Somayeh Delavari, Kamran Soltani Arabshahi, Mitra Amini, Maryam Aalaa, Ghadir Pourbairamian, Niloofar Bahoosh, Nasrin Asadi, Bhavin Dalal, Javad Kojuri, Hadi Hamidi, Sajad Delavari
The Relationship between Experiences Level and Clinical Decision-Making Skill in Midwifery Students: A Cross-Sectional Study
Medical journal of the Islamic Republic of Iran
lung tissue lung excursion segmentation thoracic dynamic magnetic resonance imaging dmri critical step quantitative analysis thoracic structure function patients respiratory disorders thoracic insufficiency syndrome tis however complex variability intensity shape anatomical structures low contrast lung surrounding tissue mr images seriously hamper accuracy robustness automatic segmentation methods paper develop interactive deeplearning based segmentation system solve problem considering significant difference lung morphological characteristics normal subjects tis subjects utilized two independent data sets normal subjects tis subjects train test model 202 dmri scans 101 normal pediatric subjects 92 dmri scans 46 tis pediatric subjects acquired study randomly divided training validation test sets approximate ratio 514 first designed interactive region interest roi strategy detect lung roi dmri accelerating training speed reducing negative influence tissue located far away lung lung segmentation second utilized modified 2d unet segment lung tissue lung rois adjacent slices utilized input data take advantage spatial information lungs third extracted lung shell lung segmentation results shape feature inputted lung rois shape feature another modified 2d unet segment lung excursion dmri evaluate performance approach computed dice coefficient dc maxmean hausdorff distance mmhd manual automatic segmentations addition utilized coefficient variation cv assess variability method repeated dmri scans differences lung tidal volumes computed manual automatic segmentation results proposed system yielded mean dice coefficients 096002 089005 lung segmentation dmri normal subjects tis subjects respectively demonstrating excellent agreement manual delineation results coefficient variation pvalues show estimated lung tidal volumes approach statistically indistinguishable derived manual segmentations proposed approach applied lung tissue lung excursion segmentation dynamic mr images high accuracy efficiency proposed approach potential utilized assessment patients tis via dmri routinely