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 200
Newest
#1Xianjin Dai (Emory University)H-Index: 9
#2Yang Lei (Emory University)H-Index: 22
Last. Xiaofeng Yang (Emory University)H-Index: 86
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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#1Yang Lei (Emory University)H-Index: 22
#2Tonghe Wang (Emory University)H-Index: 21
Last. Xiaofeng Yang (Emory University)H-Index: 86
view all 8 authors...
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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#1Shadab Momin (Emory University)H-Index: 2
#2Yang Lei (Emory University)H-Index: 22
Last. Xiaofeng Yang (Emory University)H-Index: 86
view all 10 authors...
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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#1Xianjin Dai (Emory University)H-Index: 9
#2Yang Lei (Emory University)H-Index: 22
Last. Xiaofeng Yang (Emory University)H-Index: 86
view all 10 authors...
PURPOSE Auto-segmentation algorithms offer a potential solution to eliminate the labor-intensive, time-consuming, and observer-dependent manual delineation of organs-at-risk (OARs) in radiotherapy treatment planning. This study aimed to develop a deep learning-based automated OAR delineation method to tackle the current challenges remaining in achieving reliable expert performance with the state-of-the-art auto-delineation algorithms. METHODS The accuracy of OAR delineation is expected to be imp...
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#1Mingquan Lin (Emory University)H-Index: 4
#2Shadab Momin (Emory University)H-Index: 2
Last. Xiaofeng Yang (Emory University)H-Index: 86
view all 7 authors...
PURPOSE Owing to histologic complexities of brain tumors, its diagnosis requires the use of multi-modalities to obtain valuable structural information so that brain tumor subregions can be properly delineated. In current clinical workflow, physicians typically perform slice by slice delineation of brain tumor subregions, which is a time-consuming process and also more susceptible to intra- and inter-rater variabilities possibly leading to misclassification. To deal with this issue, this study ai...
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#1Xiaofeng YangH-Index: 86
#2Yang LeiH-Index: 22
Last. Tian LiuH-Index: 31
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#1Shadab Momin (Emory University)H-Index: 2
#2Yabo Fu (Emory University)H-Index: 13
Last. Xiaofeng Yang (Emory University)H-Index: 86
view all 8 authors...
This paper surveys the data-driven dose prediction methods investigated for knowledge-based planning (KBP) in the last decade. These methods were classified into two major categories-traditional KBP methods and deep-learning (DL) methods-according to their techniques of utilizing previous knowledge. Traditional KBP methods include studies that require geometric or anatomical features to either find the best-matched case(s) from a repository of prior treatment plans or to build dose prediction mo...
1 CitationsSource
#1Mingquan Lin (Emory University)H-Index: 4
#2Jacob F. Wynne (Emory University)H-Index: 2
Last. Xiaofeng Yang (Emory University)H-Index: 86
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Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial intelligence (AI) has achieved tremendous success in medical image analysis. This paper reviews AI-based tumor subregion analysis in medical imaging. We summarize the latest AI-based methods for tumor subregion analysis and their applications. Specifically, we categorize the AI-based methods by training strategy: supervised and unsupervised. A detailed review of each category is presented, highlighting import...
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#1Xianjin Dai (Emory University)H-Index: 9
#2Yang Lei (Emory University)H-Index: 22
Last. Xiaofeng Yang (Emory University)H-Index: 86
view all 10 authors...
Purpose Ultrasound (US) imaging has been widely used in diagnosis, image-guided intervention and therapy, where high-quality three-dimensional (3D) images are highly desired from sparsely acquired two-dimensional (2D) images. This study aims to develop a deep learning-based algorithm to reconstruct high-resolution 3D US images only reliant on the acquired sparsely distributed 2D images. Methods We propose a self-supervised learning framework using cycle consistent generative adversarial network ...
1 CitationsSource
#1Shadab Momin (Emory University)H-Index: 2
#2Yang Lei (Emory University)H-Index: 22
Last. Xiaofeng Yang (Emory University)H-Index: 86
view all 10 authors...
Treatment planning for pancreatic cancer stereotactic body radiation therapy (SBRT) is very challenging owing to vast spatial variations and close proximity of many organs-at-risk. Recently, deep learning (DL)-based methods have been applied in dose prediction tasks of various treatment sites with the aim of relieving planning challenges. Limited investigations, however, have been reported on DL-based dose prediction for pancreatic cancer SBRT. This study aims to further current knowledge in DL-...
1 CitationsSource