|M.Sc. Candidate||Irene Testa|
|Supervisors||Elena De Momi, Alice Segato|
|Title||Neurosurgical Path Planner for Steerable Needle with RL and GAIL approaches|
The development of minimally invasive neurosurgery allows to reduce tissue disruption and thus morbidity. Indeed, this technique offers patients several benefits such as smaller incisions, faster recovery times and, in many cases, also offers a higher accuracy rate compared to traditional open surgery. The complex anatomical structure of the brain and the vulnerability of its tissues make diﬃcult to reach targets located in deep brain areas with rigid needles without causing damages to the adjacent tissues. For this reason, the European project EDEN2020 develops the prototype of a catheter characterized by four segments that slide one along another, generating an offset, that provides steering ability to this probe enabling the surgeon to maneuver around critical tissue to the target. Since a steerable catheter can only unfold its full potential when following the best trajectory within the brain, it is introduced the need of pre-operatively planning the path of the probe in regards of minimal length of the trajectory and maximum distance to healthy tissues, while respecting the kinematic constrains of the catheter. The aim of this thesis is to generate a reinforcement learning model to compute the optimal trajectories of the catheter in the brain. Starting from the application of Generative Adversarial Imitation Learning (GAIL) techniques on sets of demonstrations manually generated by surgeons with a 3D brain surgery simulator, the best trajectories are computed taking into account the anatomical constrains of the brain and the kinematic limits of the probe.
|M.Sc. Candidate||Sara Zucchelli|
|Supervisors||Elena De Momi, Alice Segato|
|Title||3D Motion Modeling and Control of a Steerable Needle for Keyhole Neurosurgery|
|Collaborations||Imperial College London|
Nowadays minimally invasive brain surgery is a growing field due to the exponentially increase of cerebral diseases. EDEN2020 European project proposes a change in the treatment of brain diseases, thanks to the integration of five different technologies for minimally invasive brain surgery. This thesis it is conducted in the framework of this project, in particular it is focused on the low level control of a medical robot device that will move the EDEN2020 catheter, a multi-segment probe made out of four interlocked segments, that can slide along one another generating curvilinear trajectories. The aim is to develop a control model of the catheter considering its cinematic constraints, including the maximum curvature. The dynamic model will take as input the desired trajectories in output of the EDEN2020 simulator, by using ROS (i.e. Robot Operating System). Moreover, it will be applicated to the model of the brain the consistence characteristic of the real brain to make the simulation more realistic.
|M.Sc. Candidate||Valentina Corbetta|
|Supervisors||Elena De Momi, Alice Segato, Francesco Calimeri|
|Title||Answer Set Programming for Steerable Needle Path Planning
Last-generation steerable needles represent a breakthrough in keyhole neurosurgery, as they are capable of reaching targets behind sensitive structures or in previously impenetrable areas. Different path planning approaches have been developed and can be divided in classical methods and machine learning. The first ones require a tradeoff between completeness and efficiency, which often makes them unsuitable for complex search problems; the latter requires large datasets to train the models, such datasets can be hard to retrieve in the medical and surgical context. The aim of this thesis, which is part of the EDEN2020 project, is to develop an artificial intelligent agent programmed using Answer Set Programming (ASP) to perform path planning of a steerable needle catheter to perform Deep Brain Stimulation . ASP is a form of declarative programming used to solve difficult (especially NP-hard) search problems. It constitutes a valid alternative to the previously introduced approaches, as programs are very compact and fast and it does not need large datasets. The agent should be able to autonomously compute the path from the starting point to the target, avoiding obstacles and satisfying constraints which can be customized by the surgeons and clinicians based on the specific case. It should also be able to classify a list of previously computed trajectories, based on their optimization of the specified constraints. The agent can be used both in pre-operative and intra-operative settings.
|M.Sc. Candidate||Chiara Galli de Paratesi|
|Supervisors||Elena De Momi, Guido Caccianiga, Jeremy Brown|
|Title||Training in robot-assisted surgery: impact of EMG-modulated haptic feedback on the trainee’s performance during a realistic suture task.|
Nowadays, the da Vinci robot, designed by Intuitive Surgical, is the most advanced system for minimally invasive surgery. One of the most challenging tasks performed by a surgeon when operating with this system is the suture, and, in particular, the knot tying. In fact, it is really common for the surgeon to apply an excessive force which leads to tissue damage.
|M.Sc. candidate||Sarah Elena Valderrama Hincapié|
|Thesis||Control Design of a Surgical Robotic Platform for Endoscopic Dissection|
|Supervisors||Elena De Momi, Arianna Menciassi, Andrea Mariani|
|Description||The thesis objective is the complement and improvement of the control system of a medical robotic device. This device is composed by a 6 DOFs (Degrees of Freedom) micro-manipulator that is mounted on the head of a traditional endoscope, so that it allows to carry out interventions in the field of gastrointestinal surgery under endoscopic visual guidance. The manipulator is endowed with 3 motors together with 3 additional ones that are located in an external box: these 6 motors controls the 6 degrees of freedom by means of actuation cables. Finally, the surgeon tele-operates the micro-manipulators by means of 2 Phantom Omni.|
|M.Sc. candidate||Lorenzo Minotti|
|Thesis||Autonomous tissue retraction and ultrasound guidance using the da Vinci robot|
|Supervisors||Elena De Momi, Pietro Valdastri, Andrea Mariani|
|Description||With respect to traditional “open” procedures, laparoscopic surgical operation reduces appreciably patient trauma, but on the other hand it increases complexity of the surgical task. The robotic surgery system was introduced as solution to minimize the shortcomings of this procedure, and to provide an improved visualization and a greater dexterity.
Robotic surgery is now improving surgeons’ performances in this way, enabling them to perform more delicate and precise minimally invasive surgeries and automating simple tasks. For example, the use of US echo in robotic surgery allows the physician to view a 3D volume in which the anatomy of the region of interest is visible. The volumetric representation permits information to be rendered in multiple axes, increasing the amount of information available to the doctors.
This master thesis project focuses on the development of algorithms for the automation of clinical gestures in robotic laparoscopy. The planning and control of these will be carried out especially with the reconstruction of anatomical 3D features through ultrasound images integration. Algorithms for the image quality analysis and for the estimate of the contact force will be developed. Moreover, a system of autonomous optimization of the US echo will be implemented through robotic manipulation.
|M.Sc. candidate||Tommaso Da Col|
|Thesis||Investigation on the introduction of an autonomous camera navigation system in the field of robotic surgery|
|Supervisors||Elena De Momi, Arianna Menciassi, Andrea Mariani, Peter Kazanzides|
|Description||Robotic surgery is spreadings as the main approach for performing surgery in several fields. The reason for its success lies on the advantages that it introduces for both the patient and the surgeon. Nevertheless learning how to deal with the new control dynamics of both the surgical tools and the endoscope is required. All these aspects make efficient training in practical skills fundamental to effectively exploit the advantages of robotic surgery platforms. However camera motion remains complicated, motivating the attempts for creating autonomous camera navigation systems towards clinical practice application.
The aim of this thesis is to investigate the introduction of an autonomous camera navigation system in the field of robotic surgery both to optimize the early stage of training and to reduce the workload during surgical operations for surgeons.
The project imply the use of:
1. The da Vinci Research Kit (dVRK, Intuitive Surgical, Sunnyvile, CA), Open source first generation da Vinci surgical system integrated with control hardware and software.
2. The Assisted Teleoperation in Augmented Reality (ATAR) framework, software architecture that allows to design augmented or virtual reality tasks.
(Linux, ROS (Robot Operating System), Cpp programming language, Matlab programming language basic knowledge are requested).
|M.Sc. candidate||Alberti Chiara, Dascanio Nicholas|
|Thesis||Development of an artificial intelligence system for the semi-autonomous execution of surgical tasks in Robot Assisted Minimally Invasive Surgery|
|Supervisors||Elena De Momi, Pietro Valdastri, Andrea Mariani|
|Description||This project aims at the planning and execution of semi-autonomous tissue retraction, a basic task occurring during minimally invasive surgical procedures, using the surgical robot Da Vinci. Particularly, the desired result is the autonomous remotion of flaps of organs or connective tissue from the surgical area, thus exposing the underlying anatomy of interest. In order to accomplish this, the first step is to collect a wide dataset of retraction gestures on different tissue’s flaps. These tasks are performed by expert surgeons on corpses, using the robotic system. Then, the detection of tissue’s flaps in the endoscopic image is achieved using machine learning techniques, in particular deep neural networks. Subsequently, a procedural algorithm is developed to plan and execute the retraction trajectory in a semi-autonomous way. Finally, the performance of this new system will be evaluated through experimental tests and compared with the gold standard, the same task done by the surgeon himself without the robotic system.|
|M.Sc. candidate||Eleonora Centanini|
|Thesis||Architected, inflatable and life-sized lung model|
|Supervisors||Elena De Momi, Antonio E Forte, Katia Bertoldi, Francesco Petrella|
|Description||Research has shown that the success rate in many types of surgeries is strictly related to the experience of the surgeon. However, early in their career, trainees are not given the opportunity to operate on a sufficient number of patients nor to perform an exhaustive mix of procedures. The scenario has been further worsened by the reduction of assisted training hours in Europe (since 2009) and US (since 2011). Training and technical tasks are usually practised on cadavers, animals or using virtual simulators. However, all these alternatives present difficulties: limited availability, expensive handling and preservation processes (cadaveric training), nonhuman anatomical structures (animal training), costly set-up, and doubtful skills transfer to the real operating theatre (virtual simulators). A potential solution is to promote the use of artificial synthetic models, also known as phantoms. Phantoms are reproduction of human parts and organs that allow the trainee to practice positioning of the anatomical structures as well as hand coordination. Unfortunately, they lack of reliable tactile feedback (e.g. palpation) and real tissue deformation patterns which critically reduce the fidelity of the surgical training.
The main objective of this project is to overcome the present limitations by developing phantoms capable of providing detailed anatomical structures along with an accurate tactile response when performing surgical tasks such as cutting, indention and suturing. The proposed investigation is aimed at designing, making and testing synthetic advanced materials tailored to reproduce the mechanical response of human tissues, with a particular focus on lungs.
Firstly, several synthetic compounds will be tested in order to identify their mechanical properties and compare them with those of lung tissue. Secondly, medical 3D images of the lungs during a complete respiratory cycle will be segmented and the deformation field of the organ extracted. Afterwards, finite element simulations of the 3D organ during the respiratory cycle will be performed: through a shape optimization algorithm, an architectured soft mesh will be designed and placed in the phantom, which will guide the deformation of the synthetic organ to minimize the mismatch with the medical volumes. Finally, a 3D physical model of the lungs will be manufactured and validated against the simulation.
|M.Sc. candidate||Tommaso Ciceri|
|Thesis||3D brain vasculature segmentation via weak supervised deep learning approach|
|Supervisors||Elena De Momi, Sara El Hadji|
|Description||The stereotactic electroencephalography (SEEG) procedure allows patient brain activity to be recorded in order to localize the onset of seizures through the placement of intracranial electrodes. The planning phase can be cumbersome and very time consuming, and no quantitative information is provided to neurosurgeons regarding the safety and efficacy of their trajectories. Among the critical structure to be considered, vessels are the most difficult one to segment, and in fact major complications related to SEEG are mainly related to intracerebral hemorrhage. At this purpose several methods have been investigated. Nowadays, deep learning is becoming the state of the art approach for medical image segmentation. In this work, we present a 3D brain vasculature segmentation via weak supervised deep learning approach specifically designed to ease the SEEG trajectory planning using the 3D Slicer platform as a basis.|
|M.Sc. candidate||Sara Ihab Abdelghaffar Mohamed Sabry|
|Thesis||Answer Set Programming (ASP) for declarative reasoning in surgery|
|Supervisors||Elena De Momi|
|Co-Supervisors||Francesco Calimeri, Hirenkumar Nakawala|
|Description||Expert-level knowledge has become critical for many artificial intelligence-based applications. Recently, ontologies, a method to represent the expert knowledge, have been extensively used to represent surgical process models. In our recent efforts, we developed ontologies and reasoning rules for classification of the surgical workflows. However, it is difficult to reason with a consistent set of rules over a different set of tasks in surgical ontologies. Ontology-based query answering (OBQA) for surgery can be developed to tackle complex reasoning needed to understand and execute the surgical workflow. In proposal thesis, the student will be responsible for developing a robust OBQA system, which provides robust analysis of surgical workflow of Robot-Assisted Partial Nephrectomy (RAPN). RAPN is performed to remove the kidney tumour. We already have an extensional database on RAPN workflow which is collected from a hospital in Milan. The student will also expect to enhance the ontology developed on RAPN. The logical theory (i.e. axioms in the knowledge base) will be induced with the help of inductive logic programming methodologies. Answer Set Programming (ASP) is expected to solve the query (mostly analysing surgical workflows) posed to an ontology, where the ontological explanation is converted into ASP specification to be solved by an ASL solver such as DLV. Figure shows a simple example query which find a step given action and some instrument as constraints where ASP is expected to find a solution i.e. instances of step where this constraint is satisfied. The system is expected to handle the queries expressed in the natural language.|
|M.Sc. candidate||Chiara Carlini|
|Thesis||Deep-learning based vessel tracking in minimally-invasive nephrectomy|
|Supervisors||Elena De Momi, Sara Moccia (UNIVPM)|
|Description||Vessel avoidance during nephrectomy is crucial to prevent bleeding and reduce after-treatment mortality or morbidity. In this vision, automatic vessel segmentation can be integrated in robot-assisted surgical procedures as to avoid robot tip to enter (forbidden) vascular regions. A strong literature already exists on vessel segmentation, however several limitations hamper the translation into the actual clinical practice, such as low robustness to illumination variation and noise, and to variability in patients and imaging instrumentation. Deep learning has the potential to overcome such limitations.
Thus, the goal of this thesis is to implement deep-learning based vessel segmentation and tracking in endoscopic videos recorded during nephrectomy. In particular, the implemented deep learning method is based on convolutional neural networks (CNNs). Many previous projects have obtained satisfying results in the detection and tracking of everyday life objects using CNNs starting from natural images captured by common webcams, as shown in Figure. For this reason, it has been decided to exploit CNNs potentiality to identify relevant anatomical structures present in real medical images, like endoscopic ones. In this specific case, CNNs are employed to segment kidney vessels from endoscopic kidney images. Some examples of the accomplished segmentation are shown in Figure.
|M.Sc. candidate||Giulio Russo|
|Thesis||A handheld robotic tool (Micron) to automate vessel avoidance in microsurgery|
|Supervisors||Elena De Momi, Cameron Riviere (CMU), Sara Moccia (UNIVPM)|
|Description||In neurosurgery procedures for meningioma resections, minute and accurate operations in poor visibility conditions have to be performed. One of the most common complication is bleeding caused by tools misplacement with respect to the vessels.
Numerous robotic systems have been developed to assist surgeons in micro-neurosurgical interventions: this thesis investigates the use of Micron.Micron is a robotic handheld tool, it guarantees an automatic vessel avoidance thanks to several technologies:
– Position control guarantees tremor compensation and tracking position;
– Image analysis recognizes the vessels position;The aim of this thesis is to design a force control strategy: the interaction between tip and vessels cannot exceed certain force thresholds, just to avoid vessels damages and bleeding.A FBG force sensor (optical fiber sensor) is positioned on the tip Micron: it’s a custom 2DOF temperature-compensated force sensor developed at Johns Hopkins University (Figure). It has a 0.25 mN precision.Hence, in parallel to the position loop, a force loop is implemented (Figure). Only when the force thresholds are exceeded, the force loop yields modify the reference position of the position loop (the main force regulator is a PID).The control strategy is implemented in LabView and the final system will be tested on phantoms.
This thesis is in collaboration with Carniege Mellon University (CMU).
|M.Sc. Candidate||Stefano De Nigris|
|Advisor||Elena De Momi, Digna M González-Otero (EHU-UPV), Jesús Ruiz(EHU-UPV), Sofía Ruiz de Gauna(EHU-UPV)|
|Title||Application of accelerometer-based chest compression feedback devices in novel scenarios|
|Collaborations||Laboratorio GSC (Grupo Señales y Comunicaciones), Universidad del País Vasco (EHU-UPV), Bilbao, Spain|
Sudden cardiac arrest is the largest cause of natural death worldwide. Early cardiopulmonary resuscitation (CPR) is key for patient survival. Resuscitation guidelines emphasize the importance of providing high quality chest compressions, that is, with a rate of 100 compressions per minute and a depth of 5cm in adults and one third of the diameter of the chest in children. However, meeting these requirements is difficult, even for well-trained rescuers.
The use of feedback systems can help rescuers increase chest compression quality. Most of these devices measure the acceleration of the chest during compressions, and guide the rescuer towards the target rate and depth. However, these systems present two main limitations. First, they are designed for adult patients, and thus present a target depth of 5cm, which is inadequate for children. Second, they could be inaccurate when used in moving vehicles, such as a train or a plane, as they would register the acceleration of the vehicle along with that of the patient’s chest.
The work tackles those two limitations. It is divided in two main objectives: First, to develop and test an algorithm to measure the diameter of the chest using an accelerometer. This algorithm could be used to adapt feedback systems to be used in pediatric patients. Second, to propose and test an additive model to evaluate the accuracy of accelerometer-based CPR feedback devices in moving vehicles, and to apply it to the case of a plane and train.
The results of this thesis could extend the scenarios of application of feedback systems, allowing their use in pediatric cardiac arrests or in public transportation means.