AIMC Topic: Imaging, Three-Dimensional

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Real-Time Standard View Classification in Transthoracic Echocardiography Using Convolutional Neural Networks.

Ultrasound in medicine & biology
Transthoracic echocardiography examinations are usually performed according to a protocol comprising different probe postures providing standard views of the heart. These are used as a basis when assessing cardiac function, and it is essential that t...

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning.

Journal of visualized experiments : JoVE
We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification of lymphocyte subtypes is important for the study of immunology as well as diagnosis and treatm...

Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative.

Medical image analysis
We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imagingĀ (MRI) that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs). The proposed approach incorporates 3D...

Deep Sequential Segmentation of Organs in Volumetric Medical Scans.

IEEE transactions on medical imaging
Segmentation in 3-D scans is playing an increasingly important role in current clinical practice supporting diagnosis, tissue quantification, or treatment planning. The current 3-D approaches based on convolutional neural networks usually suffer from...

Splenomegaly Segmentation on Multi-Modal MRI Using Deep Convolutional Networks.

IEEE transactions on medical imaging
The findings of splenomegaly, abnormal enlargement of the spleen, is a non-invasive clinical biomarker for liver and spleen diseases. Automated segmentation methods are essential to efficiently quantify splenomegaly from clinically acquired abdominal...

Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a robust multidimensional deep-learning based method to simultaneously generate accurate neurite orientation dispersion and density imaging (NODDI) and generalized fractional anisotropy (GFA) parameter maps from undersampled q-spa...

Development of 3D patient-based super-resolution digital breast phantoms using machine learning.

Physics in medicine and biology
Digital phantoms are important tools for optimization and evaluation of x-ray imaging systems, and should ideally reflect the 3D structure of human anatomy and its potential variability. In addition, they need to include a good level of detail at a h...

[Interest of robotic stereotactic radiosurgery in the management of brain metastases: Results of a retrospective, single center analysis].

Neuro-Chirurgie
PURPOSE: The management of malignant brain metastases becomes a main issue for the treatment of patients, because of the survival extension related to the improvement in systemic treatments. Robotic stereotactic radiosurgery (RSR) is a new approach i...

3-D Convolutional Neural Networks for Automatic Detection of Pulmonary Nodules in Chest CT.

IEEE journal of biomedical and health informatics
Deep two-dimensional (2-D) convolutional neural networks (CNNs) have been remarkably successful in producing record-breaking results in a variety of computer vision tasks. It is possible to extend CNNs to three dimensions using 3-D kernels to make th...

A Lightweight Multi-Section CNN for Lung Nodule Classification and Malignancy Estimation.

IEEE journal of biomedical and health informatics
The size and shape of a nodule are the essential indicators of malignancy in lung cancer diagnosis. However, effectively capturing the nodule's structural information from CT scans in a computer-aided system is a challenging task. Unlike previous mod...