36 research outputs found
ADVANCED INTRAOPERATIVE IMAGE REGISTRATION FOR PLANNING AND GUIDANCE OF ROBOT-ASSISTED SURGERY
Robot-assisted surgery offers improved accuracy, precision, safety, and workflow for a variety of surgical procedures spanning different surgical contexts (e.g., neurosurgery, pulmonary interventions, orthopaedics). These systems can assist with implant placement, drilling, bone resection, and biopsy while reducing human errors (e.g., hand tremors and limited dexterity) and easing the workflow of such tasks. Furthermore, such systems can reduce radiation dose to the clinician in fluoroscopically-guided procedures since many robots can perform their task in the imaging field-of-view (FOV) without the surgeon.
Robot-assisted surgery requires (1) a preoperative plan defined relative to the patient that instructs the robot to perform a task, (2) intraoperative registration of the patient to transform the planning data into the intraoperative space, and (3) intraoperative registration of the robot to the patient to guide the robot to execute the plan. However, despite the operational improvements achieved using robot-assisted surgery, there are geometric inaccuracies and significant challenges to workflow associated with (1-3) that impact widespread adoption.
This thesis aims to address these challenges by using image registration to plan and guide robot- assisted surgical (RAS) systems to encourage greater adoption of robotic-assistance across surgical contexts (in this work, spinal neurosurgery, pulmonary interventions, and orthopaedic trauma). The proposed methods will also be compatible with diverse imaging and robotic platforms (including low-cost systems) to improve the accessibility of RAS systems for a wide range of hospital and use settings.
This dissertation advances important components of image-guided, robot-assisted surgery, including: (1) automatic target planning using statistical models and surgeon-specific atlases for application in spinal neurosurgery; (2) intraoperative registration and guidance of a robot to the planning data using 3D-2D image registration (i.e., an “image-guided robot”) for assisting pelvic orthopaedic trauma; (3) advanced methods for intraoperative registration of planning data in deformable anatomy for guiding pulmonary interventions; and (4) extension of image-guided robotics in a piecewise rigid, multi-body context in which the robot directly manipulates anatomy for assisting ankle orthopaedic trauma
Rapid development of applications for the interactive visual analysis of multimodal medical data
Multimodale medizinische Volumendaten gewinnen zunehmend an Verbreitung.
Wir diskutieren verschiedene interaktive Applikationen welche den Nutzer bei der Analyse dieser Daten unterstützen.
Alle Applikationen basieren auf Erweiterungen des Voreen Frameworks, welche ebenfalls in dieser Dissertation diskutiert werden. With multimodal volumetric medical data sets becoming more common due to
the increasing availability of scanning hardware, software for the
visualization and analysis of such data sets needs to become more
efficient as well in order to prevent overloading the user with data.
This dissertation presents several interactive techniques for the visual
analysis of medical volume data.
All applications are based on extensions to the Voreen volume rendering
framework, which we will discuss first.
Since visual analysis applications are interactive by definition, we
propose a general-purpose navigation technique for volume data.
Next, we discuss our concepts for the interactive planning of brain
tumor resections.
Finally, we present two systems designed to work with images of
vasculature.
First, we discuss an interactive vessel segmentation system enabling an
efficient, visually supported workflow.
Second, we propose an application for the visual analysis of PET tracer
uptake along vessels
Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning
Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons
Mobile Wound Assessment and 3D Modeling from a Single Image
The prevalence of camera-enabled mobile phones have made mobile wound assessment a viable treatment option for millions of previously difficult to reach patients. We have designed a complete mobile wound assessment platform to ameliorate the many challenges related to chronic wound care. Chronic wounds and infections are the most severe, costly and fatal types of wounds, placing them at the center of mobile wound assessment. Wound physicians assess thousands of single-view wound images from all over the world, and it may be difficult to determine the location of the wound on the body, for example, if the wound is taken at close range. In our solution, end-users capture an image of the wound by taking a picture with their mobile camera. The wound image is segmented and classified using modern convolution neural networks, and is stored securely in the cloud for remote tracking. We use an interactive semi-automated approach to allow users to specify the location of the wound on the body. To accomplish this we have created, to the best our knowledge, the first 3D human surface anatomy labeling system, based off the current NYU and Anatomy Mapper labeling systems. To interactively view wounds in 3D, we have presented an efficient projective texture mapping algorithm for texturing wounds onto a 3D human anatomy model. In so doing, we have demonstrated an approach to 3D wound reconstruction that works even for a single wound image
Navigation system based in motion tracking sensor for percutaneous renal access
Tese de Doutoramento em Engenharia BiomédicaMinimally-invasive kidney interventions are daily performed to diagnose and treat several renal
diseases. Percutaneous renal access (PRA) is an essential but challenging stage for most of these
procedures, since its outcome is directly linked to the physician’s ability to precisely visualize and
reach the anatomical target.
Nowadays, PRA is always guided with medical imaging assistance, most frequently using X-ray
based imaging (e.g. fluoroscopy). Thus, radiation on the surgical theater represents a major risk to
the medical team, where its exclusion from PRA has a direct impact diminishing the dose exposure
on both patients and physicians.
To solve the referred problems this thesis aims to develop a new hardware/software framework
to intuitively and safely guide the surgeon during PRA planning and puncturing.
In terms of surgical planning, a set of methodologies were developed to increase the certainty of
reaching a specific target inside the kidney. The most relevant abdominal structures for PRA were
automatically clustered into different 3D volumes. For that, primitive volumes were merged as a local
optimization problem using the minimum description length principle and image statistical
properties. A multi-volume Ray Cast method was then used to highlight each segmented volume.
Results show that it is possible to detect all abdominal structures surrounding the kidney, with the
ability to correctly estimate a virtual trajectory.
Concerning the percutaneous puncturing stage, either an electromagnetic or optical solution
were developed and tested in multiple in vitro, in vivo and ex vivo trials. The optical tracking solution
aids in establishing the desired puncture site and choosing the best virtual puncture trajectory.
However, this system required a line of sight to different optical markers placed at the needle base,
limiting the accuracy when tracking inside the human body. Results show that the needle tip can
deflect from its initial straight line trajectory with an error higher than 3 mm. Moreover, a complex
registration procedure and initial setup is needed.
On the other hand, a real-time electromagnetic tracking was developed. Hereto, a catheter
was inserted trans-urethrally towards the renal target. This catheter has a position and orientation
electromagnetic sensor on its tip that function as a real-time target locator. Then, a needle integrating a similar sensor is used. From the data provided by both sensors, one computes a virtual puncture
trajectory, which is displayed in a 3D visualization software. In vivo tests showed a median renal and
ureteral puncture times of 19 and 51 seconds, respectively (range 14 to 45 and 45 to 67 seconds).
Such results represent a puncture time improvement between 75% and 85% when comparing to
state of the art methods.
3D sound and vibrotactile feedback were also developed to provide additional information about
the needle orientation. By using these kind of feedback, it was verified that the surgeon tends to
follow a virtual puncture trajectory with a reduced amount of deviations from the ideal trajectory,
being able to anticipate any movement even without looking to a monitor. Best results show that 3D
sound sources were correctly identified 79.2 ± 8.1% of times with an average angulation error of
10.4º degrees. Vibration sources were accurately identified 91.1 ± 3.6% of times with an average
angulation error of 8.0º degrees.
Additionally to the EMT framework, three circular ultrasound transducers were built with a needle
working channel. One explored different manufacture fabrication setups in terms of the piezoelectric
materials, transducer construction, single vs. multi array configurations, backing and matching
material design. The A-scan signals retrieved from each transducer were filtered and processed to
automatically detect reflected echoes and to alert the surgeon when undesirable anatomical
structures are in between the puncture path. The transducers were mapped in a water tank and
tested in a study involving 45 phantoms. Results showed that the beam cross-sectional area
oscillates around the ceramics radius and it was possible to automatically detect echo signals in
phantoms with length higher than 80 mm.
Hereupon, it is expected that the introduction of the proposed system on the PRA procedure,
will allow to guide the surgeon through the optimal path towards the precise kidney target, increasing
surgeon’s confidence and reducing complications (e.g. organ perforation) during PRA. Moreover, the
developed framework has the potential to make the PRA free of radiation for both patient and surgeon
and to broad the use of PRA to less specialized surgeons.Intervenções renais minimamente invasivas são realizadas diariamente para o tratamento e
diagnóstico de várias doenças renais. O acesso renal percutâneo (ARP) é uma etapa essencial e
desafiante na maior parte destes procedimentos. O seu resultado encontra-se diretamente
relacionado com a capacidade do cirurgião visualizar e atingir com precisão o alvo anatómico.
Hoje em dia, o ARP é sempre guiado com recurso a sistemas imagiológicos, na maior parte
das vezes baseados em raios-X (p.e. a fluoroscopia). A radiação destes sistemas nas salas cirúrgicas
representa um grande risco para a equipa médica, aonde a sua remoção levará a um impacto direto
na diminuição da dose exposta aos pacientes e cirurgiões.
De modo a resolver os problemas existentes, esta tese tem como objetivo o desenvolvimento
de uma framework de hardware/software que permita, de forma intuitiva e segura, guiar o cirurgião
durante o planeamento e punção do ARP.
Em termos de planeamento, foi desenvolvido um conjunto de metodologias de modo a
aumentar a eficácia com que o alvo anatómico é alcançado. As estruturas abdominais mais
relevantes para o procedimento de ARP, foram automaticamente agrupadas em volumes 3D, através
de um problema de optimização global com base no princípio de “minimum description length” e
propriedades estatísticas da imagem. Por fim, um procedimento de Ray Cast, com múltiplas funções
de transferência, foi utilizado para enfatizar as estruturas segmentadas. Os resultados mostram que
é possível detetar todas as estruturas abdominais envolventes ao rim, com a capacidade para
estimar corretamente uma trajetória virtual.
No que diz respeito à fase de punção percutânea, foram testadas duas soluções de deteção
de movimento (ótica e eletromagnética) em múltiplos ensaios in vitro, in vivo e ex vivo. A solução
baseada em sensores óticos ajudou no cálculo do melhor ponto de punção e na definição da melhor
trajetória a seguir. Contudo, este sistema necessita de uma linha de visão com diferentes
marcadores óticos acoplados à base da agulha, limitando a precisão com que a agulha é detetada
no interior do corpo humano. Os resultados indicam que a agulha pode sofrer deflexões à medida
que vai sendo inserida, com erros superiores a 3 mm.
Por outro lado, foi desenvolvida e testada uma solução com base em sensores
eletromagnéticos. Para tal, um cateter que integra um sensor de posição e orientação na sua ponta, foi colocado por via trans-uretral junto do alvo renal. De seguida, uma agulha, integrando um sensor
semelhante, é utilizada para a punção percutânea. A partir da diferença espacial de ambos os
sensores, é possível gerar uma trajetória de punção virtual. A mediana do tempo necessário para
puncionar o rim e ureter, segundo esta trajetória, foi de 19 e 51 segundos, respetivamente
(variações de 14 a 45 e 45 a 67 segundos). Estes resultados representam uma melhoria do tempo
de punção entre 75% e 85%, quando comparados com o estado da arte dos métodos atuais.
Além do feedback visual, som 3D e feedback vibratório foram explorados de modo a fornecer
informações complementares da posição da agulha. Verificou-se que com este tipo de feedback, o
cirurgião tende a seguir uma trajetória de punção com desvios mínimos, sendo igualmente capaz
de antecipar qualquer movimento, mesmo sem olhar para o monitor. Fontes de som e vibração
podem ser corretamente detetadas em 79,2 ± 8,1% e 91,1 ± 3,6%, com erros médios de angulação
de 10.4º e 8.0 graus, respetivamente.
Adicionalmente ao sistema de navegação, foram também produzidos três transdutores de
ultrassom circulares com um canal de trabalho para a agulha. Para tal, foram exploradas diferentes
configurações de fabricação em termos de materiais piezoelétricos, transdutores multi-array ou
singulares e espessura/material de layers de suporte. Os sinais originados em cada transdutor
foram filtrados e processados de modo a detetar de forma automática os ecos refletidos, e assim,
alertar o cirurgião quando existem variações anatómicas ao longo do caminho de punção. Os
transdutores foram mapeados num tanque de água e testados em 45 phantoms. Os resultados
mostraram que o feixe de área em corte transversal oscila em torno do raio de cerâmica, e que os
ecos refletidos são detetados em phantoms com comprimentos superiores a 80 mm.
Desta forma, é expectável que a introdução deste novo sistema a nível do ARP permitirá
conduzir o cirurgião ao longo do caminho de punção ideal, aumentado a confiança do cirurgião e
reduzindo possíveis complicações (p.e. a perfuração dos órgãos). Além disso, de realçar que este
sistema apresenta o potencial de tornar o ARP livre de radiação e alarga-lo a cirurgiões menos
especializados.The present work was only possible thanks to the support by the Portuguese Science and
Technology Foundation through the PhD grant with reference SFRH/BD/74276/2010 funded by
FCT/MEC (PIDDAC) and by Fundo Europeu de Desenvolvimento Regional (FEDER), Programa
COMPETE - Programa Operacional Factores de Competitividade (POFC) do QREN
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
Learning-based depth and pose prediction for 3D scene reconstruction in endoscopy
Colorectal cancer is the third most common cancer worldwide. Early detection and treatment of pre-cancerous tissue during colonoscopy is critical to improving prognosis. However, navigating within the colon and inspecting the endoluminal tissue comprehensively are challenging, and success in both varies based on the endoscopist's skill and experience. Computer-assisted interventions in colonoscopy show much promise in improving navigation and inspection. For instance, 3D reconstruction of the colon during colonoscopy could promote more thorough examinations and increase adenoma detection rates which are associated with improved survival rates. Given the stakes, this thesis seeks to advance the state of research from feature-based traditional methods closer to a data-driven 3D reconstruction pipeline for colonoscopy.
More specifically, this thesis explores different methods that improve subtasks of learning-based 3D reconstruction. The main tasks are depth prediction and camera pose estimation. As training data is unavailable, the author, together with her co-authors, proposes and publishes several synthetic datasets and promotes domain adaptation models to improve applicability to real data. We show, through extensive experiments, that our depth prediction methods produce more robust results than previous work. Our pose estimation network trained on our new synthetic data outperforms self-supervised methods on real sequences. Our box embeddings allow us to interpret the geometric relationship and scale difference between two images of the same surface without the need for feature matches that are often unobtainable in surgical scenes. Together, the methods introduced in this thesis help work towards a complete, data-driven 3D reconstruction pipeline for endoscopy
Shape segmentation and retrieval based on the skeleton cut space
3D vormverzamelingen groeien snel in veel toepassingsgebieden. Om deze effectief te kunnen gebruiken bij modelleren, simuleren, of 3D contentontwikkeling moet men 3D vormen verwerken. Voorbeelden hiervan zijn het snijden van een vorm in zijn natuurlijke onderdelen (ook bekend als segmentatie), en het vinden van vormen die lijken op een gegeven model in een grote vormverzameling (ook bekend als opvraging). Dit proefschrift presenteert nieuwe methodes voor 3D vormsegmentatie en vormopvraging die gebaseerd zijn op het zogenaamde oppervlakskelet van een 3D vorm. Hoewel allang bekend, dergelijke skeletten kunnen alleen sinds kort snel, robuust, en bijna automatisch berekend worden. Deze ontwikkelingen stellen ons in staat om oppervlakskeletten te gebruiken om vormen te karakteriseren en analyseren zodat operaties zoals segmentatie en opvraging snel en automatisch gedaan kunnen worden. We vergelijken onze nieuwe methodes met moderne methodes voor dezelfde doeleinden en laten zien dat ons aanpak kwalitatief betere resultaten kan produceren. Ten slotte presenteren wij een nieuwe methode om oppervlakskeletten te extraheren die is veel simpeler dan, en heeft vergelijkbare snelheid met, de beste technieken in zijn klasse. Samenvattend, dit proefschrift laat zien hoe men een complete workflow kan implementeren voor het segmenteren en opvragen van 3D vormen gebruik makend van oppervlakskeletten alleen