Current PhD projects


PhD Candidate

Hirenkumar Nakawala

Advisors

Elena De Momi, Prof. Giancarlo Ferrigno

Title

Knowledge-driven and machine-learning based context-aware framework for surgical training and assistance

Description

The PhD project focuses on creating ontology-based surgical models, logic-based amd machine-learning based reasoning processes for creating context-awareness to aid surgical training and assistance. A large amount of data and information is produced by different sensors, e.g. endoscopes, in the surgical operative rooms during the surgical interventions, which could also be annotated. Effective utilization of sensors requires complex integration of data at various levels and in different serialization formats. Also, the amount of information produced in operation theatre outpace capability of surgeons to understand information effectively in time. Surgeries are also becoming complex with advent of new technologies such as robotic assistance which requires specialised training. A context-aware system for surgical assistance was proposed using ontology engineering, computer vision and machine learning techniques, and semantic technologies. We believe, knowledge representation of surgical information and surgical workflow analysis has utmost importance to reduce iatrogenic complications by providing contextual information at a specific instance of time e.g. contextual information about a surgical instrument, which is required during the consecutive step, based on the step currently in progress. Aim of our work is to create a reliable and repeatable aid, such as context-aware system, to assist the clinician training on the surgical interventions and to therefore improving patient care, reducing medical errors and iatrogenic complications. Context-aware systems are expected to enhance decision making capability of the surgeons while operating in the complex surgical environments, during robotic surgery for instance, where specialized knowledge is required to perform the procedure. The framework could be possible to incorporate in the simulated surgical training environment.

PhD Period

XXX cycle (November 2014 – November 2017)


PhD Candidate

Jacopo Buzzi

Advisors

Elena De Momi, Prof. Giancarlo Ferrigno

Title

Surgical gesture analysis in robotic teleoperation

Description

Jacopo_phd

The PhD project focuses on the analysis of the surgical movement during teleoperation. In the recent years, more and more surgical procedures have been enhanced by the use of teleoperated surgical robots. In this scenario, the surgeon interacts with a master controller, usually a multi degrees of freedom robot, to control an actuation unit (the slave) that directly manipulates the patient. The master controllers are usually designed to be able to reproduce all the movements the surgeon would do in free hand, nevertheless a sometimes extensive training phase is required to acquire familiarity with the teleoperation. The aim of this study is to understand if and how much the use of a master controller modifies the kinematic and musculoskeletal strategies adopted by the user, also trying to compare different controllers; in order to do so, the kinematic position of the user’s arm, and the muscular activation during specific tasks are studied. Thanks to this knowledge, it would be possible to potentially design better controllers, to create more specific training tasks and environment and to produce quantitative indexes to evaluate the surgeon’s ability in teleoperation.

PhD Period

XXX cycle (November 2014 – November 2017)


PhD Candidate

Sara Moccia

Advisors

Elena De Momi, Leonardo Mattos (IIT)

Collaboration

Istituto Italiano di Tecnologia

Title

Laryngeal disorder detection and classification in narrow-band endoscopic video

Description

Sara_updatedthesis

Squamous Cell Carcinoma (SCC) is the most common cancer of the laryngeal tract, arising in 95% to 98% of all cases of laryngeal cancer. Early-stage diagnosis of laryngeal SCC is of primary importance for lowering patient mortality or after treatment morbidity, preserving both laryngeal anatomy and vocal fold function. Despite histopathological examination on tissue samples extracted with biopsy is still the gold-standard for diagnosis, the relevance of tissue visual analysis for screening purpose has led, in the past few years, to the development of new optical-biopsy techniques, such as Narrow-Band Imaging (NBI) endoscopy, which has now become the state of the art for laryngeal tract inspection. However, as reported in the clinical literature, the diagnosis is still challenging due to the small tissue modification, which can pass unnoticed to human eye. Despite this, few efforts have been invested in the computer-assisted diagnosis. The objective of this PhD project is to investigate, for the first time in the literature, the use of texture-based machine-learning algorithms for laryngeal tissue classification. Different tissue descriptors are tested, from classic global descriptors, such as local binary pattern, to convolutional neural network-based descriptors. To estimate the classification reliability, a measure of confidence is also exploited. The workflow of the proposed approach is shown in Figure. This research represents an important advancement in the state of art of computer-assisted laryngeal diagnosis, which is now mainly focused of classification algorithms strongly sensitive to a-priori set parameters. The results of this research are a promising step toward a helpful endoscope-integrated processing system to support the early-stage diagnosis.

PhD Period

XXX cycle (May 2015 – May 2018)


PhD Candidate

Davide Scorza

Advisors

Elena De Momi, Luis Kabongo (Vicomtech)

Collaboration

Vicomtech-IK4 (Donostia/San Sebastian, Gipuzkoa – Spain)

Title

Automatic planning for intracranial electrode placement in SEEG

Description

Davide_PhDpic

Epilepsy represents a group of neurological diseases characterized by epileptic seizures and is possible to distinguish between two cases: general and focal epilepsy. The latter, sometimes, offers the possibility to remove the epilepsy focus through surgical intervention. Therefore, the identification of the seizures onset is a fundamental part. Stereo ElectroEncephaloGraphy (SEEG) is a technique that imply the insertion of intracranial electrodes in order to record brain activity at multiple levels and from multiple structures, allowing the identification of the focus. Surgeons need to plan electrodes trajectories manually, which could be a cumbersome task and very time consuming. The aim of this PhD thesis is to develop an automatic planner to assist surgeons during this phase, computing the optimal set of trajectories based on patient anatomy, surgical constraints and surgeon experience.

PhD Period

XXXI cycle (Nov 2015 – Nov 2019)


PhD Candidate

Hang Su

Advisors

Prof. Giancarlo FerrignoElena De Momi

Title

Adaptive Control of Surgical Robots for Neurosurgery Tele-operation Task

Description

Hang_PhDThesis_Pic

The PhD Thesis focuses on developing the adaptive control algorithm for tele-operated surgical robotic system sharing the workspace with people. Based on developed technology in surgical planning and navigation, the project involves a new paradigm for adaptive control algorithm in Medical Robots to secure its performance in manipulation, especially for neurosurgery. During the surgical tele-operation, redundancy optimization will be introduced to optimize the pose configuration for the surgical robot system. Then, adaptation and learning methods are proposed to enhance the precision and reliability of Surgical Robot systems, not only with force and position control, but also with redundancy optimization for the robot arm pose, which are all basic requirements for practical application of Medical Robot in surgery. Prediction model will also be applied to enhance the online performance of robot tele-operation so that the surgical robot can act in advance in cooperation with nurse. Finally, experimental validation will be conducted to validate the performance of the developed real surgical robot system.

PhD Period

XXXI cycle (Nov 2015 – Nov 2018)


PhD Candidate

Alberto Favaro

Advisors

Elena De MomiGiancarlo Ferrigno; Feridnadno Rodriguez y Baena (Imperial College of London)

 Collaboration Imperial College London, U.K.
 Title Path planning for non-linear, non-holonomic systems

Description

Albertothesis

The PhD thesis regards combinatorial and sampling-based methods for path planning intended for being used in the context of robot non-holonomicity. Further than this, the systems under study also show a non-linear characteristic in their three-dimensions steering capability, which represents a further constraint in the definition of feasible paths. The work uses the EDEN2020 steerable catheter as a case of study and defines surgical trajectories for brain tumor treatment in the context of Convention Enhanced Delivery.

PhD Period

XXXI cycle (November 2015 – November 2018).


PhD Candidate

Marco Vidotto

Advisors

Elena De Momi; Daniele Dini (Imperial college, London)

 Title Convection Enhanced Delivery model for brain tumour treatment

Description

Marcothesis

Convection-Enhanced Delivery is a therapeutic treatment consisting in the injection under positive pressure of drugs directly in the brain tumorous zone. It has recently emerged as a promising delivery method because it allows to circumvent the blood-brain barrier, which is a major impediment in systemic delivery of macro-molecular therapeutic agents, by directly injecting into the parenchymal space. The treatment success depends on the CED protocol, e.g., infusion site, infusate concentration and infusate rate. Computational and analytical models of infusion may be used to predict drug distribution and optimize CED protocols. The PhD project focuses on the development of a new CFD (Computational Fluid Dynamics) model which exploits two types of information which are related to two different scales: a micro (µm) and a macro (mm) scale. Imaging techniques as the FIB-SEM (Focused Ion Beam – Scanning Electron Microscope) allow to access the geometrical organization of groups of single axons which is directly related to fundamental hydraulic parameters such as conductivity and tortuosity. On the other hand, on a macro scale, DTI (Diffusion Tensor imaging) provides essential information both on diffusivity and white matter orientation. This study aims to funnel micro and macro scale parameters in a unique and comprehensive model.

PhD Period

XXXII cycle (November 2016 – November 2019).

PhD Candidate

Sara El Hadji

Advisors

Elena De Momi; Giuseppe Baselli

Collaborations Dr.Francesco Cardinale, Epilepsy neurosurgeon at the Ospedale Ca’ Granda, Niguarda, in Milan; Medtronic, Littleton, Boston, USA
 Title ART 3.5D – a novel algorithm, to label arteries and veins from 3D angiography

Description

Several neurosurgical procedures, such as Artero Venous Malformations (AVMs), aneurysm embolizations and StereoElectroEncephaloGraphy (SEEG) require accurate reconstruction of the cerebral vascular tree, as well as the classification of arteries and veins, to increase the safety of the intervention. This project aims at developing a novel approach attempting to recover the contrast dynamic information from standard Contrast Enhanced Cone Beam Computed Tomography (CE-CBCT) scans. The algorithm proposed by our team is called ART 3.5 D. It is a novel algorithm based on the post- processing of both the angiogram and the raw data of a standard Digital Subtraction Angiography from a CBCT (DSA- CBCT) allowing arteries and veins segmentation and labeling.

PhD Period

XXXII cycle (February 2016 – F 2019).