Background We developed a fully automatic three-dimensional knee MRI analysis software that can quantify meniscus extrusion and cartilage measurements, including the projected cartilage area ratio (PCAR), which represents the ratio of the subject’s actual cartilage area to their ideal cartilage area. We also collected 3D MRI knee data from 561 volunteers (aged 30–79 years) from the “Kanagawa Knee Study.” Our purposes were to verify the accuracy of the software for automatic cartilage and meniscus segmentation using knee MRI and to examine the relationship between medial meniscus extrusion measurements and cartilage measurements from Kanagawa Knee Study data. Methods We constructed a neural network for the software by randomly choosing 10 healthy volunteers and 103 patients with knee pain. We validated the algorithm by randomly selecting 108 of these 113 subjects for training, and determined Dice similarity coefficients from five other subjects. We constructed a neural network using all data (113 subjects) for training. Cartilage thickness, cartilage volume, and PCAR in the medial femoral, lateral femoral, medial tibial, and lateral tibial regions were quantified by using the trained software on Kanagawa Knee Study data and their relationship with subject height was investigated. We also quantified the medial meniscus coverage ratio (MMCR), defined as the ratio of the overlapping area between the medial meniscus area and the medial tibial cartilage area to the medial tibial cartilage area. Finally, we examined the relationship between MMCR and PCAR at middle central medial tibial (mcMT) subregion located in the center of nine subregions in the medial tibial cartilage. Results Dice similarity coefficients for cartilage and meniscus were both approximately 0.9. The femoral and tibial cartilage thickness and volume at each region correlated with height, but PCAR did not correlate with height in most settings. PCAR at the mcMT was significantly correlated with MMCR. Conclusions Our software showed high segmentation accuracy for the knee cartilage and meniscus. PCAR was more useful than cartilage thickness or volume since it was less affected by height. Relations ips were observed between the medial tibial cartilage measurements and the medial meniscus extrusion measurements in our cross-sectional study. Trial registration UMIN, UMIN000032826; 1 September 2018,

Background: We have developed 3-dimensional (3D) magnetic resonance imaging (MRI) analysis software that allows measurement of the projected cartilage area ratio with a particular thickness intended to allow quantitation of the cartilage in the knee. Our aims in this study were to validate the projected cartilage area ratio in both pig and human knees and to examine the ratio in patients reporting knee pain. Methods: After 3D MRI reconstruction, the femoral cartilage was projected onto a flat surface. The projected cartilage area was determined in pig knees using our 3D MRI analysis software, and was compared with the area obtained with other software. The projected cartilage area ratio (for cartilage thickness ≥1.5 mm) at 4 segments was also validated in human knees. Finally, changes in the projected cartilage area ratio were examined in 8 patients with knee pain who had undergone 2 MR images at 3 to 21-month intervals. Results: The projected cartilage areas determined with our 3D MRI analysis software were validated in pig knees. The projected cartilage area ratio at each segment in human knees had an intraclass correlation coefficient (ICC) of 0.87 to 0.99 (n = 16) between readers and 0.76 to 0.99 (n = 20) between measurements on repeat MR images. The projected cartilage area ratio (for cartilage thickness ≥1.5 mm) at the most affected segment in 8 human patients significantly decreased between the pairs of MR images obtained at intervals of 3 to 21 months. Conclusions: We proposed a novel evaluation method using 3D MRI to quantify the amount of cartilage in the knee. This method had a low measurement error in both pig and human knees. Clinical Relevance: The projected cartilage area ratio based on a particular thickness may serve as a sensitive method for assessing changes in cartilage over time.

BackgroundWe developed a fully automatic three-dimensional knee MRI analysis software that can quantify meniscus extrusion and cartilage measurements, including the projected cartilage area ratio (PCAR), which represents the ratio of the subject’s actual cartilage area to their ideal cartilage area. We also collected 3D MRI knee data from 561 volunteers (aged 30–79 years) from the “Kanagawa Knee Study.” Our purposes were to verify the accuracy of the software for automatic cartilage and meniscus segmentation using knee MRI and to examine the relationship between medial meniscus extrusion measurements and cartilage measurements from Kanagawa Knee Study data.MethodsWe constructed a neural network for the software by randomly choosing 10 healthy volunteers and 103 patients with knee pain. We validated the algorithm by randomly selecting 108 of these 113 subjects for training, and determined Dice similarity coefficients from five other subjects. We constructed a neural network using all data (113 subjects) for training. Cartilage thickness, cartilage volume, and PCAR in the medial femoral, lateral femoral, medial tibial, and lateral tibial regions were quantified by using the trained software on Kanagawa Knee Study data and their relationship with subject height was investigated. We also quantified the medial meniscus coverage ratio (MMCR), defined as the ratio of the overlapping area between the medial meniscus area and the medial tibial cartilage area to the medial tibial cartilage area. Finally, we examined the relationship between MMCR and PCAR at middle central medial tibial (mcMT) subregion located in the center of nine subregions in the medial tibial cartilage.ResultsDice similarity coefficients for cartilage and meniscus were both approximately 0.9. The femoral and tibial cartilage thickness and volume at each region correlated with height, but PCAR did not correlate with height in most settings. PCAR at the mcMT was significantly correlated with MMCR.ConclusionsOur software showed high segmentation accuracy for the knee cartilage and meniscus. PCAR was more useful than cartilage thickness or volume since it was less affected by height. A relationship was observed between the medial tibial cartilage measurements and the medial meniscus extrusion measurement in our cross-sectional study.Trial registration: UMIN, UMIN000032826; 1 September 2018,https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000037299

Background: We have developed a fully automatic three-dimensional MRI analysis software program for automatic segmentation of knee cartilage using a deep neural network. The purpose of this study was to use this software to clarify the interscan measurement error of the knee cartilage thickness and projected cartilage area ratio at 9 regions and 45 subregions in the knee. Methods: Ten healthy volunteers underwent MRI twice in the same day. The software provided cartilage thickness and projected cartilage area ratio (thickness ≥ 1.5 mm) at 9 regions and 45 subregions of the knee without any manual correction. The interscan measurement error was calculated at each region and subregion from the data of nine donors, except for one donor who had body motion during the MRI examination. Results: The interscan measurement error of cartilage thickness was less than 0.10 mm at all 9 regions and at 39 subregions among 45 subregions. The measurement errors ranged from 0.03 to 0.21 mm. The intraclass correlation coefficients (ICC) of cartilage thickness were higher than 0.75 at all 9 regions and 41 subregions. The interscan measurement error of the projected cartilage area ratio ranged from 0.01 to 0.03 for all 9 regions. Conclusions: This study clarified the interscan measurement error of the knee cartilage thickness and projected cartilage area ratio.

Objective We have developed a fully automatic three-dimensional MRI analysis software that measures the projected cartilage area ratio (PCAR) to allow for the quantification of the cartilage in the knee. Our objectives for this cross-sectional study were to verify our software’s accuracy and to quantify cartilage and meniscus extrusion. We also examined which cartilage quantification was most affected by age and analyzed the relationship between PCAR and meniscus extrusion. Methods 108 subjects were selected for training, and Dice similarity coefficients were determined from 5 other subjects. This study included 561 subjects between 30–70 years of age. From their knee MRI data, we quantified cartilage thickness, cartilage volume, and PCAR (0.0–1.5 mm) in four regions, including the medial tibial (MT) cartilage. Furthermore, each region was divided into nine subregions. The medial central (mc) subregion was also analyzed. As a quantification for meniscus extrusion, the medial meniscus coverage ratio (MMCR) was also investigated. Results Dice similarity coefficients were 0.911 and 0.892 for the femoral and tibial cartilage and 0.916 and 0.891 for the medial and lateral meniscus. Among 48 cartilage quantifications, the highest absolute value of the correlation coefficient with age was mcMT PCAR 1.0 mm in females and mcMT cartilage thickness in males. In females, mcMT PCAR 1.0 mm was correlated with MMCR, although MMCR was not correlated with age. In males, mcMT PCAR 0.0 mm was correlated with MMCR. Conclusions Our software showed high segmentation accuracy and provided numerous quantifications of cartilage related to age and meniscus extrusion.

Background: We developed a fully automatic three-dimensional knee MRI analysis software that can quantify meniscus extrusion and cartilage measurements, including the projected cartilage area ratio (PCAR), which represents the ratio of the subject’s actual cartilage area to their ideal cartilage area. We also collected 3D MRI knee data from 561 volunteers (aged 30–79 years) from the “Kanagawa Knee Study.” Our purposes were to verify the accuracy of the software for automatic cartilage and meniscus segmentation using knee MRI and to examine the relationship between medial meniscus extrusion measurements and cartilage measurements from Kanagawa Knee Study data. Methods: We constructed a neural network for the software by randomly choosing 10 healthy volunteers and 103 patients with knee pain. We validated the algorithm by randomly selecting 108 of these 113 subjects for training, and determined Dice similarity coefficients from five other subjects. We constructed a neural network using all data (113 subjects) for training. Cartilage thickness, cartilage volume, and PCAR in the medial femoral, lateral femoral, medial tibial, and lateral tibial regions were quantified by using the trained software on Kanagawa Knee Study data and their relationship with subject height was investigated. We also quantified the medial meniscus coverage ratio (MMCR), defined as the ratio of the overlapping area between the medial meniscus area and the medial tibial cartilage area to the medial tibial cartilage area. Finally, we examined the relationship between MMCR and PCAR at middle central medial tibial (mcMT) subregion located in the center of nine subregions in the medial tibial cartilage. Results: Dice similarity coefficients for cartilage and meniscus were both approximately 0.9. The femoral and tibial cartilage thickness and volume at each region correlated with height, but PCAR did not correlate with height in most settings. PCAR at the mcMT was significantly correlated with MMCR. Conclusions: Our software showed high segmentation accuracy for the knee cartilage and meniscus. PCAR was more useful than cartilage thickness or volume since it was less affected by height. A relationship was observed between the medial tibial cartilage measurements and the medial meniscus extrusion measurement in our cross-sectional study.

ObjectiveWe have developed a fully automatic three-dimensional MRI analysis software that measures the projected cartilage area ratio (PCAR) to allow for the quantification of the cartilage in the knee. Our objectives were to verify our software’s accuracy for segmentation of cartilage and meniscus and to quantify cartilage and meniscus extrusion in our cross-sectional study. We also examined which cartilage quantification was most affected by age and analyzed the relationship between PCAR and meniscus extrusion.MethodsMRI data from 108 subjects were selected for training, and Dice similarity coefficients were determined from 5 other subjects to verify the accuracy. Our cross-sectional study included other 561 subjects between 30–70 years of age. We quantified cartilage thickness, cartilage volume, and PCAR (0.0–1.5 mm) in medial femoral, lateral femoral, medial tibial, and medial lateral regions. Each region was divided into nine subregions, with particular focus on the medial central (mc) subregion. The medial meniscus coverage ratio (MMCR) was investigated as a quantification for meniscus extrusion.ResultsDice similarity coefficients for cartilage and meniscus were both approximately 0.9. Among cartilage quantifications, the highest absolute value of the correlation coefficient with age was mcMT PCAR 1.0 mm in females and mcMT cartilage thickness in males. In females, mcMT PCAR 1.0 mm was correlated with MMCR, although MMCR was not correlated with age. In males, mcMT PCAR 0.0 mm was correlated with MMCR.ConclusionsOur software showed high segmentation accuracy and provided numerous quantifications of cartilage related to age and meniscus extrusion.Trial registration: UMIN, UMIN000032826. Registered 1 September 2018, https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000037299