AIMC Topic: Ultrasonography

Clear Filters Showing 751 to 760 of 1407 articles

Multi-class deep learning segmentation and automated measurements in periodontal sonograms of a porcine model.

Dento maxillo facial radiology
OBJECTIVES: Ultrasound emerges as a complement to cone-beam computed tomography in dentistry, but struggles with artifacts like reverberation and shadowing. This study seeks to help novice users recognize soft tissue, bone, and crown of a dental sono...

An Upgraded Siamese Neural Network for Motion Tracking in Ultrasound Image Sequences.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Deep learning is heavily being borrowed to solve problems in medical imaging applications, and Siamese neural networks are the front runners of motion tracking. In this article, we propose to upgrade one such Siamese architecture-based neural network...

Deep Learning for Ultrasound Image Formation: CUBDL Evaluation Framework and Open Datasets.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Deep learning for ultrasound image formation is rapidly garnering research support and attention, quickly rising as the latest frontier in ultrasound image formation, with much promise to balance both image quality and display speed. Despite this pro...

A novel complementation method of an acoustic shadow region utilizing a convolutional neural network for ultrasound-guided therapy.

International journal of computer assisted radiology and surgery
PURPOSE: Noise-free ultrasound images are essential for organ monitoring during regional ultrasound-guided therapy. When the affected area is located under the ribs, however, acoustic shadow is caused by the reflection of sound from hard tissues such...

Random Fourier Features-Based Deep Learning Improvement with Class Activation Interpretability for Nerve Structure Segmentation.

Sensors (Basel, Switzerland)
Peripheral nerve blocking (PNB) is a standard procedure to support regional anesthesia. Still, correct localization of the nerve's structure is needed to avoid adverse effects; thereby, ultrasound images are used as an aid approach. In addition, imag...

Robust Single-Probe Quantitative Ultrasonic Imaging System With a Target-Aware Deep Neural Network.

IEEE transactions on bio-medical engineering
OBJECTIVE: The speed of sound (SoS) has great potential as a quantitative imaging biomarker since it is sensitive to pathological changes in tissues. In this paper, a target-aware deep neural (TAD) network reconstructing an SoS image quantitatively f...

Enabling quantitative robot-assisted compressional elastography via the extended Kalman filter.

Physics in medicine and biology
Compressional or quasi-static elastography has demonstrated the capability to detect occult cancers in a variety of tissue types, however it has a serious limitation in that the resulting elastograms are generally qualitative whereas other forms of e...

A deep learning-based method for detecting and classifying the ultrasound images of suspicious thyroid nodules.

Medical physics
PURPOSE: The incidence of thyroid cancer has significantly increased in the last few decades. However, diagnosis of the thyroid nodules is labor and time intensive for radiologists and strongly depends on the personal experience of the radiologists. ...

Deep learning-based motion tracking using ultrasound images.

Medical physics
PURPOSE: Ultrasound (US) imaging is an established imaging modality capable of offering video-rate volumetric images without ionizing radiation. It has the potential for intra-fraction motion tracking in radiation therapy. In this study, a deep learn...