54 citations · 212 across the 13 of their papers we have counts for
8 papers · 1 filter
Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT
Jue Jiang, Jun Hong, Kathryn Tringale +4
Method: ProRSeg was trained using 5-fold cross-validation with 110 T2-weighted MRI acquired at 5 treatment fractions from 10 different patients, taking care that same patient scans…
Self-supervised 3D anatomy segmentation using self-distilled masked image transformer (SMIT)
Jue Jiang, Neelam Tyagi, Kathryn Tringale +2
Vision transformers, with their ability to more efficiently model long-range context, have demonstrated impressive accuracy gains in several computer vision and medical image analy…
One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs
Jue Jiang, Harini Veeraraghavan
Image-guided adaptive lung radiotherapy requires accurate tumor and organs segmentation from during treatment cone-beam CT (CBCT) images. Thoracic CBCTs are hard to segment because…
Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation
Jue Jiang, Sadegh Riyahi Alam, Ishita Chen +4
Despite the widespread availability of in-treatment room cone beam computed tomography (CBCT) imaging, due to the lack of reliable segmentation methods, CBCT is only used for gross…
Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation
Jue Jiang, Harini Veeraraghavan
Our contribution is a unified cross-modality feature disentagling approach for multi-domain image translation and multiple organ segmentation. Using CT as the labeled source domain…
PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentation
Jue Jiang, Yu Chi Hu, Neelam Tyagi +5
We developed a new joint probabilistic segmentation and image distribution matching generative adversarial network (PSIGAN) for unsupervised domain adaptation (UDA) and multi-organ…
Multiple resolution residual network for automatic thoracic organs-at-risk segmentation from CT
Hyemin Um, Jue Jiang, Maria Thor +4
We implemented and evaluated a multiple resolution residual network (MRRN) for multiple normal organs-at-risk (OAR) segmentation from computed tomography (CT) images for thoracic r…
Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation
Jue Jiang, Jason Hu, Neelam Tyagi +4
Lung tumors, especially those located close to or surrounded by soft tissues like the mediastinum, are difficult to segment due to the low soft tissue contrast on computed tomograp…