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Found 17 papers, 2 papers with code
Trustworthiness of Pretrained Transformers for Lung Cancer Segmentation
We assessed the trustworthiness of two self-supervision pretrained transformer models, Swin UNETR and SMIT, for fine-tuned lung (LC) tumor segmentation using 670 CT and MRI scans.
Self-distilled Masked Attention guided masked image modeling with noise Regularized Teacher (SMART) for medical image analysis
However, more accurate and efficient attention guided MIM approaches are difficult to implement with Swin due to it's lack of an explicit global attention.
Deep Learning Based Dominant Index Lesion Segmentation for MR-guided Radiation Therapy of Prostate Cancer
MRRN-DS significantly outperformed ResUnet in Dataset2 (DSC of 0. 54 vs. 0. 44, p<0. 001) and the Unet++ in Dataset3 (DSC of 0. 45 vs. p=0. 04).
Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT
ProRSeg based dose accumulation accounting for intra-fraction (pre-treatment to post-treatment MRI scan) and inter-fraction motion showed that the organ dose constraints were violated in 4 patients for stomach-duodenum and for 3 patients for small bowel.
Self-supervised 3D anatomy segmentation using self-distilled masked image transformer (SMIT)
Self-supervised learning (SSL) has demonstrated success in medical image segmentation using convolutional networks.
Wasserstein Image Local Analysis: Histogram of Orientations, Smoothing and Edge Detection
We approach the directionality problem from a novel perspective by the use of the optimal transport map of a local image patch to a uni-color patch of its mean.
One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs
The registration network was trained in an unsupervised manner using pairs of planning CT (pCT) and CBCT images and produced a progressively deformed sequence of images.
Unpaired cross-modality educed distillation (CMEDL) for medical image segmentation
Network design, methods to combine MRI with CT information, distillation learning under informative (MRI to CT), weak (CT to MRI) and equal teacher (MRI to MRI), and ablation tests were performed.
Nested-block self-attention for robust radiotherapy planning segmentation
NBSA's segmentations were less variable than multiple 3D methods, including for small organs with low soft-tissue contrast such as the submandibular glands (surface Dice of 0. 90).
Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation
Validation was done on 20 weekly CBCTs from patients not used in training.
Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation
Our contribution is a unified cross-modality feature disentagling approach for multi-domain image translation and multiple organ segmentation.
PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentation
Our method achieved an overall average DSC of 0. 87 on T1w and 0. 90 on T2w for the abdominal organs, 0. 82 on T2wFS for the parotid glands, and 0. 77 on T2w MRI for lung tumors.
Multiple resolution residual network for automatic thoracic organs-at-risk segmentation from CT
Our approach simultaneously combines feature streams computed at multiple image resolutions and feature levels through residual connections.
Local block-wise self attention for normal organ segmentation
We developed our approach using U-net and compared it against multiple state-of-the-art self attention methods.
Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation
Our approach, called cross-modality educed deep learning segmentation (CMEDL) combines CT and pseudo MR produced from CT by aligning their features to obtain segmentation on CT.
Comparison of Patch-Based Conditional Generative Adversarial Neural Net Models with Emphasis on Model Robustness for Use in Head and Neck Cases for MR-Only planning
A total of twenty paired CT and MR images were used in this study to investigate two conditional generative adversarial networks, Pix2Pix, and Cycle GAN, for generating synthetic CT images for Headand Neck cancer cases.
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets
This method produced the highest segmentation accuracy with a DSC of 0. 75 and the lowest Hausdroff distance on the test dataset.
