33,894 research outputs found

    NiftyNet: a deep-learning platform for medical imaging

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    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

    Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

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    One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

    Design, Development, and Evaluation of a Teleoperated Master-Slave Surgical System for Breast Biopsy under Continuous MRI Guidance

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    The goal of this project is to design and develop a teleoperated master-slave surgical system that can potentially assist the physician in performing breast biopsy with a magnetic resonance imaging (MRI) compatible robotic system. MRI provides superior soft-tissue contrast compared to other imaging modalities such as computed tomography or ultrasound and is used for both diagnostic and therapeutic procedures. The strong magnetic field and the limited space inside the MRI bore, however, restrict direct means of breast biopsy while performing real-time imaging. Therefore, current breast biopsy procedures employ a blind targeting approach based on magnetic resonance (MR) images obtained a priori. Due to possible patient involuntary motion or inaccurate insertion through the registration grid, such approach could lead to tool tip positioning errors thereby affecting diagnostic accuracy and leading to a long and painful process, if repeated procedures are required. Hence, it is desired to develop the aforementioned teleoperation system to take advantages of real-time MR imaging and avoid multiple biopsy needle insertions, improving the procedure accuracy as well as reducing the sampling errors. The design, implementation, and evaluation of the teleoperation system is presented in this dissertation. A MRI-compatible slave robot is implemented, which consists of a 1 degree of freedom (DOF) needle driver, a 3-DOF parallel mechanism, and a 2-DOF X-Y stage. This slave robot is actuated with pneumatic cylinders through long transmission lines except the 1-DOF needle driver is actuated with a piezo motor. Pneumatic actuation through long transmission lines is then investigated using proportional pressure valves and controllers based on sliding mode control are presented. A dedicated master robot is also developed, and the kinematic map between the master and the slave robot is established. The two robots are integrated into a teleoperation system and a graphical user interface is developed to provide visual feedback to the physician. MRI experiment shows that the slave robot is MRI-compatible, and the ex vivo test shows over 85%success rate in targeting with the MRI-compatible robotic system. The success in performing in vivo animal experiments further confirm the potential of further developing the proposed robotic system for clinical applications

    MRI-guided focused ultrasound surgery in musculoskeletal diseases: the hot topics

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    MRI-guided focused ultrasound surgery (MRgFUS) is a minimally invasive treatment guided by the most sophisticated imaging tool available in today's clinical practice. Both the imaging and therapeutic sides of the equipment are based on non-ionizing energy. This technique is a very promising option as potential treatment for several pathologies, including musculoskeletal (MSK) disorders. Apart from clinical applications, MRgFUS technology is the result of long, heavy and cumulative efforts exploring the effects of ultrasound on biological tissues and function, the generation of focused ultrasound and treatment monitoring by MRI. The aim of this article is to give an updated overview on a "new" interventional technique and on its applications for MSK and allied sciences
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