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

Acquisition models in intraoperative positron surface imaging

International audienc

Pruning strategies for efficient online globally consistent mosaicking in fetoscopy

Twin-to-twin transfusion syndrome is a condition in which identical twins share a certain pattern of vascular connections in the placenta. This leads to an imbalance in the blood flow that, if not treated, may result in a fatal outcome for both twins. To treat this condition, a surgeon explores the placenta with a fetoscope to find and photocoagulate all intertwin vascular connections. However, the reduced field of view of the fetoscope complicates their localization and general overview. A much more effective exploration could be achieved with an online mosaic created at exploration time. Currently, accurate, globally consistent algorithms such as bundle adjustment cannot be used due to their offline nature, while online algorithms lack sufficient accuracy. We introduce two pruning strategies facilitating the use of bundle adjustment in a sequential fashion: (1) a technique that efficiently exploits the potential of using an electromagnetic tracking system to avoid unnecessary matching attempts between spatially inconsistent image pairs, and (2) an aggregated representation of images, which we refer to as superframes, that allows decreasing the computational complexity of a globally consistent approach. Quantitative and qualitative results on synthetic and phantom-based datasets demonstrate a better trade-off between efficiency and accuracy

Medical-grade sterilizable target for fluid-immersed fetoscope optical distortion calibration

We have developed a calibration target for use with fluid-immersed endoscopes within the context of the GIFT-Surg (Guided Instrumentation for Fetal Therapy and Surgery) project. One of the aims of this project is to engineer novel, real-time image processing methods for intra-operative use in the treatment of congenital birth defects, such as spina bifida and the twin-to-twin transfusion syndrome. The developed target allows for the sterility-preserving optical distortion calibration of endoscopes within a few minutes. Good optical distortion calibration and compensation are important for mitigating undesirable effects like radial distortions, which not only hamper accurate imaging using existing endoscopic technology during fetal surgery, but also make acquired images less suitable for potentially very useful image computing applications, like real-time mosaicing. In this paper proposes a novel fabrication method to create an affordable, sterilizable calibration target suitable for use in a clinical setup. This method involves etching a calibration pattern by laser cutting a sandblasted stainless steel sheet. This target was validated using the camera calibration module provided by OpenCV, a state-of-the-art software library popular in the computer vision community

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

We have developed a calibration target for use with fluid-immersed endoscopes within the context of the GIFT-Surg (Guided Instrumentation for Fetal Therapy and Surgery) project. One of the aims of this project is to engineer novel, real-time image processing methods for intra-operative use in the treatment of congenital birth defects, such as spina bifida and the twin-to-twin transfusion syndrome. The developed target allows for the sterility-preserving optical distortion calibration of endoscopes within a few minutes. Good optical distortion calibration and compensation are important for mitigating undesirable effects like radial distortions, which not only hamper accurate imaging using existing endoscopic technology during fetal surgery, but also make acquired images less suitable for potentially very useful image computing applications, like real-time mosaicing. In this paper proposes a novel fabrication method to create an affordable, sterilizable calibration target suitable for use in a clinical setup. This method involves etching a calibration pattern by laser cutting a sandblasted stainless steel sheet. This target was validated using the camera calibration module provided by OpenCV, a state-of-the-art software library popular in the computer vision community.status: publishe

GIFT-Cloud:A data sharing and collaboration platform for medical imaging research

AbstractObjectivesClinical imaging data are essential for developing research software for computer-aided diagnosis, treatment planning and image-guided surgery, yet existing systems are poorly suited for data sharing between healthcare and academia: research systems rarely provide an integrated approach for data exchange with clinicians; hospital systems are focused towards clinical patient care with limited access for external researchers; and safe haven environments are not well suited to algorithm development. We have established GIFT-Cloud, a data and medical image sharing platform, to meet the needs of GIFT-Surg, an international research collaboration that is developing novel imaging methods for fetal surgery. GIFT-Cloud also has general applicability to other areas of imaging research.MethodsGIFT-Cloud builds upon well-established cross-platform technologies. The Server provides secure anonymised data storage, direct web-based data access and a REST API for integrating external software. The Uploader provides automated on-site anonymisation, encryption and data upload. Gateways provide a seamless process for uploading medical data from clinical systems to the research server.ResultsGIFT-Cloud has been implemented in a multi-centre study for fetal medicine research. We present a case study of placental segmentation for pre-operative surgical planning, showing how GIFT-Cloud underpins the research and integrates with the clinical workflow.ConclusionsGIFT-Cloud simplifies the transfer of imaging data from clinical to research institutions, facilitating the development and validation of medical research software and the sharing of results back to the clinical partners. GIFT-Cloud supports collaboration between multiple healthcare and research institutions while satisfying the demands of patient confidentiality, data security and data ownership

GIFT-Cloud: A data sharing and collaboration platform for medical imaging research

OBJECTIVES: Clinical imaging data are essential for developing research software for computer-aided diagnosis, treatment planning and image-guided surgery, yet existing systems are poorly suited for data sharing between healthcare and academia: research systems rarely provide an integrated approach for data exchange with clinicians; hospital systems are focused towards clinical patient care with limited access for external researchers; and safe haven environments are not well suited to algorithm development. We have established GIFT-Cloud, a data and medical image sharing platform, to meet the needs of GIFT-Surg, an international research collaboration that is developing novel imaging methods for fetal surgery. GIFT-Cloud also has general applicability to other areas of imaging research. METHODS: GIFT-Cloud builds upon well-established cross-platform technologies. The Server provides secure anonymised data storage, direct web-based data access and a REST API for integrating external software. The Uploader provides automated on-site anonymisation, encryption and data upload. Gateways provide a seamless process for uploading medical data from clinical systems to the research server. RESULTS: GIFT-Cloud has been implemented in a multi-centre study for fetal medicine research. We present a case study of placental segmentation for pre-operative surgical planning, showing how GIFT-Cloud underpins the research and integrates with the clinical workflow. CONCLUSIONS: GIFT-Cloud simplifies the transfer of imaging data from clinical to research institutions, facilitating the development and validation of medical research software and the sharing of results back to the clinical partners. GIFT-Cloud supports collaboration between multiple healthcare and research institutions while satisfying the demands of patient confidentiality, data security and data ownership.publisher: Elsevier articletitle: GIFT-Cloud: A data sharing and collaboration platform for medical imaging research journaltitle: Computer Methods and Programs in Biomedicine articlelink: http://dx.doi.org/10.1016/j.cmpb.2016.11.004 content_type: article copyright: © 2016 The Authors. Published by Elsevier Ireland Ltd.status: publishe

Real-time mosaicing of fetoscopic videos using SIFT

Fetoscopic laser photo-coagulation of the placental vascular anastomoses remains the most effective therapy for twin-to-twin transfusion syndrome (TTTS) in monochorionic twin pregnancies. However, to ensure the success of the intervention, complete photo-coagulation of all anastomoses is needed. This is made difficult by the limited field of view of the fetoscopic video guidance, which hinders the surgeon’s ability to locate all the anastomoses. A potential solution to this problem is to expand the field of view of the placental surface by creating a mosaic from overlapping fetoscopic images. This mosaic can then be used for anastomoses localization and spatial orientation during surgery. However, this requires accurate and fast algorithms that can operate within the real-time constraints of fetal surgery. In this work, we present an image mosaicing framework that leverages the parallelism of modern GPUs and can process clinical fetoscopic images in real-time. Initial qualitative results on ex-vivo placental images indicate that the proposed framework can generate clinically useful mosaics from fetoscopic videos in real-time