61 research outputs found
Ultrasound segmentation using U-Net: learning from simulated data and testing on real data
Segmentation of ultrasound images is an essential task in both diagnosis and
image-guided interventions given the ease-of-use and low cost of this imaging
modality. As manual segmentation is tedious and time consuming, a growing body
of research has focused on the development of automatic segmentation
algorithms. Deep learning algorithms have shown remarkable achievements in this
regard; however, they need large training datasets. Unfortunately, preparing
large labeled datasets in ultrasound images is prohibitively difficult.
Therefore, in this study, we propose the use of simulated ultrasound (US)
images for training the U-Net deep learning segmentation architecture and test
on tissue-mimicking phantom data collected by an ultrasound machine. We
demonstrate that the trained architecture on the simulated data is
transferrable to real data, and therefore, simulated data can be considered as
an alternative training dataset when real datasets are not available. The
second contribution of this paper is that we train our U- Net network on
envelope and B-mode images of the simulated dataset, and test the trained
network on real envelope and B- mode images of phantom, respectively. We show
that test results are superior for the envelope data compared to B-mode image.Comment: Accepted in EMBC 201
Biomedical Images Generation for Data Augmentation Using Generative Adversarial Networks
Accurate biomedical images annotations are necessary for the development of medical applications. The generation of these images using neural networks can be used for data augmentation. The aim of the work is to create a method for biomedical images generation for data augmentation using GAN
The Potential Dangers of Artificial Intelligence for Radiology and Radiologists
With the advent of artificial intelligence (AI) across many fields and subspecialties, there are considerable expectations for transformative impact. However, there are also concerns regarding the potential abuse of AI. Many scientists have been worried about the dangers of AI leading to “biased” conclusions, in part because of the enthusiasm of the inventor or overenthusiasm among the general public. Here, though, we consider some scenarios in which people may intend to cause potential errors within data sets of analyzed information, resulting in incorrect conclusions and leading to potential problems with patient care and outcomes
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