About
61
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723
Citations
Citations since 2017
Introduction
Additional affiliations
September 2019 - August 2021
December 2018 - September 2019
June 2016 - December 2018
Position
- Researcher
Description
- Research Fellow (Jun 2016 - Jun 2017)
Education
March 2008 - August 2013
September 2006 - February 2008
March 2002 - August 2006
Publications
Publications (61)
Purpose:
To develop a radiomics prediction model to improve pulmonary nodule (PN) classification in low-dose CT. To compare the model with the American College of Radiology (ACR) Lung CT Screening Reporting and Data System (Lung-RADS) for early detection of lung cancer.
Methods:
We examined a set of 72 PNs (31 benign and 41 malignant) from the L...
Spiculations are important predictors of lung cancer malignancy, which are spikes on the surface of the pulmonary nodules. In this study, we proposed an interpretable and parameter-free technique to quantify the spiculation using area distortion metric obtained by the conformal (angle-preserving) spherical parameterization. We exploit the insight t...
Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules, are good predictors of lung cancer malignancy and hence, are routinely assessed and reported by radiologists as part of the standardized Lung-RADS clinical scoring criteria. Given the 3D geometry of the nodule and 2D slice-by-slice assessment by radiologists, manual spicu...
Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules, are good predictors of lung cancer malignancy and hence, are routinely assessed and reported by radiologists as part of the standardized Lung-RADS clinical scoring criteria. Given the 3D geometry of the nodule and 2D slice-by-slice assessment by radiologists, manual spicu...
The ability to obtain the 3D position of target vehicles is essential to managing and coordinating a multi-robot operation. We investigate an ML-backed object localization and tracking system to estimate the target’s 3D position based on a mono-camera input. The passive vision-only technique provides a robust field awareness in challenging conditio...
A 3D deep learning model (OARnet) is developed and used to delineate 28 H&N OARs on CT images. OARnet utilizes a densely connected network to detect the OAR bounding-box, then delineates the OAR within the box. It reuses information from any layer to subsequent layers and uses skip connections to combine information from different dense block level...
Motivation
Convolutional neural networks (CNNs) have achieved great success in the areas of image processing and computer vision, handling grid-structured inputs and efficiently capturing local dependencies through multiple levels of abstraction. However, a lack of interpretability remains a key barrier to the adoption of deep neural networks, part...
A 6D human pose estimation method is studied to assist autonomous UAV control in human environments. As autonomous robots/UAVs become increasingly prevalent in the future workspace, autonomous robots must detect/estimate human movement and predict their trajectory to plan a safe motion path. Our method utilize a deep Convolutional Neural Network to...
Purpose:
To quantify the error detection power of a new treatment delivery error detection method. The method validates monitor unit (MU) resolved beam apertures using real-time EPID images.
Methods:
The on-board EPID imager was used to measure cine-EPID (~10 Hz) images for 27 beams from 15 VMAT/SBRT clinical treatment plans and five nonclinical...
Detecting cancer manually in whole slide images requires significant time and effort on the laborious process. Recent advances in whole slide image analysis have stimulated the growth and development of machine learning-based approaches that improve the efficiency and effectiveness in the diagnosis of cancer diseases. In this paper, we propose an u...
Purpose:
Low-dose CT screening allows early lung cancer detection, but is affected by frequent false positive results, inter/intra observer variation and uncertain diagnoses of lung nodules. Radiomics-based models have recently been introduced to overcome these issues, but limitations in demonstrating their generalizability on independent datasets...
Purpose
Manual delineation (MD) of organs-at-risk (OAR) is time and labor-intensive. Autodelineation (AD) can reduce the need for MD, but, because current algorithms are imperfect, manual review and modification is still typically utilized. Recognizing that many OARs are sufficiently far from important dose levels that they do not pose a realistic...
We extracted image features from serial 18F-labeled fluorodeoxyglucose (FDG) positron emission tomography (PET) / computed tomography (CT) scans of anal cancer patients for the prediction of tumor recurrence after chemoradiation therapy (CRT). Seventeen patients (4 recurrent and 13 non-recurrent) underwent three PET/CT scans at baseline (Pre-CRT),...
Spiculations are spikes on the surface of pulmonary nodule and are important predictors of malignancy in lung cancer. In this work, we introduced an interpretable, parameter-free technique for quantifying this critical feature using the area distortion metric from the spherical conformal (angle-preserving) parameterization. The conformal factor in...
Quantification of local metabolic tumor volume (MTV) changes after Chemo-radiotherapy would allow accurate tumor response evaluation. Currently, local MTV changes in esophageal (soft-tissue) cancer are measured by registering follow-up PET to baseline PET using the same transformation obtained by deformable registration of follow-up CT to baseline...
Quantification of local metabolic tumor volume (MTV) chan-ges after Chemo-radiotherapy would allow accurate tumor response evaluation. Currently, local MTV changes in esophageal (soft-tissue) cancer are measured by registering follow-up PET to baseline PET using the same transformation obtained by deformable registration of follow-up CT to baseline...
Spiculations are spikes on the surface of pulmonary nodule and are important predictors of malignancy in lung cancer. In this work, we introduced an interpretable, parameter-free technique for quantifying this critical feature using the area distortion metric from the spherical conformal (angle-preserving) parameterization. The conformal factor in...
Purpose:
The purpose of this study was to evaluate the quality of automatically propagated contours of organs at risk (OARs) based on respiratory-correlated navigator-triggered four-dimensional magnetic resonance imaging (RC-4DMRI) for calculation of internal organ-at-risk volume (IRV) to account for intra-fractional OAR motion.
Methods and mater...
Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (RILD). For these features to be clinically useful, they should be robust to tumor size variations and not correlated with the normal lung volume of interest, i.e., the volume of the peri-tumoral region (PTR). CT images of 14 lung cancer patients wer...
We proposed a framework to detect and quantify local tumor morphological changes due to chemo-radiotherapy (CRT) using Jacobian map and to extract quantitative radiomic features from the Jacobian map to predict the pathologic tumor response in locally advanced esophageal cancer patients. In 20 patients who underwent CRT, a multi-resolution BSpline...
Purpose: To develop a radiomics prediction model to improve pulmonary nodule classification in low dose CT. To compare the model against the ACR Lung-RADS for early detection of lung cancer.
Methods: The Lung Image Database Consortium image collection (LIDC-IDRI) in the Cancer Imaging Archive (TCIA) was examined. We evaluated a subset of 79 pulmo...
Purpose: To predict the histopathologic subtypes with poor surgery prognosis in early stage lung adenocarcinomas using CT and PET radiomics.
Methods: We retrospectively enrolled 53 patients with stage I lung adenocarcinoma who underwent both diagnostic CT and 18F-fluorodeoxyglucose (FDG) PET/CT before complete surgical resection of the tumors. Tum...
Accurate tumor segmentation in PET is crucial in many oncology applications. We developed an adaptive region-growing (ARG) algorithm with a maximum curvature strategy (ARG_MC) for tumor segmentation in PET. The ARG_MC repeatedly applied a confidence connected region-growing algorithm with increasing relaxing factor f. The optimal relaxing factor (O...
Purpose:To develop an individually optimized contrast-enhanced (CE) 4D-computed tomography (CT) for radiotherapy simulation in pancreatic ductal adenocarcinomas (PDA).
Methods:Ten PDA patients were enrolled. Each underwent three CT scans: a 4D-CT immediately following a CE 3D-CT and an individually optimized CE 4D-CT using test injection. Three phy...
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Purpose:To develop an individually optimized contrast-enhanced (CE) 4D-CT for radiotherapy simulation in pancreatic ductal adenocarcinomas (PDA).
Methods:Ten PDA patients were enrolled. Each underwent 3 CT scans: a 4D-CT immediately following a CE 3D-CT and an individually optimized CE 4D-CT using test injection. Three physicians contoured the tumo...
Purpose:To compare linear and deformable registration methods for evaluation of tumor response to Chemoradiation therapy (CRT) in patients with esophageal cancer.
Methods:Linear and multi-resolution BSpline deformable registration were performed on Pre-Post-CRT CT/PET images of 20 patients with esophageal cancer. For both registration methods, we r...
Purpose:To develop a quantitative radiomics approach for survival prediction of glioblastoma (GBM) patients treated with chemoradiotherapy (CRT).
Methods:28 GBM patients who received CRT at our institution were retrospectively studied. 255 radiomic features were extracted from 3 gadolinium-enhanced T1 weighted MRIs for 2 regions of interest (ROIs)...
Purpose:to quantify tumor volume/shape evolution due to chemoradiotherapy using structural evolution maps computed from deformation field. To extract radiomics from these maps for tumor response assessment.
Methods:In 20 patients with esophageal cancer, BSpline deformable registration was performed to register post-treatment CT image to pre-treatme...
Purpose:Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (radiation pneumonitis and radiation fibrosis). For these features to be clinically useful, they need to be relatively invariant (robust) to tumor size and not correlated with normal lung volume.
Methods:The free-breathing CTs of 14 lung SBRT...
Purpose: To evaluate the role of mid-treatment and post-treatment FDG-PET/CT in predicting progression-free survival (PFS) and distant metastasis (DM) of anal cancer patients treated with chemoradiotherapy (CRT). Methods: 17 anal cancer patients treated with CRT were retrospectively studied. The median prescription dose was 56 Gy (range, 50–62.5 Gy...
To identify the effective quantitative image features (radiomics features) for prediction of response, survival, recurrence and metastasis of hepatocellular carcinoma (HCC) in radiotherapy.
Multiphase contrast enhanced liver CT images were acquired in 16 patients with HCC on pre and post radiation therapy (RT). In this study, arterial phase CT imag...
Purpose: To identify PET/CT based imaging predictors of anal cancer recurrence and evaluate baseline vs. mid-treatment vs. post-treatment PET/CT scans in the tumor recurrence prediction. Methods: FDG-PET/CT scans were obtained at baseline, during chemoradiotherapy (CRT, midtreatment), and after CRT (post-treatment) in 17 patients of anal cancer. Fo...
This study presents quantitative and qualitative assessment of the image qualities in contrast-enhanced (CE) 3D-CT, 4D-CT and CE 4D-CT to identify feasibility for replacing the clinical standard simulation with a single CE 4D-CT for pancreatic adenocarcinoma (PDA) in radiotherapy simulation.
Ten PDA patients were enrolled and underwent three CT sca...
A method is provided for optimizing a contrast injection function for CT imaging. The method includes injecting, with an injector pump, a test bolus of a contrast agent into a subject. The method also includes computing, on a processor, an impulse enhancement function. The method also includes determining, on a processor, a target enhancement funct...
1414Objectives Using spatial-temporal FDG-PET/CT features to assess the accuracy of advanced analytics in predicting progression-free survival (PFS) and distant metastasis (DM) of anal cancer patients treated with chemoradiotherapy (CRT).Methods 17 patients underwent FDG-PET/CT scans before and after CRT. 3 types of features were examined: 15 tradi...
Lungs nodule detection and classification is a very crucial step for computer aided diagnosis (CAD) systems. In this paper, we have proposed a CAD system that consists of multiple phases. In the first phase, Lungs segmentation has been performed. After that, region of interest ROI that contains nodule has been extracted. Different types of features...
Computer-aided detection (CAD) can help radiologists to detect pulmonary nodules at an early stage. In pulmonary nodule CAD systems, feature extraction is very important for describing the characteristics of nodule candidates. In this paper, we propose a novel three-dimensional shape-based feature descriptor to detect pulmonary nodules in CT scans....
A computer-aided detection (CAD) system is helpful for radiologists to detect pulmonary nodules at an early stage. In this paper, we propose a novel pulmonary nodule detection method based on hierarchical block classification. The proposed CAD systemconsists of three steps. In the first step, input computed tomography images are split into three-di...
Contrary to uniform local averaging, we propose non-linear approach for precise sparse depth map extraction. Noisy focus measurements are replaced with estimated values. This helps to compute accurate 3D shape while preserving edges of objects. I. INTRODUCTION Inferring depth information of the object is important in many areas like human motion es...
This paper describes a novel nodule detection method that enhances false positive reduction. Lung region is extracted from CT image sequence using adaptive thresholding and 18-connectedness voxel labelling. In the extracted lung region, nodule candidates are detected using adaptive multiple thresholding and rule based classifier. After that, we ext...
Pulmonary nodule detection is a binary classification problem. The main objective is to classify nodule from the lung computed tomography (CT) images. The intra class variability is mainly due to the grey-level variance, texture differences and shape. The purpose of this study is to develop a novel nodule detection method which is based on Two-dime...
The main purpose of pulmonary nodule detection is to classify nodule from the lung computed tomography (CT) images. The variability
of class is mainly expected to the grey-level variance, texture differences and shape. The purpose of this study is to develop
a nodule detector based on Two-dimensional Principal Component Analysis (2DPCA). We extract...
This article introduces a new algorithm for shape from focus (SFF) based on discrete cosine transform (DCT) and principal component analysis (PCA). DCT is applied on a small 3D neighborhood for each pixel in the image volume. Instead of summing all focus values in a window, AC parts of DCT are collected and then PCA is applied to transform this dat...
In this paper, we propose shape recovery method for measuring protrusions on LCD Color filter in TFT-LCD manufacturing process. We use 3-D Focus Measure operator to find focused points. Then we find the lens step that maximizes the sum of the Focus Measure. In order to reduce the computational complexity, we apply the successive focus measure updat...
Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA) are widely used in computer vision applications. In
this paper, we introduce a new SFF method based on DCT and PCA. Contrary to computing focus quality locally by summing all
values in a 2D or 3D window obtained after applying a focus measure, a vector consisting of seven neighb...
Projects
Projects (9)
Advances in radiotherapy (RT) delivery seek to improve dose conformity to increase the therapeutic ratio. A key step in this process is the delineation of treatment targets and normal tissue avoidance regions. As treatment margins around delineated volumes shrink, if delineation variability is not accounted for, disparate volumes identified by different delineators, different imaging modalities, and different structure mapping algorithms may dominate the efficacy of both an individual patients' treatment and the collective results of clinical trials.
To quantify tumor and organs at risk (OAR) morphological change using Jacobian map computed from Deformation Vector Field and to extract quantitative radiomic features from the Jacobian map for prediction of treatment response.
To develop custom-fit guide in Korean patients for total knee arthroplasty prosthesis using CT and MR images
