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
Multi-needle localization in ultrasound (US) images is a crucial step of treatment planning for US-guided prostate brachytherapy. However, current computer-aided technologies are mostly focused on single-needle digitization, while manual digitization is labor intensive and time consuming. In this paper, we proposed a deep learning-based workflow for fast automatic multi-needle digitization, including needle shaft detection and needle tip detection. The major workflow is composed of two component...
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#10Xiaofeng Yang (Emory University)H-Index: 29
Purpose: Organ-at-risk (OAR) delineation is a key step for cone-beam CT (CBCT) based adaptive radiotherapy planning that can be a time-consuming, labor-intensive, and subject-to-variability process. We aim to develop a fully automated approach aided by synthetic MRI for rapid and accurate CBCT multi-organ contouring in head-and-neck (HN) cancer patients. MRI has superb soft-tissue contrasts, while CBCT offers bony-structure contrasts. Using the complementary information provided by MRI and CBCT ...
The segmentation of neoplasms is an important part of radiotherapy treatment planning, monitoring disease progression, and predicting patient outcome. In the brain, functional magnetic resonance imaging (MRI) like dynamic susceptibility contrast enhanced (DSCE) or T1-weighted dynamic contrast enhanced (DCE) perfusion MRI are important tools for diagnosis. They play a crucial role in providing pre-operative assessment of tumor histology, grading, and tumor biopsy guidance. However, the manual con...
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#10Xiaofeng Yang (Emory University)H-Index: 29
PURPOSE Segmentation of organs-at-risk (OARs) is a weak link in radiotherapeutic treatment planning process because the manual contouring action is labor-intensive and time-consuming. This work aimed to develop a deep learning-based method for rapid and accurate pancreatic multi-organ segmentation that can expedite the treatment planning process. METHODS We retrospectively investigated one hundred patients with CT simulation scanned and contours delineated. Eight OARs including large bowel, smal...
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PURPOSE Segmentation of organs-at-risk (OARs) is a weak link in radiotherapy treatment planning process because the manual contouring process is labor-intensive and time-consuming. This work aimed to develop a synthetic MR (sMR)-aided dual pyramid network (DPN) for rapid and accurate head and neck multi-organ segmentation that can expedite the treatment planning process. METHODS Forty-five patients' CT, MR and manual contours pairs were included as training dataset. Nineteen OARs were target org...
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#10Xiaofeng Yang (Emory University)H-Index: 29
PURPOSE In intensity-modulated proton therapy (IMPT) protons are used to deliver highly conformal dose distributions, targeting tumors and sparing organs-at-risk. However, due to uncertainties in both patient setup and relative stopping power (RSP) calculation, margins are added to the treatment volume during treatment planning, leading to higher doses to normal tissues. Cone-beam CT (CBCT) images are taken daily before treatment, however the poor image quality of CBCT limits the use of these im...
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#10Xiaofeng Yang (Emory University)H-Index: 29
PURPOSE High-dose-rate (HDR) brachytherapy is an established technique to be used as monotherapy option or focal boost in conjunction with external beam radiation therapy (EBRT) for treating prostate cancer. Radiation source path reconstruction is a critical procedure in HDR treatment planning. Manually identifying the source path is labor intensive and timely inefficient. Recent years, magnetic resonance imaging (MRI) becomes a valuable imaging modality for image-guided HDR prostate brachythera...
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#7Xiaofeng Yang (Emory University)H-Index: 29
Abstract The rapid expansion of machine learning is offering a new wave of opportunities for nuclear medicine. This paper reviews applications of machine learning for the study of attenuation correction (AC) and low-count image reconstruction in quantitative positron emission tomography (PET). Specifically, we present the developments of machine learning methodology, ranging from random forest and dictionary learning to the latest convolutional neural network-based architectures. For application...
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#10Xiaofeng Yang (Emory University)H-Index: 29
PURPOSE: Ultrasound (US)-guided high-dose-rate (HDR) prostate brachytherapy requests the clinicians to place HDR needles (catheters) into the prostate gland under transrectal US (TRUS) guidance in the operating room. The quality of the subsequent radiation treatment plan is largely dictated by the needle placements, which varies upon the experience level of the clinicians and the procedure protocols. Real-time plan dose distribution, if available, could be a vital tool to provide more subjective...
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PURPOSE: Registration and fusion of magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) of prostate can provide guidance for prostate brachytherapy. However, accurate registration remains a challenging task due to the lack of ground-truth regarding voxel-level spatial correspondence, limited field of view, low contrast-to-noise ratio in TRUS. In this study, we proposed a weakly supervised deep learning approach to address these issues. METHODS: We employed deep learning techniques...
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