An Intensity-Based Cropping Approach for Fast, Interpretable, and Robust Localization of the Knee Joint in...

Area of Science:

• Medical Imaging
• Artificial Intelligence in Healthcare
• Computational Anatomy

Background:

• Effective image preprocessing is crucial for robust deep learning (DL) models, preventing shortcut learning on spurious features.
• Knee joint localization is essential for reliable pathology assessment, isolating clinically meaningful anatomy.
• Accurate region of interest selection enhances DL model training quality and diagnostic performance.

Purpose of the Study:

• Introduce a fully automated, intensity-based cropping algorithm for knee joint localization.
• Compare its performance against Support Vector Machine (SVM) and DL-based methods.
• Evaluate its clinical utility, computational efficiency, and distributional consistency.

Main Methods:

• Developed a deterministic, intensity-based algorithm using anatomical landmarks without annotations or training.
• Evaluated cropping performance on OAI and MRKR datasets using Intersection over Union (IoU), Dice, and mAP@0.5.
• Assessed computational efficiency and trained ConvNeXt models for osteoarthritis prediction using crops from each method.

Main Results:

• DL-based method achieved highest localization accuracy (IoU: 0.737), followed by intensity-based (IoU: 0.692).
• Intensity-based method was significantly faster (0.047 s/image) and processed 100% of images.
• Models trained on intensity-based crops showed stable performance and more homogeneous feature distributions.

Conclusions:

• The proposed intensity-based algorithm is a robust, annotation-free alternative to supervised methods.
• Its deterministic nature and minimal computational cost support large-scale research and clinical deployment.
• This method enhances diagnostic reliability by reducing artifacts and spurious features in medical image analysis.