Yang Lei
Emory University
Deep learningRadiologyHausdorff distanceMagnetic resonance imagingGround truthArtificial intelligenceImage registrationPattern recognitionRadiation treatment planningProstateContouringNuclear medicineComputer visionComputer scienceRadiation therapyImage qualityMedicineConvolutional neural networkSegmentation
206Publications
22H-index
1,441Citations
Publications 190
Newest
#1Xianjin Dai (Emory University)H-Index: 10
#2Yang Lei (Emory University)H-Index: 22
Last. Xiaofeng Yang (Emory University)H-Index: 29
view all 9 authors...
MRI allows accurate and reliable organ delineation for many disease sites in radiation therapy due to its superior soft-tissue contrast. Manual organ-at-risk (OAR) delineation is labor-intensive, time-consuming and subjective. This study aims to develop a deep learning-based automated multi-organ segmentation method to release the labor and accelerate the treatment planning process for head-and-neck (HN) cancer radiotherapy. We propose a novel regional convolutional neural network (R-CNN) archit...
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Purpose/objective(s) null Ultrasound (US) imaging has been widely used in image-guided prostate brachytherapy. Current prostate brachytherapy uses transrectal US (TRUS) images for implant guidance, where contours of prostate and organs-at risk are necessary for treatment planning and dose evaluation. This work aims to develop a deep learning-based method for pelvic multi-organ TRUS segmentation to improve TRUS-guided prostate brachytherapy. null Materials/methods null We developed an anchor-free...
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Purpose/Objective(s) null Daily Cone beam CT (CBCT) imaging provides necessary anatomical information for accurate patient setup. Image quality of CBCT is usually far inferior to simulation CT scans. A workaround is to register the CT to the CBCT such that the contours and Hounsfield Unit (HU) values of the CT can be propagated to the CBCT. However, the inconsistent HU values across CT and CBCT make it less effective to use conventional image similarity measures. We aim to develop an unsupervise...
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PURPOSE/OBJECTIVE(S) Ultrasound (US) imaging has been widely used in image-guided needle/seed placement in prostate brachytherapy. For 3D US images with large slice thickness, high frequency information in the slice direction is missing and cannot be resolved through interpolation. As an ill-posed problem, most high-resolution generation methods rely on the presence of external/training atlases to learn the transform from low resolution to high resolution images. The anatomical variations among ...
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#1Xianjin Dai (Emory University)H-Index: 10
#2Yang Lei (Emory University)H-Index: 22
Last. Xiaofeng Yang (The Wallace H. Coulter Department of Biomedical Engineering)H-Index: 29
view all 8 authors...
PURPOSE Ultrasound (US) imaging is an established imaging modality capable of offering video-rate volumetric images without ionizing radiation. It has the potential for intra-fraction motion tracking in radiation therapy. In this study, a deep learning-based method has been developed to tackle the challenges in motion tracking using US imaging. METHODS We present a Markov-like network, which is implemented via generative adversarial networks, to extract features from sequential US frames (one tr...
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PURPOSE/OBJECTIVE(S) During prostate high-dose-rate (HDR) brachytherapy, physicians select needle positions in a peripheral loading technique based on experience but without knowledge of the achievable dose distribution. Sub-optimal needle placement may lead to unfavorable dose distributions even after plan optimization. We propose a new deep learning-based method to predict HDR needle position for prostate HDR brachytherapy. MATERIALS/METHODS The proposed framework consists of three major steps...
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Kilovoltage cone-beam computed tomography (CBCT)-based image-guided radiation therapy (IGRT) is used for daily delivery of radiation therapy, especially for stereotactic body radiation therapy (SBRT), which imposes particularly high demands for setup accuracy. The clinical applications of CBCTs are constrained, however, by poor soft tissue contrast, image artifacts, and instability of Hounsfield unit (HU) values. Here, we propose a new deep learning-based method to generate synthetic CTs (sCT) f...
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#1Xianjin Dai (Emory University)H-Index: 10
#2Yang Lei (Emory University)H-Index: 22
Last. Xiaofeng Yang (Emory University)H-Index: 29
view all 10 authors...
PURPOSE The delineation of organs at risk (OARs) is fundamental to cone-beam CT (CBCT)-based adaptive radiotherapy treatment planning, but is time consuming, labor intensive, and subject to interoperator variability. We investigated a deep learning-based rapid multiorgan delineation method for use in CBCT-guided adaptive pancreatic radiotherapy. METHODS To improve the accuracy of OAR delineation, two innovative solutions have been proposed in this study. First, instead of directly segmenting org...
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#8Xiaofeng Yang (Emory University)H-Index: 29
Purpose null High-dose-rate (HDR) prostate brachytherapy involves treatment catheter placement, which is currently empirical and physician dependent. The lack of proper catheter placement guidance during the procedure has left the physicians to rely on a heuristic thinking-while-doing technique, which may cause large catheter placement variation and increased plan quality uncertainty. Therefore, the achievable dose distribution could not be quantified prior to the catheter placement. To overcome...
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#10Xiaofeng Yang (Emory University)H-Index: 29
PURPOSE Manual delineation on all breathing phases of lung cancer 4D CT image datasets can be challenging, exhaustive, and prone to subjective errors because of both the large number of images in the datasets and variations in the spatial location of tumors secondary to respiratory motion. The purpose of this work is to present a new deep learning-based framework for fast and accurate segmentation of lung tumors on 4D CT image sets. METHODS The proposed DL framework leverages motion region convo...
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