Quick guide on radiology image pre-processing for deep learning applications in prostate cancer research.

Area of Science:

• Medical Imaging
• Deep Learning
• Computer Vision

Background:

• Deep learning models have shown significant advancements across various fields.
• Publicly available deep learning algorithms often require adaptation for medical image analysis.
• Medical images necessitate specific preprocessing for effective use with deep neural networks.

Purpose of the Study:

• To identify and detail essential preprocessing steps for medical images before applying deep neural networks.
• To demonstrate how tailored preprocessing can optimize deep learning model performance in clinical settings.
• To provide a guide for preprocessing computed tomography (CT) and magnetic resonance (MR) images.

Main Methods:

• Investigated preprocessing techniques applicable to medical imaging datasets.
• Detailed the sequence of preprocessing steps for CT and MR images.
• Conducted experiments to validate the impact of preprocessing on deep learning tasks.
• Focused on preprocessing for classification, detection, and segmentation tasks.

Main Results:

• Appropriate image preprocessing significantly improves deep learning model performance.
• The order of preprocessing steps is critical for enhancing classification and segmentation accuracy.
• Experiments confirmed the efficacy of the proposed preprocessing strategies.

Conclusions:

• This study outlines optimal preprocessing steps for CT and MR images, specifically for prostate cancer patient data.
• The findings offer valuable insights for researchers and practitioners new to deep learning in medical imaging.
• A repository of preprocessing code is available for educational purposes.