This study was approved by the Medical Research Ethics Committee of Tokyo Medical and Dental University and written informed consent was obtained from all participants. The protocols were enrolled in a database of the National University Hospital Council of Japan (UMIN000031924, UMIN000032826) and disclosed.
Magnetic resonance imaging (MRI)
Images were collected with a 3.0-T MRI (Achieva 3.0T TX; Philips) using 16-channel flex coils. The sagittal plane of the knee joint was imaged using both a fat-suppressed spoiled gradient echo (SPGR) sequence to extract cartilage and a proton density weighted imaging (PDWI 3D FSE/TSE) sequence to extract meniscus and bone. For both sequences, sagittal imaging was performed at an in-plane resolution of 0.31×0.31 mm, a partition thickness of 0.36 mm (320 slices), and a field of view of 150×150 mm.
Automatic segmentation algorithm of 3D MRI
We used 3D convolutional neural network model based on U-Net for our software . The network used convolution with padding to improve the accuracy of edge extraction. As a loss function, we used Soft Dice loss  (Fig. 1).
For neural network construction, we randomly chose 10 healthy volunteers and 103 patients with knee pain who had visited our hospital between July 7, 2012, and July 24, 2018. These data were manually segmented by two authors (H.A. and A.H.) who had both trained as orthopedic surgeons for six years and had experience in the manual correction of over 200 knees. Segmentation data were converted by professional engineers (K.S. and J.M.) to train the neural network. To train the network to construct a region of interest (ROI) of the femoral subchondral bone and the medial/lateral tibial plateau, we manually segmented the ROI by using a reconstructed 3D knee model.
To validate our algorithm, 108 subjects among 113 were randomly selected for training. After training with 108 subjects, we computed the segmentation accuracy of another 5 subjects as one validation test. We performed the validation test three times, which selected another 108 subjects for training and 5 subjects for each test. After completing three validation tests, the software was trained by all data collected from 113 subjects and was then used for the cross-sectional research of this study.
Kanagawa Knee Study
We mainly recruited workers or retirees from the Kanagawa Prefectural Office and named this study “Kanagawa Knee Study.” We collected 561 datasets included more than 50 females and 50 males per age group (30s, 40s, 50s, 60s, and 70s). Those who had a history of lower limb surgery or who were diagnosed with rheumatoid arthritis or cancer were excluded. We announced recruitment of these subjects at the Kanagawa Prefectural Government between September 1, 2018 and August 30, 2019. Participants joined our study voluntarily. We collected their heights, weights, knee radiographs, and MRIs between November 3, 2018, and September 28, 2019, at the AIC Yaesu clinic of Tokyo.
Nomenclature and quantitative measurements for cartilage and the meniscus
To quantify cartilage, femoral (F) cartilage was radially projected and divided into three regions inside of the ROI of the femoral subchondral bone (blue outer line of Fig. 2A), femoral trochlea (TrF: anterior cartilage from proximal to inter condylar notch), medial femoral condyle (MF: medial cartilage from intercondylar notch to posterior), and lateral femoral condyle (LF: lateral cartilage from intercondylar notch to posterior) (Fig.2A). The tibial cartilage was vertically projected and divided into two areas inside the ROI of the medial/lateral tibial plateau (blue outer line of Fig. 2B), medial tibia (MT), and lateral tibia (LT) (Fig. 2B).
Each area was automatically divided into 3×3 subregions at equal intervals and named according to the previous report. From medial to lateral, the subregions in the TrF were named medial (m), central (c), and lateral (l). From proximal to distal, they were named proximal (p), intermediate (i), and distal (d) (Fig. 2A). From anterior to posterior, subregions in the other four parts (MF, LF, MT, and LT) were named anterior (a), medial (m), and posterior (p). From internal to external, they were named internal (i), central (c), and external (e) (Figs. 2A, 2B) .
Our software automatically computed cartilage average thickness (ThC), cartilage volume (VC), and projected cartilage area ratio (PCAR) in each region and subregion. Our software could also display the cartilage thickness mapping (Fig. 2C).
In this study, PCAR represented the ratio between the projected cartilage area and the total area of the ROI. PCAR = 1.0 meant that the entire ROI of the subchondral bone was covered by cartilage. PCAR = 0.0 meant that no cartilage covered the ROI of the subchondral bone. For example, a schematic diagram of the projected cartilage area (cartilage thickness ≥1.0 mm) is shown in Fig. 2D. As practical examples, the projected cartilage area (cartilage thickness ≥ 0.0 mm) is shown in Fig. 2E, and the projected cartilage area (cartilage thickness ≥ 1.0 mm) is shown in Fig. 2F. We evaluated PCAR for the threshold of cartilage thicknesses at ≥ 0.0 mm, ≥ 0.5 mm, ≥ 1.0 mm, and ≥ 1.5 mm. PCARs for the threshold of cartilage thicknesses at each of these measurements were named PCAR0.0, PCAR0.5, PCAR1.0, and PCAR1.5, respectively.
To evaluate the meniscus, medial and lateral meniscus volume, meniscus extrusion (ME) volume, ME area, and meniscus coverage ratio (MCR) were automatically computed. MCR was defined as the ratio between the overlapping area of the meniscus and the area of the ROI for the tibial plateau (Fig. 2G).
To evaluate the accuracy of automatic segmentation, we calculated the Dice similarity coefficient (DSC) between manual segmentation and automatic segmentation . In each validation test, DSC was computed for five test subjects at the femoral bone, tibial bone, femoral cartilage, tibial cartilage, medial/lateral meniscus, femoral subchondral bone ROI, and medial/lateral tibia plateau ROI. After the three validation tests, we calculated the mean DSC of each test.
To compare gender differences in the quantification of cartilage and the meniscus, we used the Mann–Whitney U test. To evaluate the correlation between each quantitative value and age or other quantitative value, we used Spearman’s rank correlation test. All statistical analyses were done using JMP® 14 (SAS Institute Inc., Cary, NC, USA). P values < 0.005 were considered statistically significant.