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Found 6 papers, 2 papers with code
Deep Spatiotemporal Clutter Filtering of Transthoracic Echocardiographic Images Using a 3D Convolutional Auto-Encoder
To train the deep network, a diverse set of artifact patterns was simulated and the simulated patterns were superimposed onto artifact-free ultra-realistic synthetic TTE sequences of six ultrasound vendors to generate input of the filtering network.
DEEPBEAS3D: Deep Learning and B-Spline Explicit Active Surfaces
Experimental results show that: 1) the proposed framework gives the user explicit control of the surface contour; 2) the perceived workload calculated via the NASA-TLX index was reduced by 30% compared to VOCAL; and 3) it required 7 0% (170 seconds) less user time than VOCAL (p< 0. 00001)
Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentation
In this work, we propose an adaptive multi-scale online likelihood network (MONet) that adaptively learns in a data-efficient online setting from both an initial automatic segmentation and user interactions providing corrections.
Interactive Segmentation via Deep Learning and B-Spline Explicit Active Surfaces
The interactive element of the framework allows the user to precisely edit the contour in real-time, and by utilising BEAS it ensures the final contour is smooth and anatomically plausible.
Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography
Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis.
Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network
Results show a median Dice similarity coefficient of 0. 90 with an interquartile range of 0. 08, with equivalent performance to the three operators (with a Williams' index of 1. 03), and outperforming a U-Net architecture without the need for batch normalisation.
