AIMC Topic: Imaging, Three-Dimensional

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Weakly-supervised convolutional neural networks for multimodal image registration.

Medical image analysis
One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspo...

A Novel Morphological Marker for the Analysis of Molecular Activities at the Single-cell Level.

Cell structure and function
For more than a century, hematoxylin and eosin (H&E) staining has been the de facto standard for histological studies. Consequently, the legacy of histological knowledge is largely based on H&E staining. Due to the recent advent of multi-photon excit...

Sex Determination of 3D Skull Based on a Novel Unsupervised Learning Method.

Computational and mathematical methods in medicine
In law enforcement investigation cases, sex determination from skull morphology is one of the important steps in establishing the identity of an individual from unidentified human skeleton. To our knowledge, existing studies of sex determination of t...

Towards intelligent robust detection of anatomical structures in incomplete volumetric data.

Medical image analysis
Robust and fast detection of anatomical structures represents an important component of medical image analysis technologies. Current solutions for anatomy detection are based on machine learning, and are generally driven by suboptimal and exhaustive ...

Deep-learned 3D black-blood imaging using automatic labelling technique and 3D convolutional neural networks for detecting metastatic brain tumors.

Scientific reports
Black-blood (BB) imaging is used to complement contrast-enhanced 3D gradient-echo (CE 3D-GRE) imaging for detecting brain metastases, requiring additional scan time. In this study, we proposed deep-learned 3D BB imaging with an auto-labelling techniq...

Classification of pressure ulcer tissues with 3D convolutional neural network.

Medical & biological engineering & computing
A 3D convolution neural network (CNN) of deep learning architecture is supplied with essential visual features to accurately classify and segment granulation, necrotic eschar, and slough tissues in pressure ulcer color images. After finding a region ...

3D freehand ultrasound without external tracking using deep learning.

Medical image analysis
This work aims at creating 3D freehand ultrasound reconstructions from 2D probes with image-based tracking, therefore not requiring expensive or cumbersome external tracking hardware. Existing model-based approaches such as speckle decorrelation only...

Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features.

Medical image analysis
Sub-cortical brain structure segmentation in Magnetic Resonance Images (MRI) has attracted the interest of the research community for a long time as morphological changes in these structures are related to different neurodegenerative disorders. Howev...

Dual-modality endoscopic probe for tissue surface shape reconstruction and hyperspectral imaging enabled by deep neural networks.

Medical image analysis
Surgical guidance and decision making could be improved with accurate and real-time measurement of intra-operative data including shape and spectral information of the tissue surface. In this work, a dual-modality endoscopic system has been proposed ...

Peripheral bronchial identification on chest CT using unsupervised machine learning.

International journal of computer assisted radiology and surgery
PURPOSE: To automatically identify small- to medium-diameter bronchial segments distributed throughout the lungs.