197 research outputs found
Anatomy and Physiology of Artificial Intelligence in PET Imaging
The influence of artificial intelligence (AI) within the field of nuclear
medicine has been rapidly growing. Many researchers and clinicians are seeking
to apply AI within PET, and clinicians will soon find themselves engaging with
AI-based applications all along the chain of molecular imaging, from image
reconstruction to enhanced reporting. This expanding presence of AI in PET
imaging will result in greater demand for educational resources for those
unfamiliar with AI. The objective of this article to is provide an illustrated
guide to the core principles of modern AI, with specific focus on aspects that
are most likely to be encountered in PET imaging. We describe convolutional
neural networks, algorithm training, and explain the components of the commonly
used U-Net for segmentation and image synthesis
ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax
Radiology narrative reports often describe characteristics of a patient's
disease, including its location, size, and shape. Motivated by the recent
success of multimodal learning, we hypothesized that this descriptive text
could guide medical image analysis algorithms. We proposed a novel
vision-language model, ConTEXTual Net, for the task of pneumothorax
segmentation on chest radiographs. ConTEXTual Net utilizes language features
extracted from corresponding free-form radiology reports using a pre-trained
language model. Cross-attention modules are designed to combine the
intermediate output of each vision encoder layer and the text embeddings
generated by the language model. ConTEXTual Net was trained on the CANDID-PTX
dataset consisting of 3,196 positive cases of pneumothorax with segmentation
annotations from 6 different physicians as well as clinical radiology reports.
Using cross-validation, ConTEXTual Net achieved a Dice score of
0.7160.016, which was similar to the degree of inter-reader variability
(0.7120.044) computed on a subset of the data. It outperformed both
vision-only models (ResNet50 U-Net: 0.6770.015 and GLoRIA:
0.6860.014) and a competing vision-language model (LAVT: 0.7060.009).
Ablation studies confirmed that it was the text information that led to the
performance gains. Additionally, we show that certain augmentation methods
degraded ConTEXTual Net's segmentation performance by breaking the image-text
concordance. We also evaluated the effects of using different language models
and activation functions in the cross-attention module, highlighting the
efficacy of our chosen architectural design
Surface orientation, modulation frequency and the detection and perception of depth defined by binocular disparity and motion parallax
Binocular disparity and motion parallax provide information about the spatial structure and layout of the world. Descriptive similarities between the two cues have often been noted which have been taken as evidence of a close relationship between them. Here, we report two experiments which investigate the effect of surface orientation and modulation frequency on (i) a threshold detection task and (ii) a supra-threshold depth-matching task using sinusoidally corrugated surfaces defined by binocular disparity or motion parallax. For low frequency corrugations, an orientation anisotropy was observed in both domains, with sensitivity decreasing as surface orientation was varied from horizontal to vertical. In the depth-matching task, for surfaces defined by binocular disparity the greatest depth was seen for oblique orientations. For surfaces defined by motion parallax, perceived depth was found to increase as surface orientation was varied from horizontal to vertical. In neither case was perceived depth for supra-threshold surfaces related to threshold performance in any simple manner. These results reveal clear differences between the perception of depth from binocular disparity or motion parallax, and between perception at threshold and supra-threshold levels of performance. Β© 2006 Elsevier Ltd. All rights reserved
Identification of an elaborate complex mediating postsynaptic inhibition
Inhibitory synapses dampen neuronal activity through postsynaptic hyperpolarization. The composition of the inhibitory postsynapse and the mechanistic basis of its regulation, however, remains poorly understood. We used an in vivo chemico-genetic proximity-labeling approach to discover inhibitory postsynaptic proteins. Quantitative mass spectrometry not only recapitulated known inhibitory postsynaptic proteins, but also revealed a large network of new proteins, many of which are either implicated in neurodevelopmental disorders or are of unknown function. CRISPR-depletion of one of these previously uncharacterized proteins, InSyn1, led to decreased postsynaptic inhibitory sites, reduced frequency of miniature inhibitory currents, and increased excitability in the hippocampus. Our findings uncover a rich and functionally diverse assemblage of previously unknown proteins that regulate postsynaptic inhibition and might contribute to developmental brain disorders
Issues and Challenges in Applications of Artificial Intelligence to Nuclear Medicine -- The Bethesda Report (AI Summit 2022)
The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI
AI Task Force, took place in Bethesda, MD on March 21-22, 2022. It brought
together various community members and stakeholders from academia, healthcare,
industry, patient representatives, and government (NIH, FDA), and considered
various key themes to envision and facilitate a bright future for routine,
trustworthy use of AI in nuclear medicine. In what follows, essential issues,
challenges, controversies and findings emphasized in the meeting are
summarized
Soil methane sink capacity response to a long-term wildfire chronosequence in Northern Sweden
Boreal forests occupy nearly one fifth of the terrestrial land surface and are recognised as globally important regulators of carbon (C) cycling and greenhouse gas emissions. Carbon sequestration processes in these forests include assimilation of CO2 into biomass and subsequently into soil organic matter, and soil microbial oxidation of methane (CH4). In this study we explored how ecosystem retrogression, which drives vegetation change, regulates the important process of soil CH4 oxidation in boreal forests. We measured soil CH4 oxidation processes on a group of 30 forested islands in northern Sweden differing greatly in fire history, and collectively representing a retrogressive chronosequence, spanning 5000 years. Across these islands the build-up of soil organic matter was observed to increase with time since fire disturbance, with a significant correlation between greater humus depth and increased net soil CH4 oxidation rates. We suggest that this increase in net CH4 oxidation rates, in the absence of disturbance, results as deeper humus stores accumulate and provide niches for methanotrophs to thrive. By using this gradient we have discovered important regulatory controls on the stability of soil CH4 oxidation processes that could not have not been explored through shorter-term experiments. Our findings indicate that in the absence of human interventions such as fire suppression, and with increased wildfire frequency, the globally important boreal CH4 sink could be diminished
Spatial Stereoresolution for Depth Corrugations May Be Set in Primary Visual Cortex
Stereo β3Dβ depth perception requires the visual system to extract binocular disparities between the two eyes' images. Several current models of this process, based on the known physiology of primary visual cortex (V1), do this by computing a piecewise-frontoparallel local cross-correlation between the left and right eye's images. The size of the βwindowβ within which detectors examine the local cross-correlation corresponds to the receptive field size of V1 neurons. This basic model has successfully captured many aspects of human depth perception. In particular, it accounts for the low human stereoresolution for sinusoidal depth corrugations, suggesting that the limit on stereoresolution may be set in primary visual cortex. An important feature of the model, reflecting a key property of V1 neurons, is that the initial disparity encoding is performed by detectors tuned to locally uniform patches of disparity. Such detectors respond better to square-wave depth corrugations, since these are locally flat, than to sinusoidal corrugations which are slanted almost everywhere. Consequently, for any given window size, current models predict better performance for square-wave disparity corrugations than for sine-wave corrugations at high amplitudes. We have recently shown that this prediction is not borne out: humans perform no better with square-wave than with sine-wave corrugations, even at high amplitudes. The failure of this prediction raised the question of whether stereoresolution may actually be set at later stages of cortical processing, perhaps involving neurons tuned to disparity slant or curvature. Here we extend the local cross-correlation model to include existing physiological and psychophysical evidence indicating that larger disparities are detected by neurons with larger receptive fields (a size/disparity correlation). We show that this simple modification succeeds in reconciling the model with human results, confirming that stereoresolution for disparity gratings may indeed be limited by the size of receptive fields in primary visual cortex
Magnitude, precision, and realism of depth perception in stereoscopic vision
Our perception of depth is substantially enhanced by the fact that we have binocular vision. This provides us with more precise and accurate estimates of depth and an improved qualitative appreciation of the three-dimensional (3D) shapes and positions of objects. We assessed the link between these quantitative and qualitative aspects of 3D vision. Specifically, we wished to determine whether the realism of apparent depth from binocular cues is associated with the magnitude or precision of perceived depth and the degree of binocular fusion. We presented participants with stereograms containing randomly positioned circles and measured how the magnitude, realism, and precision of depth perception varied with the size of the disparities presented. We found that as the size of the disparity increased, the magnitude of perceived depth increased, while the precision with which observers could make depth discrimination judgments decreased. Beyond an initial increase, depth realism decreased with increasing disparity magnitude. This decrease occurred well below the disparity limit required to ensure comfortable viewing
On the Inverse Problem of Binocular 3D Motion Perception
It is shown that existing processing schemes of 3D motion perception such as interocular velocity difference, changing disparity over time, as well as joint encoding of motion and disparity, do not offer a general solution to the inverse optics problem of local binocular 3D motion. Instead we suggest that local velocity constraints in combination with binocular disparity and other depth cues provide a more flexible framework for the solution of the inverse problem. In the context of the aperture problem we derive predictions from two plausible default strategies: (1) the vector normal prefers slow motion in 3D whereas (2) the cyclopean average is based on slow motion in 2D. Predicting perceived motion directions for ambiguous line motion provides an opportunity to distinguish between these strategies of 3D motion processing. Our theoretical results suggest that velocity constraints and disparity from feature tracking are needed to solve the inverse problem of 3D motion perception. It seems plausible that motion and disparity input is processed in parallel and integrated late in the visual processing hierarchy
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