Botnari A, Kadar M, Patrascu J M
"Victor Babes" University of Medicine and Pharmacy, Timisoara, Romania.
"1 Decembrie 1918" University of Alba Iulia, Alba Iulia, Romania.
Maedica (Bucur). 2024 Sep;19(3):526-535. doi: 10.26574/maedica.2024.19.3.526.
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.
本研究旨在展示膝关节磁共振成像（MRI）图像的预处理步骤，以便使用深度学习模型检测半月板损伤，并突出其在诊断膝关节疾病（尤其是通常由退变或创伤引起的半月板损伤）中的实际意义。磁共振成像（MRI）在该领域至关重要，特别是与韧带评估相结合时，我们的研究强调了这些技术在实际场景中的相关性和适用性。重要的是，我们的研究结果为膝关节疾病的诊断预示了一个充满希望的未来。
我们最初处理的是医学成像标准的DICOM格式图像，利用Python包PyDicom和SimpleITK进行预处理。我们还处理了研究中常用的NIfTI格式。我们设计的预处理方法考虑到了效率，包括特定模态调整、方向调整、空间重采样、强度归一化、标准化以及转换为算法输入格式。这些步骤确保了高效的数据处理，加快了训练速度，并让读者对我们研究的有效性放心。
我们的研究处理了188名患者的质子密度矢状位图像，以创建一个用于训练深度学习分割模型的测试集。我们成功完成了所有预处理步骤，包括使用十六进制编码标识符访问DICOM头信息，并利用SimpleITK高效处理二维和三维DICOM数据。对所有188组数据进行了重采样。此外，对188次MRI扫描进行了手动分割，重点关注感兴趣区域（ROI），如正常组织以及内侧和外侧半月板的半月板撕裂。这涉及对比度调整和对ROI内结构的精确手动追踪，证明了我们研究在诊断膝关节疾病方面的有效性和潜力，并为膝关节MRI诊断的未来带来了希望。
我们的研究引入了具有推动该领域发展潜力的创新预处理方法。通过增强研究人员对预处理步骤重要性的理解，我们预计我们的技术将简化深度学习模型训练的标准化格式准备工作，并显著造福放射科医生和骨科医生。这些技术可以减少半月板撕裂分割或定位等任务中的时间和精力，为该领域取得更高效和有效的成果带来希望。
