Este trabalho apresenta um estudo sobre segmentação de volumes médicos e uma solução para se obter malhas poligonais de pacientes específicos para uso em simulações de cirurgia. Malhas de pacientes específicos são importantes para planejamento de intervenções cirúrgicas e permitem uma melhor visualização de condições patológicas em um paciente, coisa não obtível em malhas geradas artisticamente. Nós analisamos quais são os fatores complicantes para se obter estas malhas de um paciente específico usando apenas imagens médicas obtidas em exames padrões. Para isso, nós revisamos diversos métodos existentes para segmentação de volumes médicos. Isso nos levou a definir os problemas com as técnicas existentes, e a desenvolver um método que não sofra destes problemas, utilizando pouca interação humana e não tendo dependências de mais dados que não o exame do paciente. Nosso alvo para obter malhas especificas foram órgãos de tecido mole, que são um caso especialmente complicado da área, graças a várias questões relacionadas às imagens médicas e à anatomia humana. Atacamos esse problema aplicando modificações geométricas em malhas especiais, que deformam até atingir a forma dos órgãos que se deseja segmentar. Os resultados mostram que nossa técnica conseguiu obter malhas específicas de pacientes a partir de volumes médicos com qualidade superior a de outros algoritmos de mesma classe. Graças a simplicidade do método desenvolvido, nossos resultados são facilmente implementáveis e reproduzidos. / This work presents a study about medical-volume segmentation and a solution to generate patient-specific meshes to use in patient-specific surgery simulations. Patientspecific meshes are useful assets for surgery planning and to allow better visualization of certain pathological conditions of a given patient, which are not obtainable by artistically designed meshes. We analyzed what are the complications to obtain a patient-specific mesh using only standard medical imagery exams. For that, we reviewed several medical volume segmentation techniques. It led us to define the problems within the existing techniques and to develop a method that does not suffer from these problems, with the least possible user interaction or relying on any other data other then the patient exam. Our target for obtaining specific meshes were soft tissue organs, which are a specially complicated case due to various issues related to the medical images and human anatomy. This is accomplished by geometrical operations over special meshes that deform until achieving the shape of the desired organ. Results show that our technique was able to obtain patient-specific meshes from medical images with superior quality than algorithms of the same class. Thanks to the simplicity of the developed approach, its also easy to implement and to reproduce our obtained results.

Este trabalho apresenta um estudo sobre segmentação de volumes médicos e uma solução para se obter malhas poligonais de pacientes específicos para uso em simulações de cirurgia. Malhas de pacientes específicos são importantes para planejamento de intervenções cirúrgicas e permitem uma melhor visualização de condições patológicas em um paciente, coisa não obtível em malhas geradas artisticamente. Nós analisamos quais são os fatores complicantes para se obter estas malhas de um paciente específico usando apenas imagens médicas obtidas em exames padrões. Para isso, nós revisamos diversos métodos existentes para segmentação de volumes médicos. Isso nos levou a definir os problemas com as técnicas existentes, e a desenvolver um método que não sofra destes problemas, utilizando pouca interação humana e não tendo dependências de mais dados que não o exame do paciente. Nosso alvo para obter malhas especificas foram órgãos de tecido mole, que são um caso especialmente complicado da área, graças a várias questões relacionadas às imagens médicas e à anatomia humana. Atacamos esse problema aplicando modificações geométricas em malhas especiais, que deformam até atingir a forma dos órgãos que se deseja segmentar. Os resultados mostram que nossa técnica conseguiu obter malhas específicas de pacientes a partir de volumes médicos com qualidade superior a de outros algoritmos de mesma classe. Graças a simplicidade do método desenvolvido, nossos resultados são facilmente implementáveis e reproduzidos. / This work presents a study about medical-volume segmentation and a solution to generate patient-specific meshes to use in patient-specific surgery simulations. Patientspecific meshes are useful assets for surgery planning and to allow better visualization of certain pathological conditions of a given patient, which are not obtainable by artistically designed meshes. We analyzed what are the complications to obtain a patient-specific mesh using only standard medical imagery exams. For that, we reviewed several medical volume segmentation techniques. It led us to define the problems within the existing techniques and to develop a method that does not suffer from these problems, with the least possible user interaction or relying on any other data other then the patient exam. Our target for obtaining specific meshes were soft tissue organs, which are a specially complicated case due to various issues related to the medical images and human anatomy. This is accomplished by geometrical operations over special meshes that deform until achieving the shape of the desired organ. Results show that our technique was able to obtain patient-specific meshes from medical images with superior quality than algorithms of the same class. Thanks to the simplicity of the developed approach, its also easy to implement and to reproduce our obtained results.

Este trabalho apresenta um estudo sobre segmentação de volumes médicos e uma solução para se obter malhas poligonais de pacientes específicos para uso em simulações de cirurgia. Malhas de pacientes específicos são importantes para planejamento de intervenções cirúrgicas e permitem uma melhor visualização de condições patológicas em um paciente, coisa não obtível em malhas geradas artisticamente. Nós analisamos quais são os fatores complicantes para se obter estas malhas de um paciente específico usando apenas imagens médicas obtidas em exames padrões. Para isso, nós revisamos diversos métodos existentes para segmentação de volumes médicos. Isso nos levou a definir os problemas com as técnicas existentes, e a desenvolver um método que não sofra destes problemas, utilizando pouca interação humana e não tendo dependências de mais dados que não o exame do paciente. Nosso alvo para obter malhas especificas foram órgãos de tecido mole, que são um caso especialmente complicado da área, graças a várias questões relacionadas às imagens médicas e à anatomia humana. Atacamos esse problema aplicando modificações geométricas em malhas especiais, que deformam até atingir a forma dos órgãos que se deseja segmentar. Os resultados mostram que nossa técnica conseguiu obter malhas específicas de pacientes a partir de volumes médicos com qualidade superior a de outros algoritmos de mesma classe. Graças a simplicidade do método desenvolvido, nossos resultados são facilmente implementáveis e reproduzidos. / This work presents a study about medical-volume segmentation and a solution to generate patient-specific meshes to use in patient-specific surgery simulations. Patientspecific meshes are useful assets for surgery planning and to allow better visualization of certain pathological conditions of a given patient, which are not obtainable by artistically designed meshes. We analyzed what are the complications to obtain a patient-specific mesh using only standard medical imagery exams. For that, we reviewed several medical volume segmentation techniques. It led us to define the problems within the existing techniques and to develop a method that does not suffer from these problems, with the least possible user interaction or relying on any other data other then the patient exam. Our target for obtaining specific meshes were soft tissue organs, which are a specially complicated case due to various issues related to the medical images and human anatomy. This is accomplished by geometrical operations over special meshes that deform until achieving the shape of the desired organ. Results show that our technique was able to obtain patient-specific meshes from medical images with superior quality than algorithms of the same class. Thanks to the simplicity of the developed approach, its also easy to implement and to reproduce our obtained results.

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Virtual surgery planning systems have demonstrated great potential to help surgeons achieve a better functional and aesthetic outcome for the patient, and at the same time reduce time in the operating room resulting in considerable cost savings. However, the two-dimensional tools employed in these systems today, such as a mouse and a conventional graphical display, are difficult to use for interaction with three-dimensional anatomical images. Therefore surgeons often outsource virtual planning which increases cost and lead time to surgery. Haptics relates to the sense of touch and haptic technology encompasses algorithms, software, and hardware designed to engage the sense of touch. To demonstrate how haptic technology in combination with stereo visualization can make cranio-maxillofacial surgery planning more efficient and easier to use, we describe our haptics-assisted surgery planning (HASP) system. HASP supports in-house virtual planning of reconstructions in complex trauma cases, and reconstructions with a fibula osteocutaneous free flap including bone, vessels, and soft-tissue in oncology cases. An integrated stable six degrees-of-freedom haptic attraction force model, snap-to-fit, supports semi-automatic alignment of virtual bone fragments in trauma cases. HASP has potential beyond this thesis as a teaching tool and also as a development platform for future research. In addition to HASP, we describe a surgical bone saw simulator with a novel hybrid haptic interface that combines kinesthetic and vibrotactile feedback to display both low frequency contact forces and realistic high frequency vibrations when a virtual saw blade comes in contact with a virtual bone model.  We also show that visuo-haptic co-location shortens the completion time, but does not improve the accuracy, in interaction tasks performed on two different visuo-haptic displays: one based on a holographic optical element and one based on a half-transparent mirror.  Finally, we describe two prototype hand-worn haptic interfaces that potentially may expand the interaction capabilities of the HASP system. In particular we evaluate two different types of piezo-electric motors, one walking quasi-static motor and one traveling-wave ultrasonic motor for actuating the interfaces.

Healthcare systems, driven by increased demand due to growth in chronic diseases and population, suffer from lack of staffing and facility resources. Many major hospitals have long waiting lists and have subsequently pushed their production close to maximum capacity due to the high demand for services. The consequences are lack of overview of the operations and lack of coordination between healthcare staff, which leads to treatment delays. Surgery planning or scheduling is an important part of production planning in hospitals, which is considered highly complex due to high variability and many decisions variables that need to be considered. Those responsible for surgery planning are often considered to lack the right tools to support them in evaluating the many different decision factors.   Simulation is a technology within the field of operations research which has been applied to aid with surgery planning problems and to look for process improvements. Many studies however use a simplified approach to the surgery planning, due to the complexities of the planning problem. Studies have further argued that surgery planning fails to consider downstream resources and the negative effects it has on utilization of those resources. This thesis is based on a case study at one of Sweden’s major hospitals and aims to explore how simulation could become a decision support to help with surgery planning and identifying what process improvements such a tool could be aimed at. The surgery planning decision making process is first analyzed using a hierarchical framework for hospitals’ production planning. The results were that the decision making process regarding patient flows needs to be improved by taking both a top-down and bottom-up strategy for better information flow and coordination. The study further concludes that improved coordination and information sharing are important factors to improve patient flow through the hospital, which could be supported by the usage of Discrete Event Simulation for decision making. The ideal decision support tool is however considered the simulation tool embedded with an online system to support bed management decisions which could increase patient throughput. Such a tool could help to decrease the demand for the hospital’s beds by discharging patients quicker. In addition, it could support the bottom-up strategy for coordination, while implementing a multi-method or hybrid simulation could further support the top-down part of the strategy.

Image segmentation plays an important role in medical image analysis. Many segmentation algorithms exist. Most of them produce data which are more or less not suitable for further surface extraction and anatomical modeling of human tissues. In this thesis, a novel segmentation technique based on the 3D Delaunay triangulation is proposed. A modified variational tetrahedral meshing approach is used to adapt a tetrahedral mesh to the underlying CT volumetric data, so that image edges are well approximated in the mesh. In order to classify tetrahedra into regions/tissues whose characteristics are similar, three different clustering schemes are presented. Finally, several methods for improving quality of the mesh and its adaptation to the image structure are also discussed.

Le cancer du sein est le cancer le plus fréquent chez les femmes. Il y a une multitude de solutions proposées concernant une éventuelle intervention médicale pour le cancer du sein ‐ une en particulier est la chirurgie mammaire conservatrice (tumoréctomie). Le but de la tumoréctomie est de parvenir à un contrôle local du cancer, ainsi que de préserver une forme du sein qui satisfait les besoins esthétiques de la femme. Bien que ces objectifs sont généralement atteint, il reste encore parfois des résultats inattendus,tels qu'une tumeur récurrence locale, ou des résultats cosmétiques insuffisants.L'objectif de cette thèse est de proposer une plateforme de calcul, qui contribue à la tumoréctomie. Cela comprend:1) Une étude de la dynamique de croissance des tumeurs du sein.2) Une étude sur la prédiction du contour du sein grâce a la chirurgie virtuelle.3) Un modèle de calcul de la forme finale du sein après cicatrisation. / Breast cancer is the most common cancer among women in the developed as well as the developing countries. There are a plethora of proposed solutions regarding possible medical interventions for breast cancer–one in particular is Breast Conserving Therapy (BCT). BCT comprises of complete surgical excision of the tumor (partialmastectomy), and post-operative radiotherapy for the remaining breast tissue. This is a feasible treatment for most women with breast cancer. The goal of BCT is toachieve local control of the cancer, as well as to preserve breast shape that appeases awoman’s cosmetic concerns. Although these goals are usually achieved, there are still occasional unexpected results, such as reexcision of the tumor due to a positive margin assessment, tumor local recurrence, unsatisfactory cosmetic results, and breastpain. Other than surgical experience and judgment, there are currently no toolswhich can predict the outcome of partial mastectomy on the contour and deformity of the treated breast. The objective of this dissertation is to propose computational framework, which contributes to BCT operations, this was achieve by exploring two areas.On the one hand we developed a multiscale model adapted for breast cancer tumor growth, ductal carcinoma in situ (DCIS). The model features included: nutrients growth limitation, wall degradation enzyme and HER2 chemical expression tumor phenotype. Our model successfully simulate some pattern of DCIS carcinoma.Among the interesting result we showed that the enzyme contributed to a greater tumor size and that when HER2 was over expressed, the growth limiting factor wasthe EGFR. On the other hand, we developed a virtual surgery box to simulate BCT surgery. The box will input MRI patient data and will output cosmetic and functional indicator to rate the impact of the surgery. It appears that stiffness of the tissue, resection radius as well as the lump quadrant location are the most sensitive parameters to the indicators. A healing model was also embedded to simulate the wound closure after resection, this model was stress dependent and illustrate anasymmetric wound closure progression.The tools developed in this research allows a new type of field convergence between the surgery and computation field. At the local level it will allow surgeons and patient to be able to communicate on the pertinence and necessity of performing alumpectomy surgery, enabling to anticipate the possible outcome of the operation.On the global aspect this type of tool gives birth to a new type of field: computational surgery, where computer scientist and surgeons work hand in hand to provide the best and the most reliable service to the patients.