Shadab Momin
Emory University
Similarity (geometry)Deep learningCorrelationMachine learningFrame (networking)Histogram matchingMean squared errorHausdorff distanceMagnetic resonance imagingGround truthArtificial intelligenceImage registrationPattern recognitionTracking errorKey (cryptography)Radiation treatment planningCross-validationPelvisBrain tumorContext (language use)3D ultrasoundLandmarkData-drivenPelvic MRIMultiparametric MRIMultiparametric Magnetic Resonance ImagingBrain tumor segmentationRadiation doseDeformation vectorStereotactic body radiotherapyDose predictionPrior treatmentKnowledge based planningKey featuresFully automatedComputer visionComputer scienceTracking (particle physics)Predictive modellingArtificial neural networkRadiation therapyFeature (computer vision)Pyramid (image processing)Convolutional neural networkSegmentationSimilarity (network science)Match moving
9Publications
4H-index
2Citations
Publications 9
Newest
#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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#3Yang Lei (Emory University)H-Index: 8
#8Xiaofeng Yang (Emory University)H-Index: 29
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...
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#7Xiaofeng Yang (Emory University)H-Index: 29
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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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-...
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#9Xiaofeng Yang (Emory University)H-Index: 29
An accurate and robust image registration of computed tomography (CT) and magnetic resonance imaging (MRI) plays an important role in establishing a desired radiation treatment plan. Traditional image similarity measures such as cross-correlation, mean absolute error, mean squared error have very limited success in multi modal MRI-CT image registration. In this study, we propose a deformable registration method based on unsupervised deep neural networks to register MRI and CT for pelvic patients...
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#8Xiaofeng Yang (Emory University)H-Index: 29
Gliomas are very heterogenous set of tumors that grow within the substance of brain and often mix with normal brain tissues. Due to its histologic complexity and irregular shapes, multiparametric magnetic resonance imaging is used to accurately diagnose brain tumor and their subregions. Current practice requires physicians to manually segment these regions on a large image dataset, which can be a very time consuming and complicated task especially with large variations among different tumor regi...
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#9Xiaofeng Yang (Emory University)H-Index: 29
In this study, we propose a novel unsupervised deep learning-based method to register pelvic MRI and CBCT images. No ground truth deformation vector field (DVF) is needed during training. To perform registration between CBCT and MRI, a self-similarity image similarity loss, called as self-correlation descriptor, is used as loss function to learn the trainable parameters in the unsupervised deep neural networks. After training, for a new patient’s CBCT and MRI, the deformed MRI is obtained via fi...
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#9Xiaofeng Yang (Emory University)H-Index: 29
In this study, we propose a novel deep learning-based method to predict dose distribution based on patient’s pancreas planning CT and organ contours. To comprehensively extract features from CT and contours, dual pyramid networks (DPNs) with late fusion network (LFN) are used. The proposed network consists of three subnetworks, i.e., CT-only feature pyramid network (FPN), contour-only feature pyramid network (FPN) and late fusion network (LFN). CT-only FPN and contour-only FPN are proposed to le...
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#7Xiaofeng Yang (Emory University)H-Index: 29
Ultrasound imaging during or prior to radiation therapy offers a great potential in terms of safety, cost and real time imaging capacity. However, this task is challenging for tumors of abdominal such as liver cancer due to respiratory motion. In this work, we proposed an unsupervised deep-learning-based method to track the respiratory motion for 3D ultrasound (US) liver imaging. A Markov-like network, which extract features from consistent 3D US frames, was utilized to estimate a sequence of de...
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