Application of deep learning for the quantitative assessment of bone marrow lesions (BMLs)

Abstract

Purpose: MRI can be used to assess several structural changes related to knee OA (KOA). Software image analysis methods provide objective, fully quantitative measurements of these changes. However, such methods generally require a human reader and can be time consuming if the number of images in a study is large. Deep leaning (DL), a form of statistical machine learning, offers the potential for increased or full automation, therefore reducing reader time substantially. The goal of our study was to validate a DL method for segmenting (outlining) bone marrow lesions (BMLs) on knee MRI scans. Methods: We used the baseline, 12 mo. and 24 mo. scans from the 600 subjects included in the FNIH Study, a nested case/control sub study of the OAI. All 600 subjects had both BL and 24 month scans, while 582 were imaged at the 12 month time point. Therefore, the dataset included 2 or 3 scans for each of the 600 subjects, totaling 1782 scans. Selecting for subjects with a BML in at least one time point gave 1358 scans from 544 different subjects for further processing. These 544 subjects were randomly split into training, validation and test sets of 50% (673 scans, 272 subjects) / 25% (352 scans, 186 subjects), 25% (333 scans, 186 subjects), respectively. BMLs segmented using a previously-validated semi-automated (SA) method were used to train the DL algorithm. A U-Net convolutional neural network was trained on BML patches and corresponding segmentation mask pairs determined from the SA method. The U-Net model is a fully-convolutional network (FCN) architecture specifically designed for image segmentation tasks. The DL algorithm was provided with the general location of each BML. We compared the BML volumes determined with the DL software to the SA measurements. The Dice similarity coefficients (DSC) and Pearson’s R2 are reported. In Figure 1 we show a representative example of BMLs segmented with SA and DL. Results: The average DSC was 0.70 and Figure 2 is a plot comparing the SA to the DL methods. The Pearson’s R2 was 0.94. Figure 3 depicts typical segmentation results over a range of DSC values. The DL method is not fully automated since a reader is required to indicate the location of a BML. However, this step requires on average 30 seconds per scan compared to 5 minutes for the full SA method. Conclusions: We have shown that the DL approach can produce an accurate method to assess BML volume, offering substantial time savings (90%) over the SA method. Future work will investigate a fully automated method, which does not require a reader to initially locate BMLs.View Large Image Figure ViewerDownload Hi-res image Download (PPT)View Large Image Figure ViewerDownload Hi-res image Download (PPT)