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SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth
A key limitation of deep convolutional neural networks (DCNN) based imag...
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Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network
Synthesized medical images have several important applications, e.g., as...
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3D Segmentation Networks for Excessive Numbers of Classes: Distinct Bone Segmentation in Upper Bodies
Segmentation of distinct bones plays a crucial role in diagnosis, planni...
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Joint Unsupervised Learning for the Vertebra Segmentation, Artifact Reduction and Modality Translation of CBCT Images
We investigate the unsupervised learning of the vertebra segmentation, a...
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Generative Adversarial Networks for MR-CT Deformable Image Registration
Deformable Image Registration (DIR) of MR and CT images is one of the mo...
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Deep generative model-driven multimodal prostate segmentation in radiotherapy
Deep learning has shown unprecedented success in a variety of applicatio...
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Deep Mouse: An End-to-end Auto-context Refinement Framework for Brain Ventricle and Body Segmentation in Embryonic Mice Ultrasound Volumes
High-frequency ultrasound (HFU) is well suited for imaging embryonic mic...
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Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth
A lack of generalizability is one key limitation of deep learning based segmentation. Typically, one manually labels new training images when segmenting organs in different imaging modalities or segmenting abnormal organs from distinct disease cohorts. The manual efforts can be alleviated if one is able to reuse manual labels from one modality (e.g., MRI) to train a segmentation network for a new modality (e.g., CT). Previously, two stage methods have been proposed to use cycle generative adversarial networks (CycleGAN) to synthesize training images for a target modality. Then, these efforts trained a segmentation network independently using synthetic images. However, these two independent stages did not use the complementary information between synthesis and segmentation. Herein, we proposed a novel end-to-end synthesis and segmentation network (EssNet) to achieve the unpaired MRI to CT image synthesis and CT splenomegaly segmentation simultaneously without using manual labels on CT. The end-to-end EssNet achieved significantly higher median Dice similarity coefficient (0.9188) than the two stages strategy (0.8801), and even higher than canonical multi-atlas segmentation (0.9125) and ResNet method (0.9107), which used the CT manual labels.
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