Relationship between medial meniscus extrusion and cartilage measurements in the knee by fully automatic three-dimensional MRI analysis

Aoki, Hayato; Ozeki, Nobutake; Katano, Harutaka; Hyodo, Akinobu; Miura, Yugo; Matsuda, Junji; Takanashi, Kimiko; Suzuki, K.; Masumoto, Jun; Okanouchi, Noriya; Fujiwara, Takeo; Sekiya, Ichiro

doi:10.21203/rs.3.rs-43802/v2

Cited by 3 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, their systemic administration improves arthritis by inhibiting osteoclast differentiation in the arthritic animal model [ 48 ]. Clinical trials by administrating human MSCs into joint spaces have been examined [ 4 , 49 , 50 ]. Alternatively, MSC-derived exosomes, but not MSCs themselves, would be candidates for a novel treatment strategy for RA and OA since they can promote chondrogenesis [ 51 ].…”

Section: Discussionmentioning

confidence: 99%

CD34+THY1+ synovial fibroblast subset in arthritic joints has high osteoblastic and chondrogenic potentials in vitro

et al. 2022

Self Cite

View full text Add to dashboard Cite

Objective Synovial fibroblasts (SFs) in rheumatoid arthritis (RA) and osteoarthritis (OA) play biphasic roles in joint destruction and regeneration of bone/cartilage as mesenchymal stem cells (MSCs). Although MSCs contribute to joint homeostasis, such function is impaired in arthritic joints. We have identified functionally distinct three SF subsets characterized by the expression of CD34 and THY1 as follows: CD34+THY1+, CD34−THY1−, and CD34−THY1+. The objective of this study was to clarify the differentiation potentials as MSCs in each SF subset since both molecules would be associated with the MSC function. Methods SF subsets were isolated from synovial tissues of 70 patients (RA: 18, OA: 52). Expressions of surface markers associated with MSCs (THY1, CD34, CD73, CD271, CD54, CD44, and CD29) were evaluated in fleshly isolated SF subsets by flow cytometry. The differentiation potentials of osteogenesis, chondrogenesis, and adipogenesis were evaluated with histological staining and a quantitative polymerase chain reaction of differentiation marker genes. Small interfering RNA was examined to deplete THY1 in SFs. Results The expression levels of THY1+, CD73+, and CD271+ were highest and those of CD54+ and CD29+ were lowest in CD34+THY1+ among three subsets. Comparing three subsets, the calcified area, alkaline phosphatase (ALP)-stained area, and cartilage matrix subset were the largest in the CD34+THY1+ subset. Consistently, the expressions of differentiation markers of the osteoblasts (RUNX2, ALPL, and OCN) or chondrocytes (ACAN) were the highest in the CD34+THY1+ subset, indicating that the CD34+THY1+ subset possessed the highest osteogenic and chondrogenic potential among three subsets, while the differentiation potentials to adipocytes were comparable among the subsets regarding lipid droplet formations and the expression of LPL and PPARγ. The knockdown of THY1 in bulk SFs resulted in impaired osteoblast differentiation indicating some functional aspects in this stem-cell marker. Conclusion The CD34+THY1+ SF subset has high osteogenic and chondrogenic potentials. The preferential enhancement of MSC functions in the CD34+THY1+ subset may provide a new treatment strategy for regenerating damaged bone/cartilage in arthritic joints.

show abstract

Section: Discussionmentioning

confidence: 99%

CD34+THY1+ synovial fibroblast subset in arthritic joints has high osteoblastic and chondrogenic potentials in vitro

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The outputs were probability maps of the target regions, including the background region. Each pixel in the image was assigned a number as the region label for that pixel, and segmentation of the image was obtained 15 …”

Section: Methodsmentioning

confidence: 99%

“…2b). The tibial cartilage was divided into two regions (medial tibial and lateral tibial), and each region was divided into nine subregions by dividing the ROI into three equal parts horizontally and vertically 15 . In this study, only the medial compartment was analyzed (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Another limitation is that the measurements of osteophyte width, cartilage thickness, and MME were not corrected for patient height or BMI. According to our previous report, in which we analyzed data from the Kanagawa Knee Study, the thickness of the medial tibial cartilage was correlated with height, but only weakly, with a correlation coefficient of 0.2 15 …”

Section: Limitationsmentioning

confidence: 95%

See 1 more Smart Citation

Medial Tibial Osteophyte Width Strongly Reflects Medial Meniscus Extrusion Distance and Medial Joint Space Width Moderately Reflects Cartilage Thickness in Knee Radiographs

Sekiya

Sasaki

Miura

et al. 2022

Magnetic Resonance Imaging

Self Cite

View full text Add to dashboard Cite

Background: The presence of medial tibial osteophytes on knee radiographs suggests cartilage wear, but may be associated with medial meniscus extrusion (MME). The joint space width of the medial compartment consists anatomically of cartilage and the medial meniscus, but which is most responsible for joint space narrowing remains unclear. Magnetic resonance imaging (MRI) reveals MME and cartilage thickness. Purposes: To determine which radiographic medial tibial osteophyte width correlates better with cartilage thickness or MME distance and which radiographic medial joint space width correlates better with cartilage thickness or MME distance. Study Type: Cross-sectional. Population: Total of 527 subjects, 253 females and 274 males, aged 30-79 years, included in the Kanagawa Knee Study. Field Strength/Sequence: 3 T/fat-suppressed spoiled gradient echo and proton density weighted. Assessment: The medial tibial osteophyte width and "the minimum joint space width at the medial compartment" (mJSW) were measured from plain radiographs. The cartilage region was automatically extracted from MRI data using software. The medial femoral and tibial cartilage regions were each divided into nine subregions, and the average thickness of the cartilage was determined in each region and subregion. MME was manually measured by two orthopedic surgeons using MRI coronal section images. Statistical Tests: Pearson's correlation coefficient and their comparison, with P < 0.05 considered statistically significant. Results: The absolute values of the correlation coefficients were 0.33 at maximum between osteophyte width and cartilage thickness and 0.76 between osteophyte width and MME; the value was significantly higher with MME than with cartilage thickness (P < 0.001). The absolute values of the correlation coefficients were 0.50 at maximum between mJSW and cartilage thickness and 0.16 between mJSW and MME; the value was significantly higher with cartilage thickness than with MME (P < 0.001). Data Conclusion:The medial tibial osteophyte width strongly reflected MME and the medial joint space width moderately reflected cartilage thickness. Level of Evidence: 3 Technical Efficacy Stage: 3

show abstract

“…M Eniscus medical image segmentation [1], [4], [12], [19], [24], [31], [43] is an important part of human knee joint health analysis, and it has important guiding significance for subsequent diagnostic analysis [3], [8], [26]. With the development of deep learning, although fully supervised medical image segmentation has been widely studied, due to the fact that datasets in the medical field are often difficult to obtain or the scale is small, and obtaining high-quality segmentation labels often requires time-consuming production by experienced physicians, it is difficult to train excellent medical image segmentation models with full supervision.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of meniscus based on MAE self-supervision and point-line weak supervision paradigm

Xie¹,

Jiang²,

Zhang³

et al. 2022

Preprint

View full text Add to dashboard Cite

Medical image segmentation based on deep learning is often faced with the problems of insufficient data sets and long time-consuming labeling. In this paper, we introduce the self-supervised method MAE(Masked Autoencoders) into knee joint images to provide a good initial weight for the segmentation model and improve the adaptability of the model to small datasets. Secondly, we propose a weakly supervised paradigm for meniscus segmentation based on the combination of point and line to reduce the time of labeling. Based on the weak label ,we design a region growing algorithm to generate pseudolabel. Finally we train the segmentation network based on pseudo-labels with weight transfer from self-supervision. Sufficient experimental results show that our proposed method combining self-supervision and weak supervision can almost approach the performance of purely fully supervised models while greatly reducing the required labeling time and dataset size.

show abstract

Relationship between medial meniscus extrusion and cartilage measurements in the knee by fully automatic three-dimensional MRI analysis

Cited by 3 publications

References 12 publications

CD34+THY1+ synovial fibroblast subset in arthritic joints has high osteoblastic and chondrogenic potentials in vitro

CD34+THY1+ synovial fibroblast subset in arthritic joints has high osteoblastic and chondrogenic potentials in vitro

Medial Tibial Osteophyte Width Strongly Reflects Medial Meniscus Extrusion Distance and Medial Joint Space Width Moderately Reflects Cartilage Thickness in Knee Radiographs

Automatic segmentation of meniscus based on MAE self-supervision and point-line weak supervision paradigm

Contact Info

Product

Resources

About