AIMC Topic: Imaging, Three-Dimensional

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Three-dimensional deep learning to automatically generate cranial implant geometry.

Scientific reports
We present a 3D deep learning framework that can generate a complete cranial model using a defective one. The Boolean subtraction between these two models generates the geometry of the implant required for surgical reconstruction. There is little or ...

Convolutional mesh autoencoders for the 3-dimensional identification of FGFR-related craniosynostosis.

Scientific reports
Clinical diagnosis of craniofacial anomalies requires expert knowledge. Recent studies have shown that artificial intelligence (AI) based facial analysis can match the diagnostic capabilities of expert clinicians in syndrome identification. In genera...

Compound W-Net with Fully Accumulative Residual Connections for Liver Segmentation Using CT Images.

Computational and mathematical methods in medicine
Computed tomography (CT) is a common modality for liver diagnosis, treatment, and follow-up process. Providing accurate liver segmentation using CT images is a crucial step towards those tasks. In this paper, we propose a stacked 2-U-Nets model with ...

Parallax attention stereo matching network based on the improved group-wise correlation stereo network.

PloS one
Recent stereo matching methods, especially end-to-end deep stereo matching networks, have achieved remarkable performance in the fields of autonomous driving and depth sensing. However, state-of-the-art stereo algorithms, even with the deep neural ne...

A novel bone registration method using impression molding and structured-light 3D scanning technology.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Accurate bone registration is critical for computer navigation and robotic surgery. Existing registration systems are expensive, cumbersome, limited in accuracy and/or require intraoperative radiation. We recently reported a novel method of registrat...

Semantic segmentation of COVID-19 lesions with a multiscale dilated convolutional network.

Scientific reports
Automatic segmentation of infected lesions from computed tomography (CT) of COVID-19 patients is crucial for accurate diagnosis and follow-up assessment. The remaining challenges are the obvious scale difference between different types of COVID-19 le...

Classification of brain tumours in MR images using deep spatiospatial models.

Scientific reports
A brain tumour is a mass or cluster of abnormal cells in the brain, which has the possibility of becoming life-threatening because of its ability to invade neighbouring tissues and also form metastases. An accurate diagnosis is essential for successf...

Pulmonary nodules detection based on multi-scale attention networks.

Scientific reports
Pulmonary nodules are the main manifestation of early lung cancer. Therefore, accurate detection of nodules in CT images is vital for lung cancer diagnosis. A 3D automatic detection system of pulmonary nodules based on multi-scale attention networks ...

Artificial intelligence assistance improves the accuracy and efficiency of intracranial aneurysm detection with CT angiography.

European journal of radiology
PURPOSE: The aim of this study was to evaluate whether a novel head and neck artificial intelligence (AI)-assisted diagnostic system based on a three-dimensional convolutional neural network (3D-CNN) could improve the accuracy, efficiency and working...

Indocyanine Green Drives Computer Vision Based 3D Augmented Reality Robot Assisted Partial Nephrectomy: The Beginning of "Automatic" Overlapping Era.

Urology
Augmented reality robot-assisted partial nephrectomy (AR-RAPN) is limited by the need of a constant manual overlapping of the hyper-accuracy 3D (HA3D) virtual models to the real anatomy. To present our preliminary experience with automatic 3D virtual...