Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Tracking and Mapping in Medical Computer Vision: A Review

As computer vision algorithms are becoming more capable, their applications in clinical systems will become more pervasive. These applications include diagnostics such as colonoscopy and bronchoscopy, guiding biopsies and minimally invasive interventions and surgery, automating instrument motion and providing image guidance using pre-operative scans. Many of these applications depend on the specific visual nature of medical scenes and require designing and applying algorithms to perform in this environment. In this review, we provide an update to the field of camera-based tracking and scene mapping in surgery and diagnostics in medical computer vision. We begin with describing our review process, which results in a final list of 515 papers that we cover. We then give a high-level summary of the state of the art and provide relevant background for those who need tracking and mapping for their clinical applications. We then review datasets provided in the field and the clinical needs therein. Then, we delve in depth into the algorithmic side, and summarize recent developments, which should be especially useful for algorithm designers and to those looking to understand the capability of off-the-shelf methods. We focus on algorithms for deformable environments while also reviewing the essential building blocks in rigid tracking and mapping since there is a large amount of crossover in methods. Finally, we discuss the current state of the tracking and mapping methods along with needs for future algorithms, needs for quantification, and the viability of clinical applications in the field. We conclude that new methods need to be designed or combined to support clinical applications in deformable environments, and more focus needs to be put into collecting datasets for training and evaluation.Comment: 31 pages, 17 figure

Environment-aware non-rigid registration in surgery using physics-based simulation

International audienceThis paper presents a system for capturing the deformations of soft objects undergoing elastic deformations and contacts with their environment, using image and point cloud data provided by an RGB-D sensor. We improve upon previous works by integrating environment constraints in the frame-by-frame registration process. The approach combines a physics-based elastic model of the considered objects, computed in real-time using an optimized Finite Element Method (FEM), which is driven by surface constraints on the objects. Additional forces, such as gravity are added. A case study in open surgery on the liver is here described. Yet in this case a major improvement in the accuracy of the registration is provided by the integration of anatomical shape constraints, which are naturally hidden from the RGB-D camera, and that we account for through a registration with the pre-operative CT data. With a comparative study, we demonstrate the relevance of our method in a real world application mimicking an open surgery scenario where the liver has to be tracked to provide an augmented reality view

integration of enhanced optical tracking techniques and imaging in igrt

Patient setup/Optical tracking/IGRT/Treatment surveillance. In external beam radiotherapy, modern technologies for dynamic dose delivery and beam conformation provide high selectivity in radiation dose administration to the pathological volume. A comparable accuracy level is needed in the 3-D localization of tumor and organs at risk (OARs), in order to accomplish the planned dose distribution in the reality of each irradiation session. In-room imaging techniques for patient setup verification and tumor targeting may benefit of the combined daily use of optical tracking technologies, supported by techniques for the detection and compensation of organ motion events. Multiple solutions to enhance the use of optical tracking for the on-line correction of target localization uncertainties are described, with specific emphasis on the compensation of setup errors, breathing movements and non-rigid deformations. The final goal is the implementation of customized protocols where appropriate external landmarks, to be tracked in real-time by means of noninvasive optical devices, are selected as a function of inner target localization. The presented methodology features high accuracy in patient setup optimization, also providing a valuable tool for on-line patient surveillance, taking into account both breathing and deformation effects. The methodic application of optical tracking is put forward to represent a reliable and low cost procedure for the reduction of safety margins, once the patient-specific correlation between external landmarks and inner structures has been established. Therefore, the integration of optical tracking with in-room imaging devices is proposed as a way to gain higher confidence in the framework of Image Guided Radiation Therapy (IGRT) treatments

IGRT and motion management during lung SBRT delivery.

Patient motion can cause misalignment of the tumour and toxicities to the healthy lung tissue during lung stereotactic body radiation therapy (SBRT). Any deviations from the reference setup can miss the target and have acute toxic effects on the patient with consequences onto its quality of life and survival outcomes. Correction for motion, either immediately prior to treatment or intra-treatment, can be realized with image-guided radiation therapy (IGRT) and motion management devices. The use of these techniques has demonstrated the feasibility of integrating complex technology with clinical linear accelerator to provide a higher standard of care for the patients and increase their quality of life

Image-Aligned Dynamic Liver Reconstruction Using Intra-Operative Field of Views for Minimal Invasive Surgery

Available online on 30 November 2018. Author's post-print on open access repository after an embargo period of 12 months2019-11-3

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

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