ArticleIEEE transactions on medical imaging2018
SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth.
Article in IEEE transactions on medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 1 of them a synthesis that pooled it.
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Who cites it
58 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation.Journal of digital imaging · 2022Pooled it
- [Diffusion cycle-consistent generative adversarial networks for pelvic active bone marrow segmentation].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- SCEAF-UNet: Medical image segmentation based on spatial-channel feature enhancement and adaptive fusion.PloS one · 2026Article
- Towards bridging the synthetic-to-real gap in quantitative photoacoustic tomography via unsupervised domain adaptation.Photoacoustics · 2025Article
- Generative Artificial Intelligence in Prostate Cancer Imaging.Balkan medical journal · 2025Review
- Histogram matching-enhanced adversarial learning for unsupervised domain adaptation in medical image segmentation.Medical physics · 2025Article
- Medical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods.Tomography (Ann Arbor, Mich.) · 2025Review
- SVNC-Net: An optimized U-Net variant with 2D convolutions for lightweight 3D spleen segmentation.PloS one · 2025Article
- Multi-scale anatomical regularization for domain-adaptive segmentation of pelvic CBCT images.Medical physics · 2024Article
- Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Biparametric MRI Datasets.Radiology. Artificial intelligence · 2024Article
- Cross-Modality Medical Image Segmentation via Enhanced Feature Alignment and Cross Pseudo Supervision Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Entropy and distance-guided super self-ensembling for optic disc and cup segmentation.Biomedical optics express · 2024Article
- MITIGATING OVER-SATURATED FLUORESCENCE IMAGES THROUGH A SEMI-SUPERVISED GENERATIVE ADVERSARIAL NETWORK.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Alleviating tiling effect by random walk sliding window in high-resolution histological whole slide image synthesis.Proceedings of machine learning research · 2024Article
- MSR-UNet: enhancing multi-scale and long-range dependencies in medical image segmentation.PeerJ. Computer science · 2024Article
- Self-Adaptive Teacher-Student framework for colon polyp segmentation from unannotated private data with public annotated datasets.PloS one · 2024Article
- AnyStar: Domain randomized universal star-convex 3D instance segmentation.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2024Article
- Non-invasive prediction of overall survival time for glioblastoma multiforme patients based on multimodal MRI radiomics.International journal of imaging systems and technology · 2023Article
- Single slice thigh CT muscle group segmentation with domain adaptation and self-training.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining.Medical image analysis · 2023Article
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Authors and funding
9 authors.
Funding
Abstract
A key limitation of deep convolutional neural networks (DCNN) based image segmentation methods is the lack of generalizability. Manually traced training images are typically required when segmenting organs in a new imaging modality or from distinct disease cohort. The manual efforts can be alleviated if the manually traced images in one imaging modality (e.g., MRI) are able to train a segmentation network for another imaging modality (e.g., CT). In this paper, we propose an end-to-end synthetic segmentation network (SynSeg-Net) to train a segmentation network for a target imaging modality without having manual labels. SynSeg-Net is trained by using (1) unpaired intensity images from source and target modalities, and (2) manual labels only from source modality. SynSeg-Net is enabled by the recent advances of cycle generative adversarial networks (CycleGAN) and DCNN. We evaluate the performance of the SynSeg-Net on two experiments: (1) MRI to CT splenomegaly synthetic segmentation for abdominal images, and (2) CT to MRI total intracranial volume synthetic segmentation (TICV) for brain images. The proposed end-to-end approach achieved superior performance to two stage methods. Moreover, the SynSeg-Net achieved comparable performance to the traditional segmentation network using target modality labels in certain scenarios. The source code of SynSeg-Net is publicly available 2.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.