuden. TĂ€tĂ€ kautta tutkimus pÀÀtyy kĂ€sittelemÀÀn myös kontrollin ja hallinnan kysymyksiĂ€ ja sitĂ€, miten lopulta emme kuitenkaan voi ennalta mÀÀrĂ€tĂ€ miten joku asia tulee koetuksi, kohdatuksi ja tulkituksi

    A review of commercialisation mechanisms for carbon dioxide removal

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    The deployment of carbon dioxide removal (CDR) needs to be scaled up to achieve net zero emission pledges. In this paper we survey the policy mechanisms currently in place globally to incentivise CDR, together with an estimate of what different mechanisms are paying per tonne of CDR, and how those costs are currently distributed. Incentive structures are grouped into three structures, market-based, public procurement, and fiscal mechanisms. We find the majority of mechanisms currently in operation are underresourced and pay too little to enable a portfolio of CDR that could support achievement of net zero. The majority of mechanisms are concentrated in market-based and fiscal structures, specifically carbon markets and subsidies. While not primarily motivated by CDR, mechanisms tend to support established afforestation and soil carbon sequestration methods. Mechanisms for geological CDR remain largely underdeveloped relative to the requirements of modelled net zero scenarios. Commercialisation pathways for CDR require suitable policies and markets throughout the projects development cycle. Discussion and investment in CDR has tended to focus on technology development. Our findings suggest that an equal or greater emphasis on policy innovation may be required if future requirements for CDR are to be met. This study can further support research and policy on the identification of incentive gaps and realistic potential for CDR globally
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