Automatic segmentation of high- and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative

Abstract

Abstract. Clinical studies including thousands of magnetic resonance imaging (MRI) scans offer potential for pathogenesis research in osteoarthritis. However, comprehensive quantification of all bone, cartilage, and meniscus compartments is challenging. We propose a segmentation framework for fully automatic segmentation of knee MRI. The framework combines multiatlas rigid registration with voxel classification and was trained on manual segmentations with varying configurations of bones, cartilages, and menisci. The validation included high-and low-field knee MRI cohorts from the Center for Clinical and Basic Research, the osteoarthritis initiative (QAI), and the segmentation of knee images10 (SKI10) challenge. In total, 1907 knee MRIs were segmented during the evaluation. No segmentations were excluded. Our resulting OAI cartilage volume scores are available upon request. The precision and accuracy performances matched manual reader re-segmentation well. The cartilage volume scan-rescan precision was 4.9% (RMS CV). The Dice volume overlaps in the medial/lateral tibial/femoral cartilage compartments were 0.80 to 0.87. The correlations with volumes from independent methods were between 0.90 and 0.96 on the OAI scans. Thus, the framework demonstrated precision and accuracy comparable to manual segmentations. Finally, our method placed second for cartilage segmentation in the SKI10 challenge. The comprehensive validation suggested that automatic segmentation is appropriate for cohorts with thousands of scans.

Fully automated patellofemoral MRI segmentation using holistically nested networks: Implications for evaluating patellofemoral osteoarthritis, pain, injury, pathology, and adolescent development

“…The current study did not have such limitations. Furthermore, previous studies evaluating the semiautomatic or automatic segmentation of PF cartilage reported accuracies (DSCs ranged from 63% to 84%) that were inferior to the current results for PF bone segmentation. The stability of the HNN architecture, its ability to capture texture variation across the full image context, and the large training set likely promoted the superior performance of the HNN model.…”

“…To the best of our knowledge, only 2 studies presented accuracies for segmenting the full patellar bone, given that the focus has been primarily on segmenting the tibiofemoral bone and cartilage surfaces . One study on patellar bone segmentation reported more‐accurate segmentation (Table ) than the current study.…”

Use of 2D U-Net Convolutional Neural Networks for Automated Cartilage and Meniscus Segmentation of Knee MR Imaging Data to Determine Relaxometry and Morphometry

“…A popular state-of-the-art atlas-based approach is proposed by Dam et al (29), which uses a multiatlas registration accompanied by k-nearest neighbors (17,30). This approach requires multiple time-consuming steps to achieve the final segmentation, such as multiatlas registration and feature computation and selection.…”

Abstract: Automated Segmentation of Bones for the Age Assessment in 3D MR Images using Convolutional Neural Networks

“…The proposed architecture for the segmentation task resembles the encoder-decoder model type used for the U-Net. The trained network achieves a DSC score of 98% compared to the manual segmentations and an IoU of 96%, outperforming the results by Dam et al [1]. The precision and recall of the model are balanced and the error has a small value of 1.2%.…”