A Quick Guide on Radiology Image Pre-processing for Deep Learning Applications in Prostate Cancer Research Deep learning has achieved major breakthroughs during the past few years in almost every field. There are plenty of publicly available algorithms, each designed to address a different task of computer vision in general. However, most of these algorithms cannot be directly applied to images in medical domain. To employ such algorithms for clinical purposes, we must make a meaningful pixel/voxel representation from medical images which facilitates the learning process. Based on the ultimate goal expected from an algorithm (classification, detection, or segmentation), one may infer the required pre-processing steps that can ideally improve the performance of that algorithm. This article discusses the details of required pre-processing steps for CT and MR images of prostate cancer patients in correct order, supported by relevant experiments demonstrating the improved results . This material can be useful for educating those new to the field.
Required Libraries: SimpleITK, OpenCV, Pillow, and Scikit-image.