Martin Gorsky, Christopher Sirrs
World health by place: the politics of international health system metrics, 1924-c. 2010
Journal of global history
spinal cord stimulation scs wellestablished treatment management certain chronic pain conditions recently also garnered attention means modulating neural activity goal restoring lost autonomic sensorymotor function personalized modeling treatment planning critical aspects safe effective scs 46 60 however generation spine models required level detail accuracy requires time labor intensive manual image segmentation human experts hence need maximally automated segmentation routines capable producing highquality anatomical models used even cases available data limited end developed automated image segmentation model generation pipeline based novel convolutional neural network cnn architecture trained feline spinal cord magnetic resonance imaging mri data pipeline includes steps image preprocessing data augmentation transfer learning cleanup assess relative importance step pipeline choice cnn architecture systematically dropped steps substituted architectures quantifying downstream effects terms tissue segmentation quality jaccard index hausdorff distance predicted nerve recruitment estimated axonal depolarization leaveoneout analysis demonstrated pipeline step contributed small measurable increment mean segmentation quality surprisingly minor differences segmentation accuracy translated significant deviations ranging 4 13 pipeline step predicted nerve recruitment highlighting importance careful workflow design knowledge first analysis also assess downstream impact segmentation quality differences neurostimulation predictions furthermore transfer learning techniques enhanced segmentation metric consistency allowed generalization completely different spine region minimal additional training data work helps pave way towards fully automated personalized scs treatment planning clinical settings