Scenario: You want to programmatically define where your figures are in your latex document without going through and manually editing all your paths. You have a folder called "figs" that contains all your figures, but this folder might move.

Here’s how to programmatically change the path of where your images are located

Or more specifically programmatically change where the "figs" folder is located

Here’s how to calculate the Jaccard similarity coefficient and Jaccard distance between two or more images.

But first, some quick definitions…

The Jaccard index is the same thing as the Jaccard similarity coefficient. We call it a similarity coefficient since we want to measure how similar two things are.

The Jaccard distance is a measure of how dis-similar two things are. We can calculate the Jaccard distance as 1 – the Jaccard index.

For this to make sense, let’s first set up our scenario.

Often I find myself running jobs on the cluster and I can never remember the basic commands. So here’s some useful commands that you can use to run jobs on the cluster.

*** note that I’m running jobs on Simon Fraser University’s (SFU) cluster and I have no idea if these will work on your specific configuration ***

A few months ago, I romanticized about developing video games and spent an evening trying out Unity3D.

I have to say I’m pretty impressed. With zero prior experience using it and barely going through any sort of tutorials, in a couple of hours I managed to put together a little world to run around in!

I think the title really summarizes the work pretty nicely so I’ll break down each part of the title to give you a brief overview of the paper.

“Spinal Cord Segmentation” = this is the goal of the work. Given a 3D MRI, we want a method that can mark those voxels that belong to the spinal cord. Segmentation means to label the voxels as spinal cord or background.

“High Dimensions” = we represented the segmented spinal cord by a list of 6 numbers. I’ll explain this a bit more since this a bit tricky to understand. We used principle component analysis (PCA) to represent the “shape” of a 2D slice of the spinal cord. PCA allows us to represent each shape by its center point (x,y,z) and three principle components (giving us a total of 6 numbers per 2D slice). We can represent our spinal cord as a stack of these 2D shapes, or equivalently, a list of 6 numbers.

Now the question is, how to find this list of 6 numbers? Well we can search for them. This is similar to finding the shortest path in between 2 points, but instead of searching over 2 or 3 (spatial) dimensions, we search over the 6 dimensions (3 spatial and 3 shape).

“Minimal Path” = a minimal path finds the shortest path in between two points. In our methods, a user enters the start and end point of the spinal cord, and we find the minimal path in 6D between these two points. We use a slightly modified version of the A* search to find this minimal path. A path is defined to be “short” if the shapes “fits” well (e.g. the borders match) with what is in the MRI.