AIMC Topic: Imaging, Three-Dimensional

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VP-Nets : Efficient automatic localization of key brain structures in 3D fetal neurosonography.

Medical image analysis
Three-dimensional (3D) fetal neurosonography is used clinically to detect cerebral abnormalities and to assess growth in the developing brain. However, manual identification of key brain structures in 3D ultrasound images requires expertise to perfor...

Automatic planning of needle placement for robot-assisted percutaneous procedures.

International journal of computer assisted radiology and surgery
PURPOSE: Percutaneous procedures allow interventional radiologists to perform diagnoses or treatments guided by an imaging device, typically a computed tomography (CT) scanner with a high spatial resolution. To reduce exposure to radiations and impro...

Structural inference embedded adversarial networks for scene parsing.

PloS one
Explicit structural inference is one key point to improve the accuracy of scene parsing. Meanwhile, adversarial training method is able to reinforce spatial contiguity in output segmentations. To take both advantages of the structural learning and ad...

Deformable Image Registration Using a Cue-Aware Deep Regression Network.

IEEE transactions on bio-medical engineering
SIGNIFICANCE: Analysis of modern large-scale, multicenter or diseased data requires deformable registration algorithms that can cope with data of diverse nature.

Automatic Segmentation of Acute Ischemic Stroke From DWI Using 3-D Fully Convolutional DenseNets.

IEEE transactions on medical imaging
Acute ischemic stroke is recognized as a common cerebral vascular disease in aging people. Accurate diagnosis and timely treatment can effectively improve the blood supply of the ischemic area and reduce the risk of disability or even death. Understa...

3-D Consistent and Robust Segmentation of Cardiac Images by Deep Learning With Spatial Propagation.

IEEE transactions on medical imaging
We propose a method based on deep learning to perform cardiac segmentation on short axis Magnetic resonance imaging stacks iteratively from the top slice (around the base) to the bottom slice (around the apex). At each iteration, a novel variant of t...

Super-resolution musculoskeletal MRI using deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a super-resolution technique using convolutional neural networks for generating thin-slice knee MR images from thicker input slices, and compare this method with alternative through-plane interpolation methods.

A deep 3D residual CNN for false-positive reduction in pulmonary nodule detection.

Medical physics
PURPOSE: The automatic detection of pulmonary nodules using CT scans improves the efficiency of lung cancer diagnosis, and false-positive reduction plays a significant role in the detection. In this paper, we focus on the false-positive reduction tas...

Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images.

Medical physics
PURPOSE: Accurate 3D image segmentation is a crucial step in radiation therapy planning of head and neck tumors. These segmentation results are currently obtained by manual outlining of tissues, which is a tedious and time-consuming procedure. Automa...