AIMC Topic: Imaging, Three-Dimensional

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Image-Guided Human Reconstruction via Multi-Scale Graph Transformation Networks.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
3D human reconstruction from a single image is a challenging problem. Existing methods have difficulties to infer 3D clothed human models with consistent topologies for various poses. In this paper, we propose an efficient and effective method using ...

MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning.

Medical image analysis
Segmentation of organs or lesions from medical images plays an essential role in many clinical applications such as diagnosis and treatment planning. Though Convolutional Neural Networks (CNN) have achieved the state-of-the-art performance for automa...

EMDS-5: Environmental Microorganism image dataset Fifth Version for multiple image analysis tasks.

PloS one
Environmental Microorganism Data Set Fifth Version (EMDS-5) is a microscopic image dataset including original Environmental Microorganism (EM) images and two sets of Ground Truth (GT) images. The GT image sets include a single-object GT image set and...

Simultaneous Direct Depth Estimation and Synthesis Stereo for Single Image Plant Root Reconstruction.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Plant roots are the main conduit to its interaction with the physical and biological environment. A 3D root system architecture can provide fundamental and applied knowledge of a plant's ability to thrive, but the construction of 3D structures for th...

VolumeNet: A Lightweight Parallel Network for Super-Resolution of MR and CT Volumetric Data.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Deep learning-based super-resolution (SR) techniques have generally achieved excellent performance in the computer vision field. Recently, it has been proven that three-dimensional (3D) SR for medical volumetric data delivers better visual results th...

Deep Learning for the Detection of Breast Cancers on Chest Computed Tomography.

Clinical breast cancer
BACKGROUND: Incidental breast cancers can be detected on chest computed tomography (CT) scans. With the use of deep learning, the sensitivity of incidental breast cancer detection on chest CT would improve. This study aimed to evaluate the performanc...

Improving axial resolution in Structured Illumination Microscopy using deep learning.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Structured Illumination Microscopy (SIM) is a widespread methodology to image live and fixed biological structures smaller than the diffraction limits of conventional optical microscopy. Using recent advances in image up-scaling through deep learning...

A deep learning approach for 2D ultrasound and 3D CT/MR image registration in liver tumor ablation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Liver tumor ablation is often guided by ultrasound (US). Due to poor image quality, intraoperative US is fused with preoperative computed tomography or magnetic tomography (CT/MR) images to provide visual guidance. As of tod...

Mask-Guided Convolutional Neural Network for Breast Tumor Prognostic Outcome Prediction on 3D DCE-MR Images.

Journal of digital imaging
In this proof-of-concept work, we have developed a 3D-CNN architecture that is guided by the tumor mask for classifying several patient-outcomes in breast cancer from the respective 3D dynamic contrast-enhanced MRI (DCE-MRI) images. The tumor masks o...

Three-dimensional virtual planning in mandibular advancement surgery: Soft tissue prediction based on deep learning.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
The study aimed at developing a deep-learning (DL)-based algorithm to predict the virtual soft tissue profile after mandibular advancement surgery, and to compare its accuracy with the mass tensor model (MTM). Subjects who underwent mandibular advanc...