AIMC Topic: Ultrasonography

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Shadow-Consistent Semi-Supervised Learning for Prostate Ultrasound Segmentation.

IEEE transactions on medical imaging
Prostate segmentation in transrectal ultrasound (TRUS) image is an essential prerequisite for many prostate-related clinical procedures, which, however, is also a long-standing problem due to the challenges caused by the low image quality and shadow ...

Voice-Assisted Image Labeling for Endoscopic Ultrasound Classification Using Neural Networks.

IEEE transactions on medical imaging
Ultrasound imaging is a commonly used technology for visualising patient anatomy in real-time during diagnostic and therapeutic procedures. High operator dependency and low reproducibility make ultrasound imaging and interpretation challenging with a...

Collaborative Robotic Wire + Arc Additive Manufacture and Sensor-Enabled In-Process Ultrasonic Non-Destructive Evaluation.

Sensors (Basel, Switzerland)
The demand for cost-efficient manufacturing of complex metal components has driven research for metal Additive Manufacturing (AM) such as Wire + Arc Additive Manufacturing (WAAM). WAAM enables automated, time- and material-efficient manufacturing of ...

Deep learning for dose assessment in radiotherapy by the super-localization of vaporized nanodroplets in high frame rate ultrasound imaging.

Physics in medicine and biology
External beam radiotherapy is aimed to precisely deliver a high radiation dose to malignancies, while optimally sparing surrounding healthy tissues. With the advent of increasingly complex treatment plans, the delivery should preferably be verified b...

Dual Mode HRI-HRI Control System with a Hybrid Admittance-Force Controller for Ultrasound Imaging.

Sensors (Basel, Switzerland)
The COVID-19 pandemic has brought unprecedented extreme pressure on the medical system due to the physical distance policy, especially for procedures such as ultrasound (US) imaging, which are usually carried out in person. Tele-operation systems are...

Deep-learning segmentation of ultrasound images for automated calculation of the hydronephrosis area to renal parenchyma ratio.

Investigative and clinical urology
PURPOSE: We investigated the feasibility of measuring the hydronephrosis area to renal parenchyma (HARP) ratio from ultrasound images using a deep-learning network.

Automatic vein measurement by ultrasonography to prevent peripheral intravenous catheter failure for clinical practice using artificial intelligence: development and evaluation study of an automatic detection method based on deep learning.

BMJ open
OBJECTIVES: Complications due to peripheral intravenous catheters (PIVC) can be assessed using ultrasound imaging; however, it is not routinely conducted due to the need for training in image reading techniques. This study aimed to develop and valida...

Classification of Thyroid Nodules by Using Deep Learning Radiomics Based on Ultrasound Dynamic Video.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: We aimed to design a radiomics model for differential diagnosis of thyroid carcinoma based on dynamic ultrasound video, and compare its diagnostic performance with that of radiomics model based on static ultrasound images.

AI-based optimization for US-guided radiation therapy of the prostate.

International journal of computer assisted radiology and surgery
OBJECTIVES: Fast volumetric ultrasound presents an interesting modality for continuous and real-time intra-fractional target tracking in radiation therapy of lesions in the abdomen. However, the placement of the ultrasound probe close to the target s...

Machine Learning-Based Ultrasound Radiomics for Evaluating the Function of Transplanted Kidneys.

Ultrasound in medicine & biology
The aim of the study described here was to investigate the value of different machine learning models based on the clinical and radiomic features of 2-D ultrasound images to evaluate post-transplant renal function (pTRF). We included 233 patients who...