Automatic Segmentation of Meniscus in Multispectral MRI Using Regions with Convolutional Neural Network (R-CNN)

Area of Science:

• Medical Imaging
• Computer Vision
• Anatomy

Background:

• The meniscus plays a crucial role in knee joint function.
• Magnetic Resonance Imaging (MRI) is vital for meniscus assessment.
• Segmenting menisci in MRI data is challenging due to data variability and complex features.

Purpose of the Study:

• To develop an automated method for meniscus detection and segmentation in MRI sequences.
• To leverage deep learning, specifically R-CNN, for improved meniscus region identification.
• To enhance the efficiency and accuracy of meniscus segmentation using advanced image processing techniques.

Main Methods:

• Designed and trained a Regions with Convolutional Neural Network (R-CNN) model for meniscus detection.
• Utilized transfer learning to train the R-CNN with a limited dataset of meniscus MRI scans.
• Applied morphological image analysis for meniscus segmentation post-detection, incorporating data from two MRI sequences.

Main Results:

• The R-CNN successfully detected the meniscus region within MRI data sequences.
• Automatic detection facilitated a more straightforward meniscus segmentation process.
• Employing two MRI sequences with distinct contrast features aided in differentiating the meniscus from surrounding tissues.

Conclusions:

• R-CNN provides an effective solution for the automated detection of meniscus in MRI.
• The proposed method simplifies and potentially improves the accuracy of meniscus segmentation.
• Combining deep learning detection with multi-sequence morphological analysis offers a robust approach for meniscus imaging analysis.