AIMC Topic: Image Processing, Computer-Assisted

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Performance improvement of weakly supervised fully convolutional networks by skip connections for brain structure segmentation.

Medical physics
PURPOSE: For the planning and navigation of neurosurgery, we have developed a fully convolutional network (FCN)-based method for brain structure segmentation on magnetic resonance (MR) images. The capability of an FCN depends on the quality of the tr...

Artificial intelligence for solid tumour diagnosis in digital pathology.

British journal of pharmacology
Tumour diagnosis relies on the visual examination of histological slides by pathologists through a microscope eyepiece. Digital pathology, the digitalization of histological slides at high magnification with slides scanners, has raised the opportunit...

Accurate and robust sparse-view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL-PICCS).

Medical physics
BACKGROUND: Sparse-view CT image reconstruction problems encountered in dynamic CT acquisitions are technically challenging. Recently, many deep learning strategies have been proposed to reconstruct CT images from sparse-view angle acquisitions showi...

Micro-Computed Tomography-Guided Artificial Intelligence for Pulp Cavity and Tooth Segmentation on Cone-beam Computed Tomography.

Journal of endodontics
INTRODUCTION: This study proposes a novel data pipeline based on micro-computed tomographic (micro-CT) data for training the U-Net network to realize the automatic and accurate segmentation of the pulp cavity and tooth on cone-beam computed tomograph...

Predicting carcass cut yields in cattle from digital images using artificial intelligence.

Meat science
Deep Learning (DL) has proven to be a successful tool for many image classification problems but has yet to be applied to carcass images. The aim of this study was to train DL models to predict carcass cut yields and compare predictions to more stand...

CT slice alignment to whole-body reference geometry by convolutional neural network.

Physical and engineering sciences in medicine
Volumetric medical imaging lacks a standardised coordinate geometry which links image frame-of-reference to specific anatomical regions. This results in an inability to locate anatomy in medical images without visual assessment and precludes a variet...

A New General Maximum Intensity Projection Technology via the Hybrid of U-Net and Radial Basis Function Neural Network.

Journal of digital imaging
Maximum intensity projection (MIP) technology is a computer visualization method that projects three-dimensional spatial data on a visualization plane. According to the specific purposes, the specific lab thickness and direction can be selected. This...

Deformable registration of chest CT images using a 3D convolutional neural network based on unsupervised learning.

Journal of applied clinical medical physics
PURPOSE: The deformable registration of 3D chest computed tomography (CT) images is one of the most important tasks in the field of medical image registration. However, the nonlinear deformation and large-scale displacement of lung tissues caused by ...

Depth Estimation from Light Field Geometry Using Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Depth estimation based on light field imaging is a new methodology that has succeeded the traditional binocular stereo matching and depth from monocular images. Significant progress has been made in light-field depth estimation. Nevertheless, the bal...

Glomerular disease classification and lesion identification by machine learning.

Biomedical journal
BACKGROUND: Classification of glomerular diseases and identification of glomerular lesions require careful morphological examination by experienced nephropathologists, which is labor-intensive, time-consuming, and prone to interobserver variability. ...