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Marvel Comics: Cyclops Powers Explained (Comics Explained)

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Marvel Comics: Cyclops Powers Explained (Comics Explained)

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Marvel Comics: Cyclops Powers Explained (Comics Explained)

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X-Ray Vision (also referred to as penetration vision and transparent vision) is a common superpower among superheroes. It is the ability to see through opaque matter as if it were transparent.

It should be noted that the power is not always literally x-ray vision, but a shorthand for the power to see through solid matter. In many cases, this is because x-rays (or x-radiation) can be harmful to humans if one is over-exposed. Any superhero with actual x-ray eyes might be seen as less heroic if he were casually exposing people to x-rays and risking their health. In most cases, the power of what can be seen through is selective, making it seem to the user that some matter has become transparent and the images the user sees have a level of clarity and detail that would be unavailable to actual x-rays, including color and space.

Though Superman is the most iconic superhero with this power, the first major comic hero with x-ray vision was Olga Mesmer in the pulp magazine comic strip Spicy Mysteries. Olga, a half alien living on Earth, uses her x-ray eyes to fight crime. Superman used his x-ray powers for the first time in 1939 in Action Comics #11. Later, this power would become the reason that Superman did not join World War II, as Superman tried to enlist in the military as Clark Kent, only to accidentally use his x-ray vision during the eye test and read the eye chart in the room over.

Later, in the film Superman, the title character reveals that his power allowed him to know the color of her underwear.

Sometimes, there are objects that cannot be seen through. In the case of Superman, his x-ray vision cannot see through lead. However, this can also be used to an advantage; in one instance, the Joker plotted to use coffins lined with lead to hide bombs from Superman, but Superman was able to identify which coffins contained bombs by checking which he couldn't see though.

In some cases. the x-ray vision is quite a literal name, meaning that in using it there is a risk of spreading an unhealthy amount of radiation.

Superman - Superman is the most famous superhero to possess x-ray vision.

Olga Mesmer - Olga Mesmer is the first superhero to have this power.

Ultra Boy - Ultra Boy was a superhero who had powers similar to Superboy, including a version of x-ray vision called Penetra-Vision.

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X-Ray Vision (also referred to as penetration vision and transparent vision) is a common superpower among superheroes. It is the ability to see through opaque matter as if it were transparent. It should be noted that the power is not always literally x-ray vision, but a shorthand for the power to see through solid matter.

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Superman isn’t the only one with X-Ray vision: Deep Learning for CT Scans

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Superman isn’t the only one with X-Ray vision: Deep Learning for CT Scans

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A computerized tomography (CT) scan consists of a series of X-ray images taken from different angles and combines them to create cross-sectional images, or slices, of the bones, blood vessels and soft tissues inside your body. These are much more detailed than single plain X-rays.

This article will talk about details about what CT scans are used for, how to use CT data for machine learning and other research purposes, the current work done in deep learning with CT scans, the challenges faced by researchers, and other avenues for exploration.

A CT scan has many uses, but is particularly well-suited to quickly examine people who may have internal injuries from car accidents or other types of trauma. A CT scan can be used to visualize nearly all parts of the body and is used to diagnose disease or injury as well as to plan medical, surgical or radiation treatment.

A series of CT scans converted into an animated image using Photoshop [Source]

Since CT scans consist of slices which amalgamate a 3D view, the advent of this technology has revolutionized the healthcare industry by now allowing radiologists to analyze the body in high-resolution 3-dimensional representations. These provide an immense amount of information which is used to diagnose a variety of diseases in different parts of the body. This extends from detecting tumor growth and metastases to internal injuries, bleeding, clots, malformations, and fractures - and providing far better diagnoses than was earlier possible with individual X-rays.

Hounsfield Units

CT scans are generally available in DICOM files, which each contain 2D arrays with pixel intensities. These however aren't in the standard 0-255 range, but are instead in Hounsfield Units or CT Numbers.

A DCM file is an image file saved in the Digital Imaging and Communications in Medicine (DICOM) image format. It stores a medical image, such as a CT scan or ultrasound, and may also contain information about the patient.

Hounsfield Units (HU) measure the radio-intensity of particular mediums based on their attenuation coefficients.

Attenuation coefficients give a measure of how easily X-rays can pass through a particular medium. It quantifies how much the beam is weakened as it passes through the given material.

They range from -1000 HU for air, to 0 HU for distilled water, and >10,000 HU for metals. In CT scans, we usually care about about the -700 to +100 range where

Lungs occur from -700 to -600

Muscles are between +35 and +55

Blood is +13 to +50, while clotted blood is +50 to +75

Other organs and matter are around +10 to +100

Anything above +500 are foreign bodies like metal, glass, or rocks.

Once we clamp our input to a particular intensity window range as a preprocessing step, we can normalize the resulting data to 0-255 and produce an image which can be used to train machine learning models.

Data from a single trial

A trial contains all the data collected from a single visit by a patient for a procedure. In case of a CT scan, this would contain multiple DCM files, each containing an image of a single slice.

Each CT scan acquires images along the axial plane - these are typically also used to produce visualizations along the coronal and sagittal orientations.

This data can be used in multiple ways:

Process one image at a time: This loses the 3D detail that CT scans provide, however, this has shown to have good performance for analyzing nodules in the lungs for detecting lung cancer.

Processing images in a series: Multiple images of a series along any particular orientation can be used together. This is used by all the current state of the art models.

Processing multiple series: This is more computationally expensive and usually only provides marginal improvement over other methods. It involves processing all the images in a series, and using multiple series with an ensemble of models to process each one of them.

Depending on how we process the data, we can have different models:

Single image processing: This is done using simple 2D convolutional networks like DenseNet and ResNet. These can either be used in an end-to-end pipeline to directly produce the output, or it can be used to process and extract features which are then passed into another model.
With enough data, the end-to-end models have been shown to be much better at these tasks than having separate models.

Series / Multi-image processing: These images can either be stacked together to create a '3D image (or 3D point cloud)'. This can then be processed by 3D convolutional networks.
Another way to process this data is to look at it sequentially: by passing these through a recursive neural network like an RNN or LSTM with hidden layers from pre-trained models like DenseNet.

Current Work

2D Convolutional Neural Networks were used as a standard in Lung Cancer Detection from individual CT scans. Some examples of this can be seen in this Github Repo.

Historically, CT scans have been used in conjunction with PET scans to detect lesions for lung cancer. Krzysztof Pawełczyk et al., 2017, show that they can detect and segment lesions based on only CT scan data using 3D Convolutional Networks.
The work also details a dynamic way of managing class imbalance in the dataset, which is very common for medical datasets. After every few epochs, accuracy is calculated on the validation set for all classes. Then a new training subset is created where classes with lower accuracy are present in higher proportions.

In the recent Lung Nodule Detection Challenge (LUNA16), all of the top submissions use CNNs to achieve good performance. One crucial factor in their success was the availability of expert knowledge. Understanding details about localization, processes used by radiologists, and other relevant medical expertise allows for model design choices which greatly improve performance on the task. As seen in a lot of competitions, most of the teams use similar models and techniques, namely pre-trained CNN architectures. However, it is the preprocessing and data augmentation with expert knowledge that gives teams the edge. Sample code to work with the LUNA16 dataset: Github Repo

A recent work from Monika Grewal et al. from Parallel Dots, published on Jan 2018, talked about the model which they called RADNet (Recurrent Attention DenseNet). It uses a DenseNet architecture to extract features, in addition to passing sequential data through a bi-directional LSTM layer. It uses the context around each image in the series to make better predictions.

As discussed in A Survey on Deep Learning in Medical Image Analysis, models for processing medical images have greatly benefitted from pre-training on natural image datasets like ImageNet. They have also been shown to perform better if pre-trained on other medical imaging data which adapts them to better leverage the intrinsic structure of medical imaging. It is a non-trivial task to figure out how to best use expert knowledge as a prior for the model, and if its effect is going to be positive or negative on the model.

The data from CT scans is very high dimensional. Generally each series contains around 70-300 high-resolution images, which leads to a really large input dimensionality. However, the information which is useful for discriminating abnormal from normal trials can be minuscule in comparison and sometimes is present only in a small subset of the data. One way to tackle this is by using Gaussian Mixture Models to pinpoint viable regions, however this is still an open and very important problem.

Segmentation remains one of the major tasks in CT scans. Patrick Christ et al. [11] [12] have shown that they can get much better performance on liver and tumor segmentation using Conditional Random Fields (CRFs) and Cascaded Fully Convolutional Networks (CFCNs). CRFs have been one of the most popular algorithms used for segmentation in medical imaging for the past decade, and continue to be used along with its 3D variations. Other architectures like U-Net [13](for 2D image slices) and V-Net [14](for 3D image volumes) have shown remarkable performance in image segmentation by using context from multiple resolutions to provide a final segmentation map.

VoxNet [5]: This work goes in depth on using 3D convolutional networks for deep representations of 3D volumetric point cloud input data for object recognition and classification tasks. This provides a good baseline for working on 3D data

OctNet [6]: It hierarchically and dynamically partitions the input into sections of different sizes, based on the amount of detail that they contain. This can potentially pay more attention to intricate sections containing more details, which could be helpful in some tasks like detecting nodules and lesions.

Challenges
I am extremely grateful to Matthew Lungren MD MPH, Assistant Professor of Radiology and Bhavik Patel, MD, MBA, Assistant Professor of Radiology at the Stanford University Medical Center at the Stanford University Medical Center for their guidance and valuable feedback. I would also like to thank Pranav Rajpurkar, Jeremy Irvin, Shubhang Desai, and Nicholas Bien of the Stanford Machine Learning Group for their comments.
In the last few years, novel architectures like U-Net, V-Net, Cascaded fully convolutional networks etc have shown to perform much better on segmentation and classification tasks when working with CT scan images. There is immense opportunity for new models to be developed to tackle some of the challenges of training with CT data. In addition, various techniques that have been used in other fields to process similar formats of data can be used to build models which perform better on medical imaging datasets:

Since 2016, a lot of papers have come up using publicly available datasets consisting of CT scans of the abdomen, chest, and brain. However, a majority of them have been working with CT scans using individual image slices with 2D CNNs. Working with the entire 3D series is an area which is being intensely explored right now. There is potential for applying various other model architectures to tackle these datasets in their full form, and also using machine learning for other diagnostic and detection tasks. As we develop better models, get access to more data, and increase our computational capacities, it will be exciting to see how these tools can aid radiologists and medical practitioners in providing better diagnoses.

[1] Towards Detecting High-Uptake Lesions from Lung CT Scans Using Deep Learning. Krzysztof Pawełczyk et al. 13 Oct 2017

[2] RADNET: Radiologist Level Accuracy using Deep Learning for Hemorrhage Detection in CT Scans. Monika Grewal et al. 3 Jan 2018

[3] A Survey on Deep Learning in Medical Image Analysis. Geert Litjens, Thijs Kooi et al. 4 Jun 2017

[4] LUNA16 Grand Challenge (Lung Nodule Analysis): Results from 2016

[5] VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition. Daniel Maturana and Sebastian Scherer. IROS, 2015

[6] OctNet: Learning Deep 3D Representations at High Resolutions. Gernot Riegler. 10 April 2017

[7] Image Data (Pixel/Voxel Data) by Johannes Gutenberg, Institute of Computer Science.

[8] Pixel-Based Machine Learning in Medical Imaging. Kenji Suzuki. 17 October 2011

[9] Automatic segmentation of liver tumor in CT images with deep convolutional neural networks. Li et al. October 2015

[10] Automatic liver tumor segmentation in followup ct studies using convolutional neural networks. Vivanti R., et al. 7 September 2015.

[11] Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields. Christ et al. 7 October 2016.

[12] Automatic Liver and Tumor Segmentation of CT and MRI Volumes Using Cascaded Fully Convolutional Neural Networks. Christ et al. 23 February 2017.

[13] U-Net: Convolutional Networks for Biomedical Image Segmentation. Ronneberger et al. 18 May 2015

[14] V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Fausto Milletari et al. 15 June 2016.

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Superman isn't the only one with X-Ray vision: Deep Learning for CT Scans

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If you were going to eat an apple and were doubtful if it is rotten from the inside or not, you could cut it up and see if there's anything wrong on the inside. However, if you wanted to inspect a...

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https://medium.com/stanford-ai-for-healthcare/superman-isnt-the-only-one-with-x-ray-vision-deep-learning-for-ct-scans-290aaa7ba5c1

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https://scifi.stackexchange.com/questions/156059/can-superman-see-through-his-eyelids

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https://scifi.stackexchange.com/questions/156059/can-superman-see-through-his-eyelids

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Science of vision: How do our eyes enable us to see? 

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Science of vision: How do our eyes enable us to see? 

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The structure of the human eye is so complex that it's hard to believe that it's not the product of intelligent design, but by looking at the eyes of other animals, scientists have shown that it evolved very gradually from a simple light-dark sensor over the course of around 100 million years. It functions in a very similar way to a camera, with an opening through which the light enters, a lens for focusing and a light-sensitive membrane at the back.

The amount of light that enters the eye is controlled by the circular and radial muscles in the iris, which contract and relax to alter the size of the pupil. The light first passes through a tough protective sheet called the cornea, and then moves into the lens. This adjustable structure bends the light, focusing it down to a point on the retina, at the back of the eye.

The retina is covered in millions of light-sensitive receptors known as rods and cones. Each receptor contains pigment molecules, which change shape when they are hit by light, triggering an electrical message that travels to the brain via the optic nerve.

How we see

The back of the eye is covered in a layer of light-sensitive cells measuring just fractions of a millimetre in thickness. When photons of light hit the pigments inside the cells, it triggers a cascade of signals, which pass through a series of different connections before they are transmitted to the brain.

First, they move through interneurones and then to neurones known as ganglion cells. These cells are cross-linked, able to compare adjacent signals, filtering out some of the information before passing it on to the brain. This helps to improve contrast and definition. The neurones travel across the back surface toward the optic nerve, which relays the information into the brain.

As the two optic nerves enter the brain, they cross over, coming together at a point known as the optic chiasm. Here, signals from the left side of both eyes are diverted to the left side of the brain, and vice versa, allowing the images from both eyes to be combined and compared.

The signals enter the brain via the thalamus, which separates the incoming information into two parts, one containing colour and detail, and the other movement and contrast. The messages then move to the back of the brain, and into the visual cortex. The cortex is laid out so that it mirrors the back of the retina, allowing a detailed image to be reconstructed.

Colour vision

Open your eyes, and you are met with an array of different colours, but amazingly you can only detect three different wavelengths of light, corresponding to green, blue, and red. Combining these three signals in the brain creates millions of different shades.

Each eye has between 6 and 7 million cone cells, containing one of three colour-sensitive proteins known as opsins. When photons of light hit the opsins, they change shape, triggering a cascade that produces electrical signals, which in turn transmit the messages to the brain. Well over half of our cone cells respond to red light, around a third to green light, and just two per cent to blue light, giving us vision focused around the yellow-green region of the spectrum.

The vast majority of the cone cells in the human eye are located in the centre of the retina, on a spot known as the fovea, measuring just fractions of a millimetre across. Light is focused on this point, providing a crisp, full-colour image at the centre of our vision. The remainder of the retina is dominated by 120 million rod cells, which detect light, but not colour.

We are so used to seeing the world in red, green and blue that it might seem strange to think that most other animals cannot, but three-coloured vision like our own is relatively unusual. Some species of fish, reptiles and birds have four-colour vision, able to see red, green, blue and ultraviolet or infrared light, but during mammalian evolution, two of the four cone types were lost, leaving most modern mammals with dichromatic vision - seeing in shades of just yellow and blue.

This was not a problem for many early mammals, because they were largely nocturnal, and lived underground, where there was little need for good colour vision. However, when primates started moving into the trees, a gene duplication gave some species the ability to see red, providing a significant evolutionary advantage in picking out ripe red fruit against the green leaves.

Even today, not all primates can see in three colours; some have dichromatic vision, and many nocturnal monkeys only see in black and white. It is all down to environment; if you don't need to see all of the colours in order to survive, then why waste energy making the pigments?

Seeing in three dimensions

Our eyes are only able to produce two-dimensional images, but with some clever processing, the brain is able to build these flat pictures into a three-dimensional view. Our eyes are positioned about five centimetres (two inches) apart, so each sees the world from a slightly different angle. The brain compares the two pictures, using the differences to create the illusion of depth.

Discover more amazing science in the latest issue of How It Works magazine. It's available from all good retailers, or you can order it online from the ImagineShop. If you have a tablet or smartphone, you can also download the digital version onto your iOS or Android device. To make sure you never miss an issue of How It Works magazine, make sure you subscribe today!

Plus take a look at:
Top 5 facts about vision What is the maximum distance the human eye can see if unobstructed? How do sunglasses protect your eyes?

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Science of vision: How do our eyes enable us to see? | How It Works Magazine

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The structure of the human eye is so complex that it's hard to believe that it's not the product of intelligent design, but by looking at the eyes of other animals, scientists have shown that it evolved very gradually from a simple light-dark sensor over the course of around 100 million years.

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How Does Superman's Heat Vision Work? (Because Science w/ Kyle Hill)

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How Does Superman's Heat Vision Work? (Because Science w/ Kyle Hill)

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How Does Superman's Heat Vision Work? (Because Science w/ Kyle Hill)

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Interpreting a chest X-ray is a very valuable skill given how commonly it is ordered. The following guide helps walk you through some of the basic elements of X-ray imaging that are important to understand in order to interpret these studies effectively.

This imaging modality works by literally penetrating the human body with X-rays (and measuring X-rays that can not penetrate the medium in question). If the X-rays penetrate a structure, they will appear black on the film. If they can't penetrate the structure (i.e. are reflected) they appear white on the film.

There are 4 major radiological/physical mediums that appear different visually given their relative densities. They are (listed from least to most dense):

Air: this will appear black because the X-rays can easily penetrate air

Fat: fat will not be completely penetrated and will appear faintly on film

Water (soft tissues): this medium will appear more white on X-rays (compared to fat) given its increased density.

Bone (calcium): bony/calcified structures show up the most clearly on X-rays (bright white!) because the X-rays have difficulty penetrating these structures.

When looking at an X-ray image, arguably the first detail you should notice is if the image was taken with a portable or non-portable device. Portable devices are advantageous because they can taken to a bed ridden patient, however non-portable X-rays are preferred (when a patient is able to ambulate) because of their superior image quality. It is important to be wary of comparing portable to non-portable X-rays simply because there are differences between the two (a patient's pneumonia may look worse on a portable X-ray compared to a baseline non-portable image). We will discuss quality measures for X-rays more below.
***Keeping this type of X-ray machine in mind will contextualize your analysis of the image further on.***
PENETRATION OF THE FILM

The penetration of the film refers to how well the X-rays have penetrated the body of the patient. The more penetration the more X-rays will interact with the film placed on the other side of the patient. We must appreciate that their is a "sweet spot" with regards to how well penetrated the film is (to maximize the clinical information we can gain from the study). Below are some examples from a chest X-ray study to help demonstrate this concept.

Is the film under-penetrated? If film is under penetrated then there will be an excess of white present.

Is the film over-penetrated? Alternatively, if the X-rays over penetrate the patient, there will be an excess of black present on the film.
***PROPER PENETRATION IS CRITICAL TO INTERPRETING AN X-RAY!***
Magnification: How far away is the structure from the film when the image was taken? This can be a difficult concept to retain, however the further away a structure is from the X-ray film, the more it will MAGNIFY as the X-ray film is developed. It is for this reason that a standard PA (with the heart closer to the film) shows a more accurate size of the heart while an AP film (which increases the distance between the film and the heart) will show enlarged cardiac structures.

Page Updated: 08.24.2016

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Fundamentals Of X-Ray Studies - Stepwards

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Page Contents1 OVERVIEW2 HOW DOES IT WORK?3 TYPE OF X-RAY MACHINE4 PENETRATION OF THE FILM5 OVERVIEW Interpreting a chest X-ray is a very valuable skill given how commonly it is ordered. The following guide helps walk you through some of the basic elements of X-ray imaging that are important to understand in order to interpret these ...

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What do penicillin , Super Glue and X-rays have in common? Their inventors all discovered them by accident!

In 1895, German physicist Wilhelm Roentgen was experimenting with electricity in a special tube. He wanted to find out how electricity would act in a . For that reason, Roentgen removed as much air from the tube as possible. That allowed to move at a very fast pace through the tube.

As the moved, they bumped into each other. They also ran into the glass of the tube and two special parts of the tube called the cathode and anode. What Roentgen didn't know was that, when these fast-paced bumped into the tube's anode, they would send off a type of light that hadn't yet been discovered. This type of light is called an x-ray.

X-rays are a type of light ray, much like the visible light we see every day. The difference between visible light and x-rays is the wavelength of the rays. Human eyes cannot see light with longer wavelengths, such as radio waves, or light with shorter wavelengths, such as x-rays.

Soon, Roentgen noticed that a screen in his lab started glowing while he conducted his experiment. While he knew material normally glows when exposed to electromagnetic , he was still surprised because heavy cardboard, which he thought would have blocked the , surrounded the tube.

He began to experiment by placing different objects between the tube and the screen. No matter what he put between the two, the screen still glowed.

At one point, Roentgen placed his hand in front of the tube. When he did this, he saw a silhouette of his bones projected onto the screen.

Not only had Roentgen discovered x-rays, he saw firsthand ( intended!) how they could become extremely helpful to medicine.

There's no doubt the x-ray machine is very useful to doctors. X-rays can pass through nonmetallic objects, including human tissues and organs. An x-ray machine is like a giant camera that allows doctors to see what is going on inside a patient without having to do surgery.

It took a long time for scientists to make the x-ray safe for medical use. Today's x-ray machines produce a stream of electromagnetic that interacts with an anode in an x-ray tube. The x-rays made by this interaction are then directed toward the part of the body being examined. To reduce exposure, x-ray machines aim the x-rays at only the focus area.

When x-rays come into contact with our body tissues, they produce an image on a metal film. Soft tissue, such as skin and organs, cannot absorb the high-energy rays, and the beam passes through them. Dense materials inside our bodies, like bones, absorb the .

Much like camera film, the X-ray film develops depending on which areas were exposed to the X-rays. Black areas on an x-ray represent areas where the x-rays have passed through soft tissues. White areas show where denser tissues, such as bones, have absorbed the x-rays.

Have you ever had an x-ray? They're commonly used to see broken bones. Doctors might also order an x-ray when trying to figure out why you don't feel well. But rest assured! X-rays are nothing to be nervous about. They don't hurt at all. Instead, they help doctors find ways to make you feel better.

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What do penicillin , Super Glue and X-rays have in common? Their inventors all discovered them by accident! In 1895, German physicist Wilhelm Roentgen was experimenting with electricity in a special tube. He wanted to find out how electricity would act in a .

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Superman's ability to shoot laser beams from his eyes has come a step closer to reality, thanks to scientists who have developed an ultra-thin membrane laser using organic semiconductors.

These ocular lasers could be harnessed for new applications in security, biophotonics and photomedicine, according to researchers from the University of St Andrews in the UK.

Published in the journal Nature Communications, the research explains that the threshold of the membrane lasers is compatible with the requirements for safe operation in the human eye.

"Our work represents a new milestone in laser development and, in particular, points the way to how lasers can be used in inherently soft and ductile environments, be it in wearable sensors or as an authentication feature on bank notes," said Malte Gather, a professor at the University of St Andrews.

By floating a thin plastic film off a substrate we have made some of the world's smallest and lightest lasers and put them on contact lenses and bank notes, said Ifor Samuel from University of St Andrews.

"By varying the materials and adjusting the grating structures of the laser, the emission can be designed to show a specific series of sharp lines on a flat background - the ones and zeros of a digital barcode," said Markus Karl, who worked on the new lasers as part of his PhD.PTI

Our work represents a new milestone in laser development and, in particular, points the way to how lasers can be used in inherently soft and ductile environments, be it in wearable sensors or as an authentication feature on bank notes

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Superman's ability to shoot laser beams from his eyes has come a step closer to reality, thanks to scientists who have developed an ultra-thin membrane laser using organic semiconductors. These ocular lasers could be harnessed for new applications in security, biophotonics and photomedicine, according to researchers from the University of St Andrews in the UK.

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How Does Cyclops Actually See? (Because Science w/ Kyle Hill)

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How Does Cyclops Actually See? (Because Science w/ Kyle Hill)

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How Does Cyclops Actually See? (Because Science w/ Kyle Hill)