Researchers have created a method of processing 3D images for computer simulations that could have beneficial implications for several industries including healthcare, manufacturing, and electric vehicles.
The Efficient Quantification of Uncertainty in Image-based Physics Simulation (EQUIPS) can use machine learning to quantify the uncertainty in how an image is drawn for 3D computer simulations. By giving a range of uncertainty, the workflow allows decision-makers to consider best- and worst-case outcomes.
A doctor, for example, when examining a CT scan to create a cancer treatment plan, can render the scan into a 3D image, which can then be used in a computer simulation to create a radiation dose that will efficiently treat a tumor without unnecessarily damaging surrounding tissue. Normally, the simulation would produce one result because the 3D image was rendered once. But drawing object boundaries in a scan can be difficult and there is more than one sensible way to do so. Humans and machines will draw different but reasonable interpretations of the tumor's size and shape from those blurry images.
Using the EQUIPS workflow, which can use machine learning to automate the drawing process, the 3D image is rendered into many viable variations showing size and location of a potential tumor. Those different renderings will produce a range of different simulation outcomes. Instead of one answer, the doctor will have a range of prognoses to consider that can affect risk assessments and treatment decisions.
The first step of image-based simulation is the image segmentation — deciding which pixel (voxel in a 3D image) to assign to each object and therefore drawing the boundary between two objects. From there, scientists can begin to build models for computational simulation. But pixels and voxels will blend with gradual gradient changes, so it is not always clear where to draw the boundary line — the gray areas in a black and white CT scan or X-ray.
EQUIPS can employ two types of machine learning techniques — Monte Carlo Dropout Networks and Bayesian Convolutional Neural Networks — to perform image segmentation, with both approaches creating a set of image segmentation samples. These samples are combined to map the probability that a certain pixel or voxel is in the segmented material. To explore the impact of segmentation uncertainty, EQUIPS creates a probability map to obtain segmentations, which are then used to perform multiple simulations and calculate uncertainty distributions.