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Evaluation of five image registration tools for abdominal CT: pitfalls and opportunities with soft anatomy.

, , , , , , and . Medical Imaging: Image Processing, volume 9413 of SPIE Proceedings, page 94131N. SPIE, (2015)

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Multi-atlas segmentation enables robust multi-contrast MRI spleen segmentation for splenomegaly., , , , , , and . Medical Imaging: Image Processing, volume 10133 of SPIE Proceedings, page 101330A. SPIE, (2017)Whole abdominal wall segmentation using Augmented Active Shape Models (AASM) with multi-atlas label fusion and level set., , , , and . Medical Imaging: Image Processing, volume 9784 of SPIE Proceedings, page 97840U. SPIE, (2016)SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth., , , , , , , , and . CoRR, (2018)Splenomegaly Segmentation on Multi-Modal MRI Using Deep Convolutional Networks., , , , , , , , , and 1 other author(s). IEEE Trans. Medical Imaging, 38 (5): 1185-1196 (2019)RAP-Net: Coarse-to-Fine Multi-Organ Segmentation with Single Random Anatomical Prior., , , , , and . CoRR, (2020)Validation and optimization of multi-organ segmentation on clinical imaging archives., , , , , , , , , and 2 other author(s). Medical Imaging: Image Processing, volume 11313 of SPIE Proceedings, page 113132O. SPIE, (2020)Robust Multicontrast MRI Spleen Segmentation for Splenomegaly Using Multi-Atlas Segmentation., , , , , , and . IEEE Trans. Biomed. Eng., 65 (2): 336-343 (2018)Adversarial synthesis learning enables segmentation without target modality ground truth., , , , , and . ISBI, page 1217-1220. IEEE, (2018)Splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks., , , , , , , , , and . Medical Imaging: Image Processing, volume 10574 of SPIE Proceedings, page 1057409. SPIE, (2018)UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation., , , , , , , , , and 5 other author(s). CoRR, (2022)