approaches and methodologies for creating physical prototypes of patient-specific cardiac structures, with particular reference to most critical phases such as: 3D image acquisition, interactive image segmentation and restoration, interactive 3D model reconstruction, physical prototyping through additive ...
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction [paper] Image Reconstruction by Domain Transform Manifold Learning [paper] Human-level CMR Image Analysis with Deep Fully Convolutional Networks [paper] A Novel Automatic Segmentation Method to Quantify the Effects of Spi...
Fig. 2. Overview schematic of the standard JP3D encoder. Download: Download full-size image Fig. 3. Schematic representation of the JP3D+DA encoder. Table 1. Description of the wavelet kernels used for the experiments. KernelMKPKUGLGHikcP,i,kcU,i,k 5×3 1 1 1 1 1 0 −1, 1 0.5...
Medical image reconstruction from devices like CT, MRI, and ultrasound gives clinicians the ability to look deep inside the body in 3D to make a diagnosis. Doing this requires intense computational processing of large amounts of data from multiple sensors or 2D images. GPUs and accelerated computi...
(2009) for an overview) and histogram pyramids by Ziegler et al. (2006). 3. Segmentation methods In this section, several commonly used image segmentation methods are presented and discussed in terms of GPU computing. All of these segmentation methods can be used on both 2D and 3D images, ...
Medical image reconstruction from devices like CT, MRI, and ultrasound gives clinicians the ability to look deep inside the body in 3D to make a diagnosis. Doing this requires intense computational processing of large amounts of data from multiple sensors or 2D images. GPUs and accelerated computi...
The Medical Image Analysis Laboratory Super-Resolution ToolKit (MIALSRTK) consists of a set of C++ and Python processing and workflow tools necessary to perform motion-robust super-resolution fetal MRI reconstruction in the BIDS Apps framework. ...
Subsequently, the transformer encoder is pre-trained with tailored, self-supervised tasks by leveraging various proxy tasks such as image inpainting, 3D rotation prediction, and contrastive learning (See Fig. 1 for an overview). Specifically, the human body presents naturally consistent contextual ...
Fig. 4. Musculoskeletal model after femur reconstruction in an intermediate frame of a gait cycle (image courtesy of M. Viceconti; reprinted from Taddei et al. (2012) with the permission of Elsevier). The X-ray images show the reconstructed femur immediately after surgery (a) and after remov...
In medical image processing, transformers are successfully used in image segmentation, classification, reconstruction, and diagnosis. In this paper, we mainly expound on the transformer principle and its application in medical imaging. Specifically, we first introduce the basic principles and model ...