Guang Fu, Zishun Xu, Shao Zhang
Navigating appendicitis care during the Covid-19 pandemic: a retrospective cohort study in China
BMC surgery
cryoelectron tomography cryoet allows visualize cellular context macromolecular level date impossibility obtaining reliable ground truth limiting application deep learningbased image processing algorithms field consequence growing demand realistic synthetic datasets training deep learning algorithms addition besides assisting acquisition interpretation experimental data synthetic tomograms used reference models cellular organization analysis cellular tomograms current simulators cryoet focus reproducing distortions image acquisition tomogram reconstruction however generate many low order features present cellular tomograms propose several geometric organization models simulate low order cellular structures imaged cryoet specifically clusters known cytosolic membrane bound macromolecules membranes different geometries well different filamentous structures microtubules actinlike networks moreover use parametrizable stochastic models generate high diversity geometries organizations simulate representative generalized datasets including crowded environments like observed native cells models implemented multiplatform opensource python package including scripts generate cryotomograms adjustable sizes resolutions addition scripts provide also distortionfree density maps besides ground truth different file formats efficient access advanced visualization show realistic synthetic dataset readily used train generalizable deep learning algorithms