Improved-Mask R-CNN: Towards an Accurate Generic MSK MRI instance segmentation platform (Data from the Osteoarthritis Initiative)

TL;DR

This study enhances Mask R-CNN for more accurate instance segmentation of knee MRI tissues related to osteoarthritis, improving upon previous methods with specific architectural modifications and validated on large datasets.

Contribution

The paper introduces iMaskRCNN, an improved version of Mask R-CNN with architectural modifications tailored for OA tissue segmentation in MRI scans, achieving higher accuracy.

Findings

iMaskRCNN achieved higher dice scores for bone and cartilage segmentation.

The method demonstrated high agreement with human readers in effusion detection.

Architectural modifications improved edge segmentation performance.

Abstract

Objective assessment of Magnetic Resonance Imaging (MRI) scans of osteoarthritis (OA) can address the limitation of the current OA assessment. Segmentation of bone, cartilage, and joint fluid is necessary for the OA objective assessment. Most of the proposed segmentation methods are not performing instance segmentation and suffer from class imbalance problems. This study deployed Mask R-CNN instance segmentation and improved it (improved-Mask R-CNN (iMaskRCNN)) to obtain a more accurate generalized segmentation for OA-associated tissues. Training and validation of the method were performed using 500 MRI knees from the Osteoarthritis Initiative (OAI) dataset and 97 MRI scans of patients with symptomatic hip OA. Three modifications to Mask R-CNN yielded the iMaskRCNN: adding a 2nd ROIAligned block, adding an extra decoder layer to the mask-header, and connecting them by a skip connection. The results were assessed using Hausdorff distance, dice score, and coefficients of variation (CoV). The iMaskRCNN led to improved bone and cartilage segmentation compared to Mask RCNN as indicated with the increase in dice score from 95% to 98% for the femur, 95% to 97% for tibia, 71% to 80% for femoral cartilage, and 81% to 82% for tibial cartilage. For the effusion detection, dice improved with iMaskRCNN 72% versus MaskRCNN 71%. The CoV values for effusion detection between Reader1 and Mask R-CNN (0.33), Reader1 and iMaskRCNN (0.34), Reader2 and Mask R-CNN (0.22), Reader2 and iMaskRCNN (0.29) are close to CoV between two readers (0.21), indicating a high agreement between the human readers and both Mask R-CNN and iMaskRCNN. Mask R-CNN and iMaskRCNN can reliably and simultaneously extract different scale articular tissues involved in OA, forming the foundation for automated assessment of OA. The iMaskRCNN results show that the modification improved the network performance around the edges.