Using DeepLab v3 + -based semantic segmentation to evaluate platelet activation

[1] Modic M(2014)Morphology transformations of platelets on plasma activated surfaces Plasma Processes Polym 11 596-605

[2] Junkar I(2018)Wound healing in streptozotocin-induced diabetic rats using atmospheric-pressure argon plasma jet Sci Rep 8 1-15

[3] Stana-Kleinschek K(2018)Simultaneous segmentation of leukocyte and erythrocyte in microscopic images using a marker-controlled watershed algorithm Comput Math Methods Med 2018 1-9

[4] Kostanjšek R(2017)Label-free detection of aggregated platelets in blood by machine-learning-aided optofluidic time-stretch microscopy Lab Chip 17 2426-2434

[5] Mozetič M(2019)3D ultrastructural analysis of α-granule, dense granule, mitochondria, and canalicular system arrangement in resting human platelets Res Practice Thromb Haemost 4 72-85

[6] Polymers PJPPa K-Y(2018)Classification of red blood cell shapes in flow using outlier tolerant machine learning PLoS Comput Biol 14 1-11

[7] Cheng Z-H(2017)A deep convolutional neural network for classification of red blood cells in sickle cell anemia PLoS Comput Biol 13 248-255

[8] Lin Y-P(2021)Dense cellular segmentation for EM using 2D–3D neutral network ensemble Sci Rep 11 387-392

[9] Cheng H-Y(2009)Imagenet: a large-scale hierarchical image database IEEE Confer Comput Vis Pattern Recognit 2009 undefined-undefined

[10] Chiu N-L(2010)Air pollution, platelet activation and atherosclerosis Inflamm Allergy-Drug Targets (Formerly Current Drug Targets-Inflammation & Allergy) 9 undefined-undefined

[1] Modic M(2014)Morphology transformations of platelets on plasma activated surfaces Plasma Processes Polym 11 596-605

[2] Junkar I(2018)Wound healing in streptozotocin-induced diabetic rats using atmospheric-pressure argon plasma jet Sci Rep 8 1-15

[3] Stana-Kleinschek K(2018)Simultaneous segmentation of leukocyte and erythrocyte in microscopic images using a marker-controlled watershed algorithm Comput Math Methods Med 2018 1-9

[4] Kostanjšek R(2017)Label-free detection of aggregated platelets in blood by machine-learning-aided optofluidic time-stretch microscopy Lab Chip 17 2426-2434

[5] Mozetič M(2019)3D ultrastructural analysis of α-granule, dense granule, mitochondria, and canalicular system arrangement in resting human platelets Res Practice Thromb Haemost 4 72-85

[6] Polymers PJPPa K-Y(2018)Classification of red blood cell shapes in flow using outlier tolerant machine learning PLoS Comput Biol 14 1-11

[7] Cheng Z-H(2017)A deep convolutional neural network for classification of red blood cells in sickle cell anemia PLoS Comput Biol 13 248-255

[8] Lin Y-P(2021)Dense cellular segmentation for EM using 2D–3D neutral network ensemble Sci Rep 11 387-392

[9] Cheng H-Y(2009)Imagenet: a large-scale hierarchical image database IEEE Confer Comput Vis Pattern Recognit 2009 undefined-undefined

[10] Chiu N-L(2010)Air pollution, platelet activation and atherosclerosis Inflamm Allergy-Drug Targets (Formerly Current Drug Targets-Inflammation & Allergy) 9 undefined-undefined