Yuki Imamatsu, Yuka Iwata, Ayuka Yokoyama, Yuko Tanaka, Etsuko Tadaka
Empowering Community Health Workers in Japan: Determinants of Non-Communicable Disease Prevention Competency
Healthcare (Basel, Switzerland)
classificationbased segmentation method proposed quantify synovium rheumatoid arthritis ra patients using deep learning dl method based timeintensity curve tic analysis dynamic contrastenhanced magnetic resonance imaging dcemri retrospective study analyzed hand mr dataset 28 ra patients six males mean age 537 years researcher expert guidance used inhouse software delineate regions interest rois hand muscles bones synovitis generating dataset 27255 pixels corresponding tics muscle 11413 bone 8502 synovitis 7340 one experienced musculoskeletal radiologist performed ground truth segmentation enhanced pannus joint bounding box 10th dce phase around 5 min contrast injection data preprocessing included median filtering noise reduction phaseonly correlation algorithm motion correction contrastlimited adaptive histogram equalization improved image contrast noise suppression tic intensity values normalized using zeromean normalization dl model dilated causal convolution selu activation function developed enhanced pannus segmentation tested using leaveoneout crossvalidation 407 joint bounding boxes manually segmented 129 synovitis masks pixelbased level dl model achieved sensitivity 85 specificity 98 accuracy 99 precision 84 enhanced pannus segmentation mean dice score 073 falsepositive rate predicting cases without synovitis 08 dlmeasured enhanced pannus volume strongly correlated ground truth pixelbased r 087 p 0001 patientbased levels r 084 p 0001 blandaltman analysis showed mean difference hand joints pixelbased patientbased levels 946 mm3 5087 mm3 respectively dlbased dcemri tic shape analysis potential automatic segmentation quantification enhanced synovium hands ra patients