AIMC Topic: Optical Imaging

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Neural Network Kalman Filtering for 3-D Object Tracking From Linear Array Ultrasound Data.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Many interventional surgical procedures rely on medical imaging to visualize and track instruments. Such imaging methods not only need to be real time capable but also provide accurate and robust positional information. In ultrasound (US) application...

Single-molecule fluorescence imaging and deep learning reveal highly heterogeneous aggregation of amyloid-β 42.

Proceedings of the National Academy of Sciences of the United States of America
Polymorphism in the structure of amyloid fibrils suggests the existence of many different assembly pathways. Characterization of this heterogeneity is the key to understanding the aggregation mechanism and toxicity, but in practice it is extremely di...

High Resolution of Plasmonic Resonance Scattering Imaging with Deep Learning.

Analytical chemistry
The dark-field microscopy (DFM) imaging technology has the advantage of a high signal-to-noise ratio, and it is often used for real-time monitoring of plasmonic resonance scattering and biological imaging at the single-nanoparticle level. Due to the ...

Missing-Sheds Granularity Estimation of Glass Insulators Using Deep Neural Networks Based on Optical Imaging.

Sensors (Basel, Switzerland)
Insulator defect detection is an important task in inspecting overhead transmission lines. However, the surrounding environment is complex, and the detection accuracy of traditional image processing algorithms is low. Therefore, insulator defect dete...

Combining deep learning and fluorescence imaging to automatically identify fecal contamination on meat carcasses.

Scientific reports
Food safety and foodborne diseases are significant global public health concerns. Meat and poultry carcasses can be contaminated by pathogens like E. coli and salmonella, by contact with animal fecal matter and ingesta during slaughter and processing...

Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning.

Nature cell biology
Simultaneous imaging of various facets of intact biological systems across multiple spatiotemporal scales is a long-standing goal in biology and medicine, for which progress is hindered by limits of conventional imaging modalities. Here we propose us...

MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning.

Nature communications
Understanding the basis of brain function requires knowledge of cortical operations over wide spatial scales and the quantitative analysis of brain activity in well-defined brain regions. Matching an anatomical atlas to brain functional data requires...

Foundational Considerations for Artificial Intelligence Using Ophthalmic Images.

Ophthalmology
IMPORTANCE: The development of artificial intelligence (AI) and other machine diagnostic systems, also known as software as a medical device, and its recent introduction into clinical practice requires a deeply rooted foundation in bioethics for cons...

Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach.

Computational and mathematical methods in medicine
Alopecia areata is defined as an autoimmune disorder that results in hair loss. The latest worldwide statistics have exhibited that alopecia areata has a prevalence of 1 in 1000 and has an incidence of 2%. Machine learning techniques have demonstrate...

Optical tissue clearing and machine learning can precisely characterize extravasation and blood vessel architecture in brain tumors.

Communications biology
Precise methods for quantifying drug accumulation in brain tissue are currently very limited, challenging the development of new therapeutics for brain disorders. Transcardial perfusion is instrumental for removing the intravascular fraction of an in...