AIMC Topic:
Ultrasonography
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Developing an Intelligent Automatic Appendix Extraction Method from Ultrasonography Based on Fuzzy ART and Image Processing.
Computational and mathematical methods in medicine
May 18, 2015
Ultrasound examination (US) does a key role in the diagnosis and management of the patients with clinically suspected appendicitis which is the most common abdominal surgical emergency. Among the various sonographic findings of appendicitis, outer di...
Versatile robotic probe calibration for position tracking in ultrasound imaging.
Physics in medicine and biology
Apr 9, 2015
Within the field of ultrasound-guided procedures, there are a number of methods for ultrasound probe calibration. While these methods are usually developed for a specific probe, they are in principle easily adapted to other probes. In practice, howev...
Ultrasound texture-based CAD system for detecting neuromuscular diseases.
International journal of computer assisted radiology and surgery
Dec 2, 2014
PURPOSE: Diagnosis of neuromuscular diseases in ultrasonography is a challenging task since experts are often unable to discriminate between healthy and pathological cases. A computer-aided diagnosis (CAD) system for skeletal muscle ultrasonography w...
Can sonographic features be efficacy predictors of robotic massage treatment for masseter and temporal muscle in patients with temporomandibular disorder with myofascial pain?
Cranio : the journal of craniomandibular practice
Nov 17, 2014
OBJECTIVE: The aim of this study was to detect sonographic predictors for the efficacy of massage treatment of masseter and temporal muscle in temporomandibular disorders (TMDs) patients with myofascial pain.
Perioperative outcomes of robotic partial nephrectomy for intrarenal tumors.
Journal of endourology
Jul 31, 2014
INTRODUCTION: Intrarenal tumors pose a unique challenge to surgeons due to the lack of visual cues on the kidney surface. Intraoperative ultrasonography has facilitated the management of these tumors during minimally invasive partial nephrectomy. We ...
A Deep Reinforcement Learning Based Region-Specific Beamformer for Sparse Arrays 3-D Ultrasound Imaging.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Jun 1, 2025
Sparse arrays offer several advantages over other element reduction techniques for 3-D ultrasound imaging. However, the large interelement spacing in these arrays results in high sidelobe-related artifacts, which significantly degrade image quality a...
Deep learning based on ultrasound images predicting cervical lymph node metastasis in postoperative patients with differentiated thyroid carcinoma.
The British journal of radiology
Jun 1, 2025
OBJECTIVES: To develop a deep learning (DL) model based on ultrasound (US) images of lymph nodes for predicting cervical lymph node metastasis (CLNM) in postoperative patients with differentiated thyroid carcinoma (DTC).
Coarse for Fine: Bounding Box Supervised Thyroid Ultrasound Image Segmentation Using Spatial Arrangement and Hierarchical Prediction Consistency.
IEEE journal of biomedical and health informatics
Jun 1, 2025
Weakly-supervised learning methods have become increasingly attractive for medical image segmentation, but suffered from a high dependence on quantifying the pixel-wise affinities of low-level features, which are easily corrupted in thyroid ultrasoun...
Advancing Acoustic Droplet Vaporization for Tissue Characterization Using Quantitative Ultrasound and Transfer Learning.
IEEE transactions on bio-medical engineering
Jun 1, 2025
Acoustic droplet vaporization (ADV) is an emerging technique with expanding applications in biomedical ultrasound. ADV-generated bubbles can function as microscale probes that provide insights into the mechanical properties of their surrounding micro...
Ultrasound-based deep learning radiomics for enhanced axillary lymph node metastasis assessment: a multicenter study.
The oncologist
May 8, 2025
BACKGROUND: Accurate preoperative assessment of axillary lymph node metastasis (ALNM) in breast cancer is crucial for guiding treatment decisions. This study aimed to develop a deep-learning radiomics model for assessing ALNM and to evaluate its impa...
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