The Sunnybrook dataset consists of training images and corresponding ground truth contours used to evaluate methods for automatically segmenting LV contours from a series of MRI SAX scans in DICOM format. If you are able to successfully import all the packages, congratulations! I hope the setup is not too painful.
In order to view the Sunnybrook and DSB images stored in the DICOM standard format, we obtain the pydicom package using the pip install client: pip install pydicomįinally, we try to import the following packages neccessary for the tutorial to see if we have correctly set up our environment. Execute the following commands in the terminal to clone the repository and checkout the future branch: git clone I have compiled a list of pertinent resources in the References section below. Also, it is a good idea to explore the documentation to fully understand the newest features. Since this is the bleeding edge version, so cutting edge that it bleeds, there is no guarantee that it will behave well under your development environment. We use the "bleeding edge" branch of Caffe found at (commit hash: commit/0e5cd0c0214e5020308f7b3f4eec8fa79aa8103a). When compiling Caffe, remember to enable the following flag in nfig to fully harness the Python API within Caffe: # Uncomment to support layers written in Python (will link against Python libs) People have also successfully compiled Caffe on Windows, and a quick Google search should direct you to the right places.
I refer the reader to the Caffe documentation for the list of dependencies and step-by-step instructions on how to install it on Unix-based platforms. Anaconda also conveniently satisfies the majority of Python library dependencies required for compiling Caffe. I highly recommend the Anaconda Python distribution that comes with 200+ packages useful for many tasks in scientific computing and data science. This tutorial requires Python and Caffe, and is tested on both Mac OS X 10.10.5 and Ubuntu 14.04.3 LTS.
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