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Found 10 papers, 1 papers with code
Motion Informed Needle Segmentation in Ultrasound Images
Third, to our knowledge we are the first to implement a learnable filter to incorporate non-linear needle motion for improving needle segmentation.
Unsupervised Deformable Ultrasound Image Registration and Its Application for Vessel Segmentation
We also show how our approach, together with a robot performing force-controlled scans, can be used to generate synthetic deformed images to significantly expand the size of a femoral vessel segmentation training dataset without the need for additional manual labeling.
Unsupervised Deformable Image Registration for Respiratory Motion Compensation in Ultrasound Images
In this paper, we present a novel deep-learning model for deformable registration of ultrasound images and an unsupervised approach to training this model.
Good and Bad Boundaries in Ultrasound Compounding: Preserving Anatomic Boundaries While Suppressing Artifacts
Ultrasound images depend on probe direction and the path sound waves pass through, so when multiple intersecting B-scans of the same spot from different perspectives yield different pixel values, there is not a single, ideal representation for compounding (i. e. combining) the overlapping pixel values.
Weakly- and Semi-Supervised Probabilistic Segmentation and Quantification of Ultrasound Needle-Reverberation Artifacts to Allow Better AI Understanding of Tissue Beneath Needles
Our method models the intensity decay of artifact intensities and is designed to minimize the human labeling error.
Ultrasound Confidence Maps of Intensity and Structure Based on Directed Acyclic Graphs and Artifact Models
Ultrasound imaging has been improving, but continues to suffer from inherent artifacts that are challenging to model, such as attenuation, shadowing, diffraction, speckle, etc.
A Study of Domain Generalization on Ultrasound-based Multi-Class Segmentation of Arteries, Veins, Ligaments, and Nerves Using Transfer Learning
Identifying landmarks in the femoral area is crucial for ultrasound (US) -based robot-guided catheter insertion, and their presentation varies when imaged with different scanners.
Accurate Tissue Interface Segmentation via Adversarial Pre-Segmentation of Anterior Segment OCT Images
To the best of our knowledge, this is the first approach to remove severe specular artifacts and speckle noise patterns (prior to the shallowest interface) that affects the interpretation of anterior segment OCT datasets, thereby resulting in the accurate segmentation of the shallowest tissue interface.
Learning to Segment Corneal Tissue Interfaces in OCT Images
To the best of our knowledge, this is the first deep learning based approach to segment both anterior and posterior corneal tissue interfaces.
Fast Vessel Segmentation and Tracking in Ultra High-Frequency Ultrasound Images
Ultra High Frequency Ultrasound (UHFUS) enables the visualization of highly deformable small and medium vessels in the hand.
