Prof. Despina Kontos Profile

Prof. Despina Kontos

Assistant Professor at Univ of Pennsylvania

SPIE Involvement:

Publications (43)

PROCEEDINGS ARTICLE | March 22, 2018

Automatic segmentation of fibroglandular tissue in breast MRI using anatomy-driven three-dimensional spatial context

Proc. SPIE. 10574, Medical Imaging 2018: Image Processing

KEYWORDS: Breast, Wavelet transforms, Breast cancer, Magnetic resonance imaging, Image segmentation

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PROCEEDINGS ARTICLE | March 2, 2018

Volumetric versus area-based density assessment: comparisons using automated quantitative measurements from a large screening cohort

Proc. SPIE. 10574, Medical Imaging 2018: Image Processing

KEYWORDS: Breast, Breast cancer, Image segmentation, Digital mammography, Digital breast tomosynthesis

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PROCEEDINGS ARTICLE | February 27, 2018

Deformable image registration as a tool to improve survival prediction after neoadjuvant chemotherapy for breast cancer: results from the ACRIN 6657/I-SPY-1 trial

Proc. SPIE. 10575, Medical Imaging 2018: Computer-Aided Diagnosis

KEYWORDS: Breast, Statistical analysis, Breast cancer, Tumors, Magnetic resonance imaging, Image segmentation, Feature extraction, Image registration, Statistical modeling, Performance modeling

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SPIE Journal Paper | January 11, 2018

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome

Davatzikos, Christos , Rathore, Saima , Bakas, Spyridon , Pati, Sarthak , Bergman, Mark , Kalarot, Ratheesh , Sridharan, Patmaa , Gastounioti, Aimilia , Jahani, Nariman , Cohen, Eric , Akbari, Hamed , Tunc, Birkan , Doshi, Jimit , Parker, Drew , Hsieh, Michael , Sotiras, Aristeidis , Li, Hongming , Ou, Yangming , Doot, Robert K. , Bilello, Michel , Fan, Yong , Shinohara, Russell T. , Yushkevich, Paul , Verma, Ragini , Kontos, Despina

JMI Vol. 5 Issue 01

KEYWORDS: Tumors, Magnetic resonance imaging, Cancer, Breast, Analytics, Diagnostics, Image segmentation, Brain, Tissues, Feature extraction

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SPIE Journal Paper | January 4, 2018

Special Section Guest Editorial: Radiomics and Deep Learning

Kontos, Despina , Summers, Ronald , Giger, Maryellen

JMI Vol. 4 Issue 04

KEYWORDS: Machine learning, Image segmentation, Magnetic resonance imaging, Medical imaging, Feature extraction, Prostate, Analytics, Convolutional neural networks, Cancer, Network architectures

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PROCEEDINGS ARTICLE | March 3, 2017

Mammographic phenotypes of breast cancer risk driven by breast anatomy

Proc. SPIE. 10134, Medical Imaging 2017: Computer-Aided Diagnosis

KEYWORDS: Breast cancer, Data modeling, Image segmentation, Volume rendering, Digital mammography, Nipple

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Image-derived features of breast parenchymal texture patterns have emerged as promising risk factors for breast cancer, paving the way towards personalized recommendations regarding women’s cancer risk evaluation and screening. The main steps to extract texture features of the breast parenchyma are the selection of regions of interest (ROIs) where texture analysis is performed, the texture feature calculation and the texture feature summarization in case of multiple ROIs. In this study, we incorporate breast anatomy in these three key steps by (a) introducing breast anatomical sampling for the definition of ROIs, (b) texture feature calculation aligned with the structure of the breast and (c) weighted texture feature summarization considering the spatial position and the underlying tissue composition of each ROI. We systematically optimize this novel framework for parenchymal tissue characterization in a case-control study with digital mammograms from 424 women. We also compare the proposed approach with a conventional methodology, not considering breast anatomy, recently shown to enhance the case-control discriminatory capacity of parenchymal texture analysis. The case-control classification performance is assessed using elastic-net regression with 5-fold cross validation, where the evaluation measure is the area under the curve (AUC) of the receiver operating characteristic. Upon optimization, the proposed breast-anatomy-driven approach demonstrated a promising case-control classification performance (AUC=0.87). In the same dataset, the performance of conventional texture characterization was found to be significantly lower (AUC=0.80, DeLong's test p-value<0.05). Our results suggest that breast anatomy may further leverage the associations of parenchymal texture features with breast cancer, and may therefore be a valuable addition in pipelines aiming to elucidate quantitative mammographic phenotypes of breast cancer risk.

PROCEEDINGS ARTICLE | March 3, 2017

Convolutional neural network approach for enhanced capture of breast parenchymal complexity patterns associated with breast cancer risk

Proc. SPIE. 10134, Medical Imaging 2017: Computer-Aided Diagnosis

KEYWORDS: Image fusion, Breast cancer, Convolutional neural networks, Receivers, Neural networks, Computer architecture, Digital mammography

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PROCEEDINGS ARTICLE | February 24, 2017

Three-dimensional whole breast segmentation in sagittal MR images with dense depth field modeling and localized self-adaptation

Proc. SPIE. 10133, Medical Imaging 2017: Image Processing

KEYWORDS: Breast, Chest, Visual process modeling, Breast cancer, Magnetic resonance imaging, Image segmentation, 3D modeling, Quantitative analysis, Mammography, 3D image processing

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SPIE Conference Volume | June 22, 2016

Medical Imaging 2016: Physics of Medical Imaging

PROCEEDINGS ARTICLE | March 24, 2016

Parenchymal texture measures weighted by breast anatomy: preliminary optimization in a case-control study

Proc. SPIE. 9785, Medical Imaging 2016: Computer-Aided Diagnosis

KEYWORDS: Breast, Breast cancer, Image segmentation, Feature extraction, Volume rendering, Mammography, Digital mammography, Nipple

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PROCEEDINGS ARTICLE | March 24, 2016

Parameter optimization of parenchymal texture analysis for prediction of false-positive recalls from screening mammography

Proc. SPIE. 9785, Medical Imaging 2016: Computer-Aided Diagnosis

KEYWORDS: Breast cancer, Tissues, Image segmentation, Feature extraction, Digital imaging, Mammography, Feature selection, Binary data, Digital mammography

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PROCEEDINGS ARTICLE | March 23, 2016

Preliminary evaluation of a fully automated quantitative framework for characterizing general breast tissue histology via color histogram and color texture analysis

Proc. SPIE. 9791, Medical Imaging 2016: Digital Pathology

KEYWORDS: Breast, Cancer, Breast cancer, Visualization, Tissues, Image segmentation, Pathology, Feature extraction, Image analysis, Collagen, Tissue optics

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SPIE Conference Volume | April 30, 2015

Medical Imaging 2015: Physics of Medical Imaging

SPIE Journal Paper | April 3, 2015

Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices

Keller, Brad , Oustimov, Andrew , Wang, Yan , Chen, Jinbo , Acciavatti, Raymond , Zheng, Yuanjie , Ray, Shonket , Gee, James , Maidment, Andrew D. , Kontos, Despina

JMI Vol. 2 Issue 02

KEYWORDS: Lawrencium, Digital mammography, Breast, Imaging systems, Breast cancer, Sensors, Computing systems, Mammography, Tissues, Image segmentation

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PROCEEDINGS ARTICLE | March 20, 2015

Quantification of tumor changes during neoadjuvant chemotherapy with longitudinal breast DCE-MRI registration

Proc. SPIE. 9414, Medical Imaging 2015: Computer-Aided Diagnosis

KEYWORDS: Breast, Oncology, Cancer, Breast cancer, Tumors, Tissues, Magnetic resonance imaging, Image registration, Medical imaging, Brain mapping

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Imaging plays a central role in the evaluation of breast tumor response to neoadjuvant chemotherapy. Image-based assessment of tumor change via deformable registration is a powerful, quantitative method potentially to explore novel information of tumor heterogeneity, structure, function, and treatment response. In this study, we continued a previous pilot study to further validate the feasibility of an open source deformable registration algorithm DRAMMS developed within our group as a means to analyze spatio-temporal tumor changes for a set of 14 patients with DCE-MR imaging. Two experienced breast imaging radiologists marked landmarks according to their anatomical meaning on image sets acquired before and during chemotherapy. Yet, chemotherapy remarkably changed the anatomical structure of both tumor and normal breast tissue, leading to significant discrepancies between both raters for landmarks in certain areas. Therefore, we proposed a novel method to grade the manually denoted landmarks into different challenge levels based on the inter-rater agreement, where a high level indicates significant discrepancies and considerable amounts of anatomical structure changes, which would indeed impose giant problem for the following registration algorithm. It is interesting to observe that DRAMMS performed in a similar manner as the human raters: landmark errors increased as inter-rater differences rose. Among all selected six deformable registration algorithms, DRAMMS achieves the highest overall accuracy, which is around 5.5 mm, while the average difference between human raters is 3 mm. Moreover, DRAMMS performed consistently well within both tumor and normal tissue regions. Lastly, we comprehensively tuned the fundamental parameters of DRAMMS to better understand DRAMMS to guide similar works in the future. Overall, we further validated that DRAMMS is a powerful registration tool to accurately quantify tumor changes and potentially predict early tumor response to chemotherapy. Therefore, future studies that aim at examining if DRAMMS can generate valuable biomarkers for tumor response prediction during chemotherapy become feasible.

PROCEEDINGS ARTICLE | March 20, 2015

A superpixel-based framework for automatic tumor segmentation on breast DCE-MRI

Proc. SPIE. 9414, Medical Imaging 2015: Computer-Aided Diagnosis

KEYWORDS: Breast, Tumor growth modeling, Cancer, Tumors, Magnetic resonance imaging, Image segmentation, Feature extraction, Medical imaging, Reconstruction algorithms, Radiology

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SPIE Conference Volume | April 11, 2014

Medical Imaging 2014: Physics of Medical Imaging

PROCEEDINGS ARTICLE | March 27, 2014

Breast density and parenchymal texture measures as potential risk factors for estrogen-receptor positive breast cancer

Proc. SPIE. 9035, Medical Imaging 2014: Computer-Aided Diagnosis

KEYWORDS: Breast, Tumor growth modeling, Cancer, Breast cancer, Tissues, Receivers, Mammography, Receptors, Statistical modeling, Performance modeling

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PROCEEDINGS ARTICLE | March 20, 2014

Application of computer-extracted breast tissue texture features in predicting false-positive recalls from screening mammography

Proc. SPIE. 9035, Medical Imaging 2014: Computer-Aided Diagnosis

KEYWORDS: Breast, Tumor growth modeling, Cancer, Breast cancer, Tissues, Image segmentation, Biopsy, Mammography, Biological research, Digital mammography

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PROCEEDINGS ARTICLE | March 20, 2014

Sparse representation of multi parametric DCE-MRI features using K-SVD for classifying gene expression based breast cancer recurrence risk

Proc. SPIE. 9035, Medical Imaging 2014: Computer-Aided Diagnosis

KEYWORDS: Breast, Genetic algorithms, Medicine, Cancer, Breast cancer, Tumors, Magnetic resonance imaging, Feature extraction, Associative arrays, Feature selection

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PROCEEDINGS ARTICLE | March 19, 2013

Testing realism of software breast phantoms: texture analysis of synthetic mammograms

Proc. SPIE. 8668, Medical Imaging 2013: Physics of Medical Imaging

KEYWORDS: Breast, Tissues, Imaging systems, X-rays, Ions, Computer simulations, Image analysis, Software development, Mammography, Breast imaging

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PROCEEDINGS ARTICLE | March 6, 2013

Dependence of radiation dose on area and volumetric mammographic breast density estimation

Proc. SPIE. 8668, Medical Imaging 2013: Physics of Medical Imaging

KEYWORDS: Breast, Statistical analysis, Breast cancer, Tissues, Imaging systems, Physics, Image quality, Mammography, X-ray imaging, Digital mammography

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PROCEEDINGS ARTICLE | March 6, 2013

Intensity standardization in breast MR images improves tissue quantification

Proc. SPIE. 8668, Medical Imaging 2013: Physics of Medical Imaging

KEYWORDS: Image processing algorithms and systems, Breast, 3D imaging standards, Tissues, Magnetic resonance imaging, Image segmentation, Image processing, Scanners, Algorithm development, 3D image processing

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PROCEEDINGS ARTICLE | February 28, 2013

A fully-automated software pipeline for integrating breast density and parenchymal texture analysis for digital mammograms: parameter optimization in a case-control breast cancer risk assessment study

Proc. SPIE. 8670, Medical Imaging 2013: Computer-Aided Diagnosis

KEYWORDS: Breast, Cancer, Breast cancer, Tissues, Image segmentation, Machine vision, Software development, Mammography, Image classification, Digital mammography

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PROCEEDINGS ARTICLE | February 28, 2013

Texture feature standardization in digital mammography for improving generalizability across devices

Proc. SPIE. 8670, Medical Imaging 2013: Computer-Aided Diagnosis

KEYWORDS: Breast, Breast cancer, Imaging systems, Sensors, Image processing, Control systems, Feature extraction, Modulation transfer functions, Spatial resolution, Digital mammography

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Growing evidence suggests a relationship between mammographic texture and breast cancer risk. For studies performing texture analysis on digital mammography (DM) images from various DM systems, it is important to evaluate if different systems could introduce inherent differences in the images analyzed and how to construct a methodological framework to identify and standardize such effects, if these differences exist. In this study, we compared two DM systems, the GE Senographe 2000D and DS using a validated physical breast phantom (Rachel, Gammex). The GE 2000D and DS systems use the same detector, but a different automated exposure control (AEC) system, resulting in differences in dose performance. On each system, images of the phantom are acquired five times in the Cranio-Caudal (CC) view with the same clinically optimized phototimer setting. Three classes of texture features, namely grey-level histogram, cooccurrence, and run-length texture features (a total of 26 features), are generated within the breast region from the raw DM images and compared between the two imaging systems. To alleviate system effects, a range of standardization steps are applied to the feature extraction process: z-score normalization is performed as the initial step to standardize image intensities, and the parameters in generating co-occurrence features are varied to decrease system differences introduced by detector blurring effects. To identify texture features robust to detectors (i.e. the ones minimally affected only by electronic noise), the distribution of each texture feature is compared between the two systems using the Kolmogorov-Smirnov (K-S) test at 0.05 significance, where features with p>0.05 are deemed robust to inherent system differences. Our approach could provide a basis for texture feature standardization across different DM imaging systems and provide a systematic methodology for selecting generalizable texture descriptors in breast cancer risk assessment.

PROCEEDINGS ARTICLE | February 26, 2013

Fully-automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI by integrating a continuous max-flow model and a likelihood atlas

Proc. SPIE. 8670, Medical Imaging 2013: Computer-Aided Diagnosis

KEYWORDS: Breast, Visualization, Tissues, Magnetic resonance imaging, Image segmentation, Fourier transforms, 3D modeling, 3D scanning, Electronic filtering, Breast imaging

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PROCEEDINGS ARTICLE | March 3, 2012

Model-based estimation of breast percent density in raw and processed full-field digital mammography images from image-acquisition physics and patient-image characteristics

Proc. SPIE. 8313, Medical Imaging 2012: Physics of Medical Imaging

KEYWORDS: Breast, Tumor growth modeling, Breast cancer, Tissues, Image processing, Image acquisition, Physics, Mammography, Model-based design, Digital mammography

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PROCEEDINGS ARTICLE | February 23, 2012

Predicting axillary lymph node metastasis from kinetic statistics of DCE-MRI breast images

Proc. SPIE. 8315, Medical Imaging 2012: Computer-Aided Diagnosis

KEYWORDS: Breast, Medicine, Statistical analysis, Breast cancer, Tumors, Magnetic resonance imaging, Radiotherapy, Image enhancement, Lymphatic system, Targeting Task Performance metric

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PROCEEDINGS ARTICLE | February 23, 2012

Fully automated chest wall line segmentation in breast MRI by using context information

Proc. SPIE. 8315, Medical Imaging 2012: Computer-Aided Diagnosis

KEYWORDS: Breast, Chest, Tissues, Magnetic resonance imaging, Image segmentation, Linear filtering, Image filtering, Image enhancement, Neodymium

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PROCEEDINGS ARTICLE | February 14, 2012

Segmentation of anatomical branching structures based on texture features and conditional random field

Proc. SPIE. 8314, Medical Imaging 2012: Image Processing

KEYWORDS: Image processing algorithms and systems, Breast, Breast cancer, Image segmentation, Error analysis, X-rays, Medical imaging, Neural networks, Image enhancement, Algorithm development

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PROCEEDINGS ARTICLE | March 9, 2011

Comparison of breast percent density estimation from raw versus processed digital mammograms

Proc. SPIE. 7963, Medical Imaging 2011: Computer-Aided Diagnosis

KEYWORDS: Breast, Medicine, Breast cancer, Data modeling, Tissues, Image segmentation, Image processing, Image analysis, Mammography, Signal detection

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PROCEEDINGS ARTICLE | March 9, 2011

Mammographic parenchymal texture as an imaging marker of hormonal activity: a comparative study between pre- and post-menopausal women

Proc. SPIE. 7963, Medical Imaging 2011: Computer-Aided Diagnosis

KEYWORDS: Breast, Tumor growth modeling, Cancer, Breast cancer, Tissues, Image segmentation, Receivers, Fractal analysis, Image classification, Performance modeling

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PROCEEDINGS ARTICLE | March 22, 2010

FFDM image quality assessment using computerized image texture analysis

Proc. SPIE. 7622, Medical Imaging 2010: Physics of Medical Imaging

KEYWORDS: Signal to noise ratio, Breast, Medicine, Imaging systems, Image segmentation, Image analysis, Image quality, Digital imaging, Fractal analysis, Digital mammography

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PROCEEDINGS ARTICLE | March 9, 2010

A comparative study of volumetric breast density estimation in digital mammography and magnetic resonance imaging: results from a high-risk population

Proc. SPIE. 7624, Medical Imaging 2010: Computer-Aided Diagnosis

KEYWORDS: Breast, Statistical analysis, Breast cancer, Tissues, Magnetic resonance imaging, Image segmentation, Image analysis, Mammography, Digital mammography, Breast imaging

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PROCEEDINGS ARTICLE | March 3, 2009

Breast tissue classification in digital breast tomosynthesis images using texture features: a feasibility study

Proc. SPIE. 7260, Medical Imaging 2009: Computer-Aided Diagnosis

KEYWORDS: Breast, Principal component analysis, Tissues, Image segmentation, Tomography, Fractal analysis, Mammography, Algorithm development, 3D image processing, Digital breast tomosynthesis

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PROCEEDINGS ARTICLE | March 18, 2008

Breast percent density estimation from 3D reconstructed digital breast tomosynthesis images

Proc. SPIE. 6913, Medical Imaging 2008: Physics of Medical Imaging

KEYWORDS: Breast, 3D image reconstruction, Breast cancer, Tissues, Image segmentation, Tomography, Mammography, X-ray imaging, 3D image processing, Digital breast tomosynthesis

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PROCEEDINGS ARTICLE | March 17, 2008

Texture in digital breast tomosynthesis: a comparison between mammographic and tomographic characterization of parenchymal properties

Proc. SPIE. 6915, Medical Imaging 2008: Computer-Aided Diagnosis

KEYWORDS: Breast, Breast cancer, Tissues, Image segmentation, Tomography, Quantization, Mammography, X-ray imaging, Digital mammography, Digital breast tomosynthesis

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PROCEEDINGS ARTICLE | March 30, 2007

Analysis of texture patterns in medical images with an application to breast imaging

Proc. SPIE. 6514, Medical Imaging 2007: Computer-Aided Diagnosis

KEYWORDS: Breast, Image compression, Databases, X-rays, Image analysis, Medical imaging, Quantization, Image retrieval, Image classification, Breast imaging

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PROCEEDINGS ARTICLE | March 30, 2007

Analysis of percent density estimates from digital breast tomosynthesis projection images

Proc. SPIE. 6514, Medical Imaging 2007: Computer-Aided Diagnosis

KEYWORDS: Breast, Cancer, Breast cancer, Tissues, X-rays, Image analysis, Collimation, Mammography, X-ray imaging, Digital breast tomosynthesis

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PROCEEDINGS ARTICLE | March 29, 2007

Analysis of parenchymal texture properties in breast tomosynthesis images

Proc. SPIE. 6514, Medical Imaging 2007: Computer-Aided Diagnosis

KEYWORDS: Breast, 3D acquisition, Cancer, Breast cancer, Visualization, X-rays, Mammography, X-ray imaging, 3D image processing, Digital breast tomosynthesis

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PROCEEDINGS ARTICLE | March 10, 2006

A representation and classification scheme for tree-like structures in medical images: an application on branching pattern analysis of ductal trees in x-ray galactograms

Proc. SPIE. 6144, Medical Imaging 2006: Image Processing

KEYWORDS: Breast, Visualization, Image segmentation, Pathology, X-rays, Computer programming, Medical imaging, Mining, Image classification, X-ray imaging

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PROCEEDINGS ARTICLE | April 12, 2005

A 3D image analysis tool for SPECT imaging

Proc. SPIE. 5744, Medical Imaging 2005: Visualization, Image-Guided Procedures, and Display

KEYWORDS: 3D image reconstruction, Visualization, Image segmentation, Image processing, Image analysis, Medical imaging, 3D metrology, Stomach, Single photon emission computed tomography, 3D image processing

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PROCEEDINGS ARTICLE | May 12, 2004

Fast and effective characterization of 3D region of interest in medical image data

Proc. SPIE. 5370, Medical Imaging 2004: Image Processing

KEYWORDS: Optical spheres, Tumors, Magnetic resonance imaging, Image processing, Feature extraction, Medical imaging, 3D image processing, Functional magnetic resonance imaging, Classification systems, Brain

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