The Experts below are selected from a list of 27276 Experts worldwide ranked by ideXlab platform

Michael Rudzsky - One of the best experts on this subject based on the ideXlab platform.

  • cortex segmentation a fast variational geometric approach
    IEEE Transactions on Medical Imaging, 2002
    Co-Authors: Roman Goldenberg, Ron Kimmel, Ehud Rivlin, Michael Rudzsky
    Abstract:

    An automatic cortical gray matter segmentation from a three-dimensional (3-D) brain Images [magnetic resonance (MR) or computed tomography] is a well known problem in Medical Image Processing. In this paper, we first formulate it as a geometric variational problem for propagation of two coupled bounding surfaces. An efficient numerical scheme is then used to implement the geodesic active surface model. Experimental results of cortex segmentation on real 3-D MR data are provided.

  • cortex segmentation a fast variational geometric approach
    Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision, 2001
    Co-Authors: Roman Goldenberg, Ron Kimmel, Ehud Rivlin, Michael Rudzsky
    Abstract:

    An automatic cortical gray matter segmentation from three-dimensional brain Images (MR or CT) is a well known problem in Medical Image Processing. We formulate it as a geometric variational problem for propagation of two coupled bounding surfaces. An efficient numerical scheme is used to implement the geodesic active surface model. Experimental results of cortex segmentation on real three-dimensional MR data are provided.

Sebastien Ourselin - One of the best experts on this subject based on the ideXlab platform.

  • torchio a python library for efficient loading preProcessing augmentation and patch based sampling of Medical Images in deep learning
    Computer Methods and Programs in Biomedicine, 2021
    Co-Authors: Fernando Perezgarcia, Rachel Sparks, Sebastien Ourselin
    Abstract:

    Abstract Background and objective Processing of Medical Images such as MRI or CT presents different challenges compared to RGB Images typically used in computer vision. These include a lack of labels for large datasets, high computational costs, and the need of metadata to describe the physical properties of voxels. Data augmentation is used to artificially increase the size of the training datasets. Training with Image subvolumes or patches decreases the need for computational power. Spatial metadata needs to be carefully taken into account in order to ensure a correct alignment and orientation of volumes. Methods We present TorchIO, an open-source Python library to enable efficient loading, preProcessing, augmentation and patch-based sampling of Medical Images for deep learning. TorchIO follows the style of PyTorch and integrates standard Medical Image Processing libraries to efficiently process Images during training of neural networks. TorchIO transforms can be easily composed, reproduced, traced and extended. Most transforms can be inverted, making the library suitable for test-time augmentation and estimation of aleatoric uncertainty in the context of segmentation. We provide multiple generic preProcessing and augmentation operations as well as simulation of MRI-specific artifacts. Results Source code, comprehensive tutorials and extensive documentation for TorchIO can be found at http://torchio.rtfd.io/ . The package can be installed from the Python Package Index (PyPI) running pip install torchio . It includes a command-line interface which allows users to apply transforms to Image files without using Python. Additionally, we provide a graphical user interface within a TorchIO extension in 3D Slicer to visualize the effects of transforms. Conclusion TorchIO was developed to help researchers standardize Medical Image Processing pipelines and allow them to focus on the deep learning experiments. It encourages good open-science practices, as it supports experiment reproducibility and is version-controlled so that the software can be cited precisely. Due to its modularity, the library is compatible with other frameworks for deep learning with Medical Images.

  • torchio a python library for efficient loading preProcessing augmentation and patch based sampling of Medical Images in deep learning
    arXiv: Image and Video Processing, 2020
    Co-Authors: Fernando Perezgarcia, Rachel Sparks, Sebastien Ourselin
    Abstract:

    We present TorchIO, an open-source Python library for efficient loading, preProcessing, augmentation and patch-based sampling of Medical Images for deep learning. It follows the design of PyTorch and relies on standard Medical Image Processing libraries such as SimpleITK or NiBabel to efficiently process large 3D Images during the training of convolutional neural networks. We provide multiple generic as well as magnetic-resonance-imaging-specific operations for preProcessing and augmentation of Medical Images. TorchIO is an open-source project with code, comprehensive examples and extensive documentation shared at this https URL.

Hamid Soltanianzadeh - One of the best experts on this subject based on the ideXlab platform.

  • web based interactive 2d 3d Medical Image Processing and visualization software
    Computer Methods and Programs in Biomedicine, 2010
    Co-Authors: Seyyed Ehsan Mahmoudi, Alireza Akhondiasl, Roohollah Rahmani, Shahrooz Faghihroohi, Vahid Taimouri, Ahmad Sabouri, Hamid Soltanianzadeh
    Abstract:

    There are many Medical Image Processing software tools available for research and diagnosis purposes. However, most of these tools are available only as local applications. This limits the accessibility of the software to a specific machine, and thus the data and Processing power of that application are not available to other workstations. Further, there are operating system and Processing power limitations which prevent such applications from running on every type of workstation. By developing web-based tools, it is possible for users to access the Medical Image Processing functionalities wherever the internet is available. In this paper, we introduce a pure web-based, interactive, extendable, 2D and 3D Medical Image Processing and visualization application that requires no client installation. Our software uses a four-layered design consisting of an algorithm layer, web-user-interface layer, server communication layer, and wrapper layer. To compete with extendibility of the current local Medical Image Processing software, each layer is highly independent of other layers. A wide range of Medical Image preProcessing, registration, and segmentation methods are implemented using open source libraries. Desktop-like user interaction is provided by using AJAX technology in the web-user-interface. For the visualization functionality of the software, the VRML standard is used to provide 3D features over the web. Integration of these technologies has allowed implementation of our purely web-based software with high functionality without requiring powerful computational resources in the client side. The user-interface is designed such that the users can select appropriate parameters for practical research and clinical studies.

Roman Goldenberg - One of the best experts on this subject based on the ideXlab platform.

  • cortex segmentation a fast variational geometric approach
    IEEE Transactions on Medical Imaging, 2002
    Co-Authors: Roman Goldenberg, Ron Kimmel, Ehud Rivlin, Michael Rudzsky
    Abstract:

    An automatic cortical gray matter segmentation from a three-dimensional (3-D) brain Images [magnetic resonance (MR) or computed tomography] is a well known problem in Medical Image Processing. In this paper, we first formulate it as a geometric variational problem for propagation of two coupled bounding surfaces. An efficient numerical scheme is then used to implement the geodesic active surface model. Experimental results of cortex segmentation on real 3-D MR data are provided.

  • cortex segmentation a fast variational geometric approach
    Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision, 2001
    Co-Authors: Roman Goldenberg, Ron Kimmel, Ehud Rivlin, Michael Rudzsky
    Abstract:

    An automatic cortical gray matter segmentation from three-dimensional brain Images (MR or CT) is a well known problem in Medical Image Processing. We formulate it as a geometric variational problem for propagation of two coupled bounding surfaces. An efficient numerical scheme is used to implement the geodesic active surface model. Experimental results of cortex segmentation on real three-dimensional MR data are provided.

Zhijian Song - One of the best experts on this subject based on the ideXlab platform.

  • An Augmentation Strategy for Medical Image Processing Based on Statistical Shape Model and 3D Thin Plate Spline for Deep Learning
    IEEE Access, 2019
    Co-Authors: Zhixian Tang, Manning Wang, Kun Chen, Zhijian Song
    Abstract:

    At present, deep learning has been widely adopted in Medical Image Processing. However, the current deep neural networks depend on a large number of labeled training data, but Medical Images segmentation tasks often suffer from the problem of small quantity of labeled data because labeling Medical Images is a very expensive and time-consuming task. In order to overcome this difficulty, this paper proposes a new Image augmentation strategy based on statistical shape model and three-dimensional thin plate spline, which can generate many simulated Images from a small number of real Images. Firstly, the shape information of the real labeled Images is modeled with the statistical shape model, and a series of simulated shapes are generated by sampling from this model. Secondly, the simulated shapes are filled with texture using three-dimensional thin plate spline to generate the simulated Images. Finally, the simulated Images and the real Images are used together for training deep neural networks. The proposed framework is a general data augmentation method that can be used in any anatomical structure segmentation tasks with any deep neural network architecture. We used two different datasets, including prostate MRI dataset and liver CT dataset, and used two different deep network structures, including multi-scale 3D Convolutional Neural Networks (multi-scale 3D CNN) and U-net. The experimental results showed that the proposed data augmentation strategy can improve the accuracy of existing segmentation algorithms based on deep neural networks.