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Medical Imaging with Deep Learning: MIDL 2019 - Extended Abstract Track.

, , , , , , и . CoRR, (2019)

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Oguz Karagöz

Kamile Ipek

Ipek Ölcüm

Ipek Tüner

Gonca Ipek-Özdemir

 

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Fully automated rodent brain MR image processing pipeline on a Midas server: from acquired images to region-based statistics., , , , , и . Frontiers Neuroinformatics, (2013)Enhanced cortical thickness measurements for rodent brains via Lagrangian-based RK4 streamline computation., , , и . Medical Imaging: Image Processing, том 9784 из SPIE Proceedings, стр. 97840B. SPIE, (2016)Medical Imaging with Deep Learning: MIDL 2019 - Extended Abstract Track., , , , , , и . CoRR, (2019)Self-Supervised CSF Inpainting with Synthetic Atrophy for Improved Accuracy Validation of Cortical Surface Analyses., , и . CoRR, (2023)Entropy-based particle correspondence for shape populations., , , , , , , и . Int. J. Comput. Assist. Radiol. Surg., 11 (7): 1221-1232 (2016)Longitudinal subcortical segmentation with deep learning., , , , , и . Medical Imaging: Image Processing, том 11596 из SPIE Proceedings, SPIE, (2021)Establishing surface correspondence for post-surgical cortical thickness changes in temporal lobe epilepsy., , , , , , , и . Medical Imaging: Image Processing, том 11596 из SPIE Proceedings, SPIE, (2021)CATS v2: Hybrid encoders for robust medical segmentation., , , , , и . CoRR, (2023)VesselMorph: Domain-Generalized Retinal Vessel Segmentation via Shape-Aware Representation., , , , , и . CoRR, (2023)Generalizing MRI Subcortical Segmentation to Neurodegeneration., , , , , , и . MLCN/RNO-AI@MICCAI, том 12449 из Lecture Notes in Computer Science, стр. 139-147. Springer, (2020)