AIMC Topic: Image Processing, Computer-Assisted

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Image super-resolution with an enhanced group convolutional neural network.

Neural networks : the official journal of the International Neural Network Society
CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In thi...

Multimodal image synthesis based on disentanglement representations of anatomical and modality specific features, learned using uncooperative relativistic GAN.

Medical image analysis
Growing number of methods for attenuation-coefficient map estimation from magnetic resonance (MR) images have recently been proposed because of the increasing interest in MR-guided radiotherapy and the introduction of positron emission tomography (PE...

A parallel network utilizing local features and global representations for segmentation of surgical instruments.

International journal of computer assisted radiology and surgery
PURPOSE: Automatic image segmentation of surgical instruments is a fundamental task in robot-assisted minimally invasive surgery, which greatly improves the context awareness of surgeons during the operation. A novel method based on Mask R-CNN is pro...

DXM-TransFuse U-net: Dual cross-modal transformer fusion U-net for automated nerve identification.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate nerve identification is critical during surgical procedures to prevent damage to nerve tissues. Nerve injury can cause long-term adverse effects for patients, as well as financial overburden. Birefringence imaging is a noninvasive technique ...

Deep-learning-based methods of attenuation correction for SPECT and PET.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of single-photon emission computed tomography (SPECT) and positron emission tomography (PET). In clinical practice, computed tomography (CT) is utilized to gene...

Benchmarking Deep Learning Models for Tooth Structure Segmentation.

Journal of dental research
A wide range of deep learning (DL) architectures with varying depths are available, with developers usually choosing one or a few of them for their specific task in a nonsystematic way. Benchmarking (i.e., the systematic comparison of state-of-the ar...

Deep learning for image-based liver analysis - A comprehensive review focusing on malignant lesions.

Artificial intelligence in medicine
Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver l...

Automatic Cobb angle measurement method based on vertebra segmentation by deep learning.

Medical & biological engineering & computing
The accuracy of the Cobb measurement is essential for the diagnosis and treatment of scoliosis. Manual measurement is however influenced by the observer variability hence affecting progression evaluation. In this paper, we propose a fully automatic C...

Bridging 2D and 3D segmentation networks for computation-efficient volumetric medical image segmentation: An empirical study of 2.5D solutions.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Recently, deep convolutional neural networks have achieved great success for medical image segmentation. However, unlike segmentation of natural images, most medical images such as MRI and CT are volumetric data. In order to make full use of volumetr...