Considerations on Image Preprocessing Techniques Required by Deep Learning Models. The Case of the Knee MRIs

This study aims to demonstrate the preprocessing steps for knee MRI images to detect meniscal lesions using deep learning models and highlight their practical implications in diagnosing knee conditions, especially meniscal injuries, often caused by degeneration or trauma. Magnetic resonance imaging (MRI) is key in this field, especially when combined with ligament evaluations, and our research underscores the relevance and applicability of these techniques in real-world scenarios. Importantly, our findings suggest a promising future for the diagnosis of knee conditions.

We initially worked with DICOM-format images, the standard for medical imaging, utilizing the Python packages PyDicom and SimpleITK for preprocessing. We also addressed the NIfTI format commonly used in research. Our preprocessing methods, designed with efficiency in mind, encompassed modality-specific adjustments, orientation, spatial resampling, intensity normalization, standardization and conversion to algorithm input format. These steps ensure efficient data handling, accelerate training speeds, and reassure the audience about the effectiveness of our research.

Our study processed PD-sagittal images from 188 patients to create a test set for training a deep learning segmentation model. We successfully completed all preprocessing steps, including accessing DICOM header information using hexadecimal encoded identifiers and utilizing SimpleITK for efficient handling of both 2D and 3D DICOM data. Resampling was performed for all 188 sets. Additionally, manual segmentation was conducted on 188 MRI scans, focusing on regions of interest (ROIs), such as normal tissue and meniscus tears in both the medial and lateral menisci. This involved contrast adjustment and precise hand-tracing of the structures within the ROIs, demonstrating the effectiveness and potential of our research in diagnosing knee conditions, and offering hope for the future of knee MRI diagnosis.

Our study introduces innovative preprocessing methods that have the potential to advance the field. By enhancing researchers' understanding of the importance of preprocessing steps, we anticipate that our techniques will streamline the preparation of standardized formats for deep learning model training and significantly benefit radiologists and orthopedic surgeons. These techniques could reduce time and effort in tasks like meniscal tear segmentation or localization, inspiring hope for more efficient and effective achievements in the field.