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Found 10 papers, 4 papers with code
RapidVol: Rapid Reconstruction of 3D Ultrasound Volumes from Sensorless 2D Scans
Two-dimensional (2D) freehand ultrasonography is one of the most commonly used medical imaging modalities, particularly in obstetrics and gynaecology.
Prototype Learning for Explainable Brain Age Prediction
Therefore, in this work, we present ExPeRT: an explainable prototype-based model specifically designed for regression tasks.
Adaptive 3D Localization of 2D Freehand Ultrasound Brain Images
Two-dimensional (2D) freehand ultrasound is the mainstay in prenatal care and fetal growth monitoring.
INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical Examples
Convolutional neural networks (CNNs) have shown exceptional performance for a range of medical imaging tasks.
ImplicitVol: Sensorless 3D Ultrasound Reconstruction with Deep Implicit Representation
The objective of this work is to achieve sensorless reconstruction of a 3D volume from a set of 2D freehand ultrasound images with deep implicit representation.
TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations
We tested our method on myocardium segmentation from an open-source 2D heart dataset.
Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-Supervised Learning
The objective of this work is to segment any arbitrary structures of interest (SOI) in 3D volumes by only annotating a single slice, (i. e. semi-automatic 3D segmentation).
Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis
To regularise the anatomical structures between US and MRI during synthesis, we further propose an adversarial structural constraint.
Automated fetal brain extraction from clinical Ultrasound volumes using 3D Convolutional Neural Networks
To improve the performance of most neuroimiage analysis pipelines, brain extraction is used as a fundamental first step in the image processing.
Anatomy-Aware Self-supervised Fetal MRI Synthesis from Unpaired Ultrasound Images
The feasibility of the approach to produce realistic looking MR images is demonstrated quantitatively and with a qualitative evaluation compared to real fetal MR images.
