ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2022
Self-supervised 3D anatomy segmentation using self-distilled masked image transformer (SMIT).
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 34 citations in OpenAlex.
- Transformer-based cardiac substructure segmentation from contrast and non-contrast computed tomography for radiotherapy planning.Physics and imaging in radiation oncology · 2026Article
- Segmentation regularized training for multi-domain deep learning registration applied to magnetic resonance-guided prostate cancer radiotherapy.Physics and imaging in radiation oncology · 2026Article
- LEARNABLE HIERARCHICAL VISUAL CONTEXTS FOR TUMOR SEGMENTATION IN COMPUTED TOMOGRAPHY IMAGES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- Random forest-based out-of-distribution detection for robust lung cancer segmentation.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Modality-agnostic, patient-specific digital twins modeling temporally varying digestive motion.Physics in medicine and biology · 2026Article
- Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation.Transactions on machine learning research · 2026Article
- Patient-specific fine-tuning of a self-distilled transformer model for normal tissue segmentation in abdominal magnetic resonance guided adaptive radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Co-distilled attention guided masked image modeling with noisy teacher for self-supervised learning on medical images.Proceedings of machine learning research · 2026Article
- Three discipline collaborative radiation therapy (3DCRT) special debate: AI structure segmentation is better than clinician contouring for both OARs and targets.Journal of applied clinical medical physics · 2025Article
- BENCHMARKING TRANSFERABILITY OF SELF-SUPERVISED PRETRAINING FOR MULTI-ORGAN SEGMENTATION ON DIFFERENT MODALITIES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Learning General-Purpose Biomedical Volume Representations using Randomized Synthesis.... International Conference on Learning Representations · 2025Article
- Self-supervised learning improves robustness of deep learning lung tumor segmentation models to CT imaging differences.Medical physics · 2025Article
- Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- AI-based 3D analysis of retinal vasculature associated with retinal diseases using OCT angiography.Biomedical optics express · 2024Article
- Self-supervised pretraining in the wild imparts image acquisition robustness to medical image transformers: an application to lung cancer segmentation.Proceedings of machine learning research · 2024Article
- Multi-scale nested UNet with transformer for colorectal polyp segmentation.Journal of applied clinical medical physics · 2024Article
- Training Robust T1-Weighted Magnetic Resonance Imaging Liver Segmentation Models Using Ensembles of Datasets with Different Contrast Protocols and Liver Disease Etiologies.Research square · 2024Article
- A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound.BMC medical imaging · 2024Review
- SwinCross: Cross-modal Swin transformer for head-and-neck tumor segmentation in PET/CT images.Medical physics · 2024Article
- Auto-segmentation of neck nodal metastases using self-distilled masked image transformer on longitudinal MR images.BJR artificial intelligence · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Vision transformers efficiently model long-range context and thus have demonstrated impressive accuracy gains in several image analysis tasks including segmentation. However, such methods need large labeled datasets for training, which is hard to obtain for medical image analysis. Self-supervised learning (SSL) has demonstrated success in medical image segmentation using convolutional networks. In this work, we developed a self-distillation learning with masked image modeling method to perform SSL for vision transformers (SMIT) applied to 3D multi-organ segmentation from CT and MRI. Our contribution combines a dense pixel-wise regression pretext task performed within masked patches called masked image prediction with masked patch token distillation to pre-train vision transformers. Our approach is more accurate and requires fewer fine tuning datasets than other pretext tasks. Unlike prior methods, which typically used image sets arising from disease sites and imaging modalities corresponding to the target tasks, we used 3,643 CT scans (602,708 images) arising from head and neck, lung, and kidney cancers as well as COVID-19 for pre-training and applied it to abdominal organs segmentation from MRI pancreatic cancer patients as well as publicly available 13 different abdominal organs segmentation from CT. Our method showed clear accuracy improvement (average DSC of 0.875 from MRI and 0.878 from CT) with reduced requirement for fine-tuning datasets over commonly used pretext tasks. Extensive comparisons against multiple current SSL methods were done. Our code is available at: https://github.com/harveerar/SMIT.git.
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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.