Medical image analysis methods for anatomical surface reconstruction using tracked 3D ultrasound

The thesis focuses on a study of techniques for acquisition and reconstruction of surface data from anatomical objects by means of tracked 3D ultrasound. In the context of the work two experimental scanning systems are developed and tested on both artificial objects and biological tissues. The first system is based on the freehand ultrasound principle and utilizes a conventional 2D ultrasound transducer coupled with an electromechanical 3D position tracker. The main properties and the basic features of this system are discussed. A number of experiments show that its accuracy in the close to ideal conditions reaches 1.2 mm RMS. The second proposed system implements the sequential triggered scanning approach. The system consists of an ultrasound machine, a workstation and a scanning body (a moving tank filled with liquid and a transducer fixation block) that performs transducer positioning and tracking functions. The system is tested on artificial and real bones. The performed experiments illustrate that it provides significantly better accuracy than the freehand ultrasound (about 0.2 mm RMS) and allows acquiring regular data with a good precision. This makes such a system a promising tool for orthopaedic and trauma surgeons during contactless X-ray-free examinations of injured extremities. The second major subject of the thesis concerns development of medical image analysis methods for 3D surface reconstruction and 2D object detection. We introduce a method based on mesh-growing surface reconstruction that is designed for noisy and sparse data received from 3D tracked ultrasound scanners. A series of experiments on synthetic and ultrasound data show an appropriate reconstruction accuracy. The reconstruction error is measured as the averaged distance between the faces of the mesh and the points from the cloud. Dependently on the initial settings of the method the error varies in range 0.04 - 0.2% for artificial data and 0.3 - 0.7 mm for ultrasound bone data. The reconstructed surfaces correctly interpolate the original point clouds and demonstrate proper smoothness. The next significant problem considered in the work is 2D object detection. Although medical object detection is not integrated into the developed scanning systems, it can be used as a possible further extension of the systems for automatic detection of specific anatomical structures. We analyse the existent object detection methods and introduce a modification of the one based on the popular Generalized Hough Transform (GHT). Unlike the original GHT, the developed method is invariant to rotation and uniform scaling, and uses an intuitive two-point parametrization. We propose several implementations of the feature-to-vote conversion function with the corresponding vote analysis principles. Special attention is devoted to a study of the hierarchical vote analysis and its probabilistic properties. We introduce a parameter space subdivision strategy that reduces the probability of vote peak omission, and show that it can be efficiently implemented in practice using the Gumbel probability distribution

Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519

EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging without External Trackers

Ultrasound (US) is the most widely used fetal imaging technique. However, US images have limited capture range, and suffer from view dependent artefacts such as acoustic shadows. Compounding of overlapping 3D US acquisitions into a high-resolution volume can extend the field of view and remove image artefacts, which is useful for retrospective analysis including population based studies. However, such volume reconstructions require information about relative transformations between probe positions from which the individual volumes were acquired. In prenatal US scans, the fetus can move independently from the mother, making external trackers such as electromagnetic or optical tracking unable to track the motion between probe position and the moving fetus. We provide a novel methodology for image-based tracking and volume reconstruction by combining recent advances in deep learning and simultaneous localisation and mapping (SLAM). Tracking semantics are established through the use of a Residual 3D U-Net and the output is fed to the SLAM algorithm. As a proof of concept, experiments are conducted on US volumes taken from a whole body fetal phantom, and from the heads of real fetuses. For the fetal head segmentation, we also introduce a novel weak annotation approach to minimise the required manual effort for ground truth annotation. We evaluate our method qualitatively, and quantitatively with respect to tissue discrimination accuracy and tracking robustness.Comment: MICCAI Workshop on Perinatal, Preterm and Paediatric Image analysis (PIPPI), 201

Cardiac Motion Analysis Based on Optical Flow on Real-Time Three-Dimensional Ultrasound Data

With relatively high frame rates and the ability to acquire volume data sets with a stationary transducer, 3D ultrasound systems, based on matrix phased array transducers, provide valuable three-dimensional information, from which quantitative measures of cardiac function can be extracted. Such analyses require segmentation and visual tracking of the left ventricular endocardial border. Due to the large size of the volumetric data sets, manual tracing of the endocardial border is tedious and impractical for clinical applications. Therefore the development of automatic methods for tracking three-dimensional endocardial motion is essential. In this study, we evaluate a four-dimensional optical flow motion tracking algorithm to determine its capability to follow the endocardial border in three dimensional ultrasound data through time. The four-dimensional optical flow method was implemented using three-dimensional correlation. We tested the algorithm on an experimental open-chest dog data set and a clinical data set acquired with a Philips' iE33 three-dimensional ultrasound machine. Initialized with left ventricular endocardial data points obtained from manual tracing at end-diastole, the algorithm automatically tracked these points frame by frame through the whole cardiac cycle. Finite element surfaces were fitted through the data points obtained by both optical flow tracking and manual tracing by an experienced observer for quantitative comparison of the results. Parameterization of the finite element surfaces was performed and maps displaying relative differences between the manual and semi-automatic methods were compared. The results showed good consistency with less than 10% difference between manual tracing and optical flow estimation on 73% of the entire surface. In addition, the optical flow motion tracking algorithm greatly reduced processing time (about 94% reduction compared to human involvement per cardiac cycle) for analyzing cardiac function in three-dimensional ultrasound data sets. A displacement field was computed from the optical flow output, and a framework for computation of dynamic cardiac information is introduced. The method was applied to a clinical data set from a heart transplant patient and dynamic measurements agreed with known physiology as well as experimental results

Evaluation of optical flow algorithms for tracking endocardial surfaces on three-dimensional ultrasound data

With relatively high frame rates and the ability to acquire volume data sets with a stationary transducer, 3D ultrasound systems, based on matrix phased array transducers, provide valuable three-dimensional information, from which quantitative measures of cardiac function can be extracted. Such analyses require segmentation and visual tracking of the left ventricular endocardial border. Due to the large size of the volumetric data sets, manual tracing of the endocardial border is tedious and impractical for clinical applications. Therefore the development of automatic methods for tracking three-dimensional endocardial motion is essential. In this study, we evaluate a four-dimensional optical flow motion tracking algorithm to determine its capability to follow the endocardial border in three dimensional ultrasound data through time. The four-dimensional optical flow method was implemented using three-dimensional correlation. We tested the algorithm on an experimental open-chest dog data set and a clinical data set acquired with a Philips' iE33 three-dimensional ultrasound machine. Initialized with left ventricular endocardial data points obtained from manual tracing at end-diastole, the algorithm automatically tracked these points frame by frame through the whole cardiac cycle.A finite element surface was fitted through the data points obtained by both optical flow tracking and manual tracing by an experienced observer for quantitative comparison of the results. Parameterization of the finite element surfaces was performed and maps displaying relative differences between the manual and semi-automatic methods were compared.The results showed good consistency between manual tracing and optical flow estimation on 73% of the entire surface with fewer than 10% difference. In addition, the optical flow motion tracking algorithm greatly reduced processing time (about 94% reduction compared to human involvement per cardiac cycle) for analyzing cardiac function in three-dimensional ultrasound data sets

Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology

Until recently, Computer-Aided Medical Interventions (CAMI) and Medical Robotics have focused on rigid and non deformable anatomical structures. Nowadays, special attention is paid to soft tissues, raising complex issues due to their mobility and deformation. Mini-invasive digestive surgery was probably one of the first fields where soft tissues were handled through the development of simulators, tracking of anatomical structures and specific assistance robots. However, other clinical domains, for instance urology, are concerned. Indeed, laparoscopic surgery, new tumour destruction techniques (e.g. HIFU, radiofrequency, or cryoablation), increasingly early detection of cancer, and use of interventional and diagnostic imaging modalities, recently opened new challenges to the urologist and scientists involved in CAMI. This resulted in the last five years in a very significant increase of research and developments of computer-aided urology systems. In this paper, we propose a description of the main problems related to computer-aided diagnostic and therapy of soft tissues and give a survey of the different types of assistance offered to the urologist: robotization, image fusion, surgical navigation. Both research projects and operational industrial systems are discussed

Performance of image guided navigation in laparoscopic liver surgery – A systematic review

Background: Compared to open surgery, minimally invasive liver resection has improved short term outcomes. It is however technically more challenging. Navigated image guidance systems (IGS) are being developed to overcome these challenges. The aim of this systematic review is to provide an overview of their current capabilities and limitations. Methods: Medline, Embase and Cochrane databases were searched using free text terms and corresponding controlled vocabulary. Titles and abstracts of retrieved articles were screened for inclusion criteria. Due to the heterogeneity of the retrieved data it was not possible to conduct a meta-analysis. Therefore results are presented in tabulated and narrative format. Results: Out of 2015 articles, 17 pre-clinical and 33 clinical papers met inclusion criteria. Data from 24 articles that reported on accuracy indicates that in recent years navigation accuracy has been in the range of 8–15 mm. Due to discrepancies in evaluation methods it is difficult to compare accuracy metrics between different systems. Surgeon feedback suggests that current state of the art IGS may be useful as a supplementary navigation tool, especially in small liver lesions that are difficult to locate. They are however not able to reliably localise all relevant anatomical structures. Only one article investigated IGS impact on clinical outcomes. Conclusions: Further improvements in navigation accuracy are needed to enable reliable visualisation of tumour margins with the precision required for oncological resections. To enhance comparability between different IGS it is crucial to find a consensus on the assessment of navigation accuracy as a minimum reporting standard

NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6 figures; Update includes additional applications, updated author list and formatting for journal submissio

Intraoperative Navigation Systems for Image-Guided Surgery

Recent technological advancements in medical imaging equipment have resulted in a dramatic improvement of image accuracy, now capable of providing useful information previously not available to clinicians. In the surgical context, intraoperative imaging provides a crucial value for the success of the operation. Many nontrivial scientific and technical problems need to be addressed in order to efficiently exploit the different information sources nowadays available in advanced operating rooms. In particular, it is necessary to provide: (i) accurate tracking of surgical instruments, (ii) real-time matching of images from different modalities, and (iii) reliable guidance toward the surgical target. Satisfying all of these requisites is needed to realize effective intraoperative navigation systems for image-guided surgery. Various solutions have been proposed and successfully tested in the field of image navigation systems in the last ten years; nevertheless several problems still arise in most of the applications regarding precision, usability and capabilities of the existing systems. Identifying and solving these issues represents an urgent scientific challenge. This thesis investigates the current state of the art in the field of intraoperative navigation systems, focusing in particular on the challenges related to efficient and effective usage of ultrasound imaging during surgery. The main contribution of this thesis to the state of the art are related to: Techniques for automatic motion compensation and therapy monitoring applied to a novel ultrasound-guided surgical robotic platform in the context of abdominal tumor thermoablation. Novel image-fusion based navigation systems for ultrasound-guided neurosurgery in the context of brain tumor resection, highlighting their applicability as off-line surgical training instruments. The proposed systems, which were designed and developed in the framework of two international research projects, have been tested in real or simulated surgical scenarios, showing promising results toward their application in clinical practice