14,841 research outputs found
Reconstructing 3d lung shape from a single 2d image during the deaeration deformation process using model-based data augmentation
Three-dimensional (3D) shape reconstruction is particularly important for computer assisted medical systems, especially in the case of lung surgeries, where large deaeration deformation occurs. Recently, 3D reconstruction methods based on machine learning techniques have achieved considerable success in computer vision. However, it is difficult to apply these approaches to the medical field, because the collection of a massive amount of clinic data for training is impractical. To solve this problem, this paper proposes a novel 3D shape reconstruction method that adopts both data augmentation techniques and convolutional neural networks. In the proposed method, a deformable statistical model of the 3D lungs is designed to augment various training data. As the experimental results demonstrate, even with a small database, the proposed method can realize 3D shape reconstruction for lungs during a deaeration deformation process from only one captured 2D image. Moreover, the proposed data augmentation technique can also be used in other fields where the training data are insufficient
Non-invasive aerosol delivery and transport of gold nanoparticles to the brain
Targeted delivery of nanoscale carriers containing packaged payloads to the central nervous system has potential use in many diagnostic and therapeutic applications. Moreover, understanding of the bio-interactions of the engineered nanoparticles used for tissue-specific delivery by non-invasive delivery approaches are also of paramount interest. Here, we have examined this issue systematically in a relatively simple invertebrate model using insects. We synthesized 5 nm, positively charged gold nanoparticles (AuNPs) and targeted their delivery using the electrospray aerosol generator. Our results revealed that after the exposure of synthesized aerosol to the insect antenna, AuNPs reached the brain within an hour. Nanoparticle accumulation in the brain increased linearly with the exposure time. Notably, electrophysiological recordings from neurons in the insect brain several hours after exposure did not show any significant alterations in their spontaneous and odor-evoked spiking properties. Taken together, our findings reveal that aerosolized delivery of nanoparticles can be an effective non-invasive approach for delivering nanoparticles to the brain, and also presents an approach to monitor the short-term nano-biointeractions
Automated liver tissues delineation based on machine learning techniques: A survey, current trends and future orientations
There is no denying how machine learning and computer vision have grown in
the recent years. Their highest advantages lie within their automation,
suitability, and ability to generate astounding results in a matter of seconds
in a reproducible manner. This is aided by the ubiquitous advancements reached
in the computing capabilities of current graphical processing units and the
highly efficient implementation of such techniques. Hence, in this paper, we
survey the key studies that are published between 2014 and 2020, showcasing the
different machine learning algorithms researchers have used to segment the
liver, hepatic-tumors, and hepatic-vasculature structures. We divide the
surveyed studies based on the tissue of interest (hepatic-parenchyma,
hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more
than one task simultaneously. Additionally, the machine learning algorithms are
classified as either supervised or unsupervised, and further partitioned if the
amount of works that fall under a certain scheme is significant. Moreover,
different datasets and challenges found in literature and websites, containing
masks of the aforementioned tissues, are thoroughly discussed, highlighting the
organizers original contributions, and those of other researchers. Also, the
metrics that are used excessively in literature are mentioned in our review
stressing their relevancy to the task at hand. Finally, critical challenges and
future directions are emphasized for innovative researchers to tackle, exposing
gaps that need addressing such as the scarcity of many studies on the vessels
segmentation challenge, and why their absence needs to be dealt with in an
accelerated manner.Comment: 41 pages, 4 figures, 13 equations, 1 table. A review paper on liver
tissues segmentation based on automated ML-based technique
Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs
We propose a method for synthesizing cardiac magnetic resonance (MR) images
with plausible heart pathologies and realistic appearances for the purpose of
generating labeled data for the application of supervised deep-learning (DL)
training. The image synthesis consists of label deformation and label-to-image
translation tasks. The former is achieved via latent space interpolation in a
VAE model, while the latter is accomplished via a label-conditional GAN model.
We devise three approaches for label manipulation in the latent space of the
trained VAE model; i) \textbf{intra-subject synthesis} aiming to interpolate
the intermediate slices of a subject to increase the through-plane resolution,
ii) \textbf{inter-subject synthesis} aiming to interpolate the geometry and
appearance of intermediate images between two dissimilar subjects acquired with
different scanner vendors, and iii) \textbf{pathology synthesis} aiming to
synthesize a series of pseudo-pathological synthetic subjects with
characteristics of a desired heart disease. Furthermore, we propose to model
the relationship between 2D slices in the latent space of the VAE prior to
reconstruction for generating 3D-consistent subjects from stacking up 2D
slice-by-slice generations. We demonstrate that such an approach could provide
a solution to diversify and enrich an available database of cardiac MR images
and to pave the way for the development of generalizable DL-based image
analysis algorithms. We quantitatively evaluate the quality of the synthesized
data in an augmentation scenario to achieve generalization and robustness to
multi-vendor and multi-disease data for image segmentation. Our code is
available at https://github.com/sinaamirrajab/CardiacPathologySynthesis.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) https://www.melba-journal.org/2023:01
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