Med Biol Eng Comput - A fully automated human knee 3D MRI bone segmentation using the ray casting technique.

Tópicos

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Resumo

This study aimed at developing a fully automated bone segmentation method for the human knee (femur and tibia) from magnetic resonance (MR) images. MR imaging was acquired on a whole body 1.5T scanner with a gradient echo fat suppressed sequence using an extremity coil. The method was based on the Ray Casting technique which relies on the decomposition of the MR images into multiple surface layers to localize the boundaries of the bones and several partial segmentation objects being automatically merged to obtain the final complete segmentation of the bones. Validation analyses were performed on 161 MR images from knee osteoarthritis patients, comparing the developed fully automated to a validated semi-automated segmentation method, using the average surface distance (ASD), volume correlation coefficient, and Dice similarity coefficient (DSC). For both femur and tibia, respectively, data showed excellent bone surface ASD (0.50???0.12?mm; 0.37???0.09?mm), average oriented distance between bone surfaces within the cartilage domain (0.02???0.07?mm; -0.05???0.10?mm), and bone volume DSC (0.94???0.05; 0.92???0.07). This newly developed fully automated bone segmentation method will enable large scale studies to be conducted within shorter time durations, as well as increase stability in the reading of pathological bone.

Resumo Limpo

studi aim develop fulli autom bone segment method human knee femur tibia magnet reson mr imag mr imag acquir whole bodi t scanner gradient echo fat suppress sequenc use extrem coil method base ray cast techniqu reli decomposit mr imag multipl surfac layer local boundari bone sever partial segment object automat merg obtain final complet segment bone valid analys perform mr imag knee osteoarthr patient compar develop fulli autom valid semiautom segment method use averag surfac distanc asd volum correl coeffici dice similar coeffici dsc femur tibia respect data show excel bone surfac asd mm mm averag orient distanc bone surfac within cartilag domain mm mm bone volum dsc newli develop fulli autom bone segment method will enabl larg scale studi conduct within shorter time durat well increas stabil read patholog bone

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