Registration of Untracked 2D Laparoscopic Ultrasound to CT Images of the Liver using Multi-Labelled Content-Based Image Retrieval

Laparoscopic Ultrasound (LUS) is recommended as a standard-of-care when performing laparoscopic liver resections as it images sub-surface structures such as tumours and major vessels. Given that LUS probes are difficult to handle and some tumours are iso-echoic, registration of LUS images to a pre-operative CT has been proposed as an image-guidance method. This registration problem is particularly challenging due to the small field of view of LUS, and usually depends on both a manual initialisation and tracking to compose a volume, hindering clinical translation. In this paper, we extend a proposed registration approach using Content-Based Image Retrieval (CBIR), removing the requirement for tracking or manual initialisation. Pre-operatively, a set of possible LUS planes is simulated from CT and a descriptor generated for each image. Then, a Bayesian framework is employed to estimate the most likely sequence of CT simulations that matches a series of LUS images. We extend our CBIR formulation to use multiple labelled objects and constrain the registration by separating liver vessels into portal vein and hepatic vein branches. The value of this new labeled approach is demonstrated in retrospective data from 5 patients. Results show that, by including a series of 5 untracked images in time, a single LUS image can be registered with accuracies ranging from 5.7 to 16.4 mm with a success rate of 78%. Initialisation of the LUS to CT registration with the proposed framework could potentially enable the clinical translation of these image fusion techniques

Computer-assisted access to the kidney

OBJECTIVES: The aim of this paper is to introduce the principles of computer-assisted access to the kidney. The system provides the surgeon with a pre-operative 3D planning on computed tomography (CT) images. After a rigid registration with space-localized ultrasound (US) data, preoperative planning can be transferred to the intra-operative conditions and an intuitive man-machine interface allows the user to perform a puncture. MATERIAL AND METHODS: Both CT and US images of informed normal volunteer were obtained to perform calculation on the accuracy of registration and punctures were carried out on a kidney phantom to measure the precision of the whole of the system. RESULTS: We carried out millimetric registrations on real data and guidance experiments on a kidney phantom showed encouraging results of 4.7 mm between planned and reached targets. We noticed that the most significant error was related to the needle deflection during the puncture. CONCLUSION: Preliminary results are encouraging. Further work will be undertaken to improve efficiency and accuracy, and to take breathing into account

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

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

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