Iván Macía

Bio: Iván Macía is an academic researcher from University of the Basque Country. The author has contributed to research in topics: Aneurysm & Endovascular aneurysm repair. The author has an hindex of 9, co-authored 36 publications receiving 300 citations.

An automatic segmentation and reconstruction of mandibular structures from CT-data

Abstract: In any medical data analysis a good visualization of specific parts or tissues are fundamental in order to perform accurate diagnosis and treatments. For a better understanding of the data, a segmentation process of the images to isolate the area or region of interest is important to be applied beforehand any visualization step. In this paper we present a method for mandibular structure surface extraction and reconstruction from CT-data images. We tested several methods and algorithms in order to find a fast and feasible approach that could be applicable in clinical procedures, providing practical and efficient tools for mandibular structures analysis.

Segmentation of abdominal aortic aneurysms in CT images using a radial model approach

Abstract: Abdominal Aortic Aneurysm (AAA) is a dangerous condition where the weakening of the aortic wall leads to its deformation and the generation of a thrombus. To prevent a possible rupture of the aortic wall, AAAs can be treated non-invasively by means of the Endovascular Aneurysm Repair technique (EVAR), which consists of placing a stent-graft inside the aorta in order to exclude the bulge from the blood circulation and usually leads to its contraction. Nevertheless, the bulge may continue to grow without any apparent leak. In order to effectively assess the changes experienced after surgery, it is necessary to segment the aneurysm, which is a very time-consuming task. Here we describe the initial results of a novel model-based approach for the semi-automatic segmentation of both the lumen and the thrombus of AAAs, using radial functions constrained by a priori knowledge and spatial coherency.

Knowledge management in image-based analysis of blood vessel structures

Abstract: We have detected the lack of a widely accepted knowledge representation model in the area of Blood Vessel analysis. We find that such a tool is needed for the future development of the field and our own research efforts. It will allow easy reuse of software pieces through appropriate abstractions, facilitating the development of innovative methods, procedures and applications. We include a thorough review of vascular morphology image analysis. After the identification of the key representation elements and operations, we propose a Vessel Knowledge Representation (VKR) model that would fill this gap. We give insights into its implementation based on standard Object-Oriented Programming tools and paradigms. The VKR would easily integrate with existing medical imaging and visualization software platforms, such as the Insight ToolKit (ITK) and Visualization Toolkit (VTK).

Augmented reality in laparoscopic surgical oncology.

Abstract: Minimally invasive surgery represents one of the main evolutions of surgical techniques aimed at providing a greater benefit to the patient. However, minimally invasive surgery increases the operative difficulty since the depth perception is usually dramatically reduced, the field of view is limited and the sense of touch is transmitted by an instrument. However, these drawbacks can currently be reduced by computer technology guiding the surgical gesture. Indeed, from a patient’s medical image (US, CT or MRI), Augmented Reality (AR) can increase the surgeon’s intra-operative vision by providing a virtual transparency of the patient. AR is based on two main processes: the 3D visualization of the anatomical or pathological structures appearing in the medical image, and the registration of this visualization on the real patient. 3D visualization can be performed directly from the medical image without the need for a pre-processing step thanks to volume rendering. But better results are obtained with surface rendering after organ and pathology delineations and 3D modelling. Registration can be performed interactively or automatically. Several interactive systems have been developed and applied to humans, demonstrating the benefit of AR in surgical oncology. It also shows the current limited interactivity due to soft organ movements and interaction between surgeon instruments and organs. If the current automatic AR systems show the feasibility of such system, it is still relying on specific and expensive equipment which is not available in clinical routine. Moreover, they are not robust enough due to the high complexity of developing a real-time registration taking organ deformation and human movement into account. However, the latest results of automatic AR systems are extremely encouraging and show that it will become a standard requirement for future computer-assisted surgical oncology. In this article, we will explain the concept of AR and its principles. Then, we will review the existing interactive and automatic AR systems in digestive surgical oncology, highlighting their benefits and limitations. Finally, we will discuss the future evolutions and the issues that still have to be tackled so that this technology can be seamlessly integrated in the operating room.

Understanding Deep Learning Techniques for Image Segmentation

Abstract: The machine learning community has been overwhelmed by a plethora of deep learning--based approaches. Many challenging computer vision tasks, such as detection, localization, recognition, and segmentation of objects in an unconstrained environment, are being efficiently addressed by various types of deep neural networks, such as convolutional neural networks, recurrent networks, adversarial networks, and autoencoders. Although there have been plenty of analytical studies regarding the object detection or recognition domain, many new deep learning techniques have surfaced with respect to image segmentation techniques. This article approaches these various deep learning techniques of image segmentation from an analytical perspective. The main goal of this work is to provide an intuitive understanding of the major techniques that have made a significant contribution to the image segmentation domain. Starting from some of the traditional image segmentation approaches, the article progresses by describing the effect that deep learning has had on the image segmentation domain. Thereafter, most of the major segmentation algorithms have been logically categorized with paragraphs dedicated to their unique contribution. With an ample amount of intuitive explanations, the reader is expected to have an improved ability to visualize the internal dynamics of these processes.

A Unified Passivity Based Control Framework for Position, Torque and Impedance Control of Flexible Joint Robots.

Abstract: This paper describes a general passivity-based framework for the control of flexible joint robots. Recent results on torque, position, as well as impedance control of flexible joint robots are summarized, and the relations between the individual contributions are highlighted. It is shown that an inner torque feedback loop can be incorporated into a passivity-based analysis by interpreting torque feedback in terms of shaping of the motor inertia. This result, which implicitly was already included in earlier work on torque and position control, can also be used for the design of impedance controllers. For impedance control, furthermore, potential energy shaping is of special interest. It is shown how, based only on the motor angles, a potential function can be designed which simultaneously incorporates gravity compensation and a desired Cartesian stiffness relation for the link angles. All the presented controllers were experimentally evaluated on DLR lightweight robots and their performance and robustness shown with respect to uncertain model parameters. Experimental results with position controllers as well as an impact experiment are presented briefly, and an overview of several applications is given in which the controllers have been applied.