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1. Full Text MO-A-207B-00: Segmentation & Feature Extraction Techniques
by Veeraraghavan, Harini
Medical Physics, ISSN 0094-2405, 06/2016, Volume 43, Issue 6, pp. 3694 - 3694
Image segmentation is one of the core problems for applying radiomics-based analysis to images. However, achieving repeatable and accurate segmentations for... 
Medical image quality | Image analysis | Medical magnetic resonance imaging | Computed tomography | Medical image segmentation
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2. Full Text Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images
by Duc Fehr and Harini Veeraraghavan and Andreas Wibmer and Tatsuo Gondo and Kazuhiro Matsumoto and Herbert Alberto Vargas and Evis Sala and Hedvig Hricak and Joseph O. Deasy
Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, 11/2015, Volume 112, Issue 46, pp. E6265 - E6273
Noninvasive, radiological image-based detection and stratification of Gleason patterns can impact clinical outcomes, treatment selection, and the determination... 
Gleason score classification | Learning from unbalanced data | PCa gleason 6 vs. ≥7 | Multiparametric mri | PCa gleason (3+4) vs. (4+3) cancers | CONTRAST-ENHANCED MRI | ACTIVE SURVEILLANCE | MULTIDISCIPLINARY SCIENCES | 3 T | BIOPSY | TISSUE | SELECTION BIAS | APPARENT DIFFUSION-COEFFICIENT | COMPUTER-AIDED DIAGNOSIS | DIFFERENTIATION | COMBINATION | Radiography | Magnetic Resonance Imaging | Predictive Value of Tests | Humans | Prostatic Neoplasms - diagnostic imaging | Male | Image Processing, Computer-Assisted - methods | Machine Learning | Development and progression | Care and treatment | Research | Magnetic resonance imaging | Prostate cancer | Patient outcomes | multiparametric MRI | PCa Gleason 6 vs. ≥7 | Biological Sciences | Physical Sciences | PNAS Plus | PCa Gleason (3+4) vs. (4+3) cancers | learning from unbalanced data
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3. Full Text Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores
by Wibmer, Andreas and Hricak, Hedvig and Gondo, Tatsuo and Matsumoto, Kazuhiro and Veeraraghavan, Harini and Fehr, Duc and Zheng, Junting and Goldman, Debra and Moskowitz, Chaya and Fine, Samson W and Reuter, Victor E and Eastham, James and Sala, Evis and Vargas, Hebert Alberto
European Radiology, ISSN 0938-7994, 10/2015, Volume 25, Issue 10, pp. 2840 - 2850
To investigate Haralick texture analysis of prostate MRI for cancer detection and differentiating Gleason scores (GS).One hundred and forty-seven patients... 
Adenocarcinoma | Prostatic neoplasm | Computer-assisted | Diagnostic Radiology | Image processing | Internal Medicine | Neuroradiology | Medicine & Public Health | Magnetic resonance imaging | Interventional Radiology | Gleason grading | Imaging / Radiology | Ultrasound | DIAGNOSIS | GUIDELINES | CLASSIFICATION | FEATURES | IMAGES | AGGRESSIVENESS | GRADE | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | Prostatic Neoplasms - pathology | Diagnosis, Differential | Prostatic Neoplasms - surgery | Humans | Middle Aged | Male | Image Processing, Computer-Assisted - methods | Tumor Burden | Neoplasm Grading | Diffusion Magnetic Resonance Imaging | Adult | Prostatectomy | Aged | Retrospective Studies | Prostate cancer | Magnetic Resonance Imaging | Gleason Grading | Prostatic Neoplasm | Image Processing | Computer-Assisted
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4. Full Text GBM volumetry using the 3D slicer medical image computing platform
by Egger, Jan and Kapur, Tina and Fedorov, Andriy and Pieper, Steve and Miller, James V and Veeraraghavan, Harini and Freisleben, Bernd and Golby, Alexandra J and Nimsky, Christopher and Kikinis, Ron
Scientific Reports, ISSN 2045-2322, 2013, Volume 3, Issue 1, p. 1364
Volumetric change in glioblastoma multiforme (GBM) over time is a critical factor in treatment decisions. Typically, the tumor volume is computed on a... 
MODELS | GLIOMA | MULTIDISCIPLINARY SCIENCES | GROWTH | ALGORITHMS | LEVEL | INTERACTIVE SEGMENTATION | TUMOR VOLUME | Magnetic Resonance Imaging | Glioblastoma - diagnosis | Image Processing, Computer-Assisted | Glioblastoma - pathology | Humans | Tumor Burden | Imaging, Three-Dimensional | Image processing | Magnetic resonance imaging | Segmentation | Glioblastoma multiforme | Glioblastoma | Computer Science - Computer Vision and Pattern Recognition
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5. Full Text Breast cancer subtype intertumor heterogeneity: MRI‐based features predict results of a genomic assay
by Sutton, Elizabeth J and Oh, Jung Hun and Dashevsky, Brittany Z and Veeraraghavan, Harini and Apte, Aditya P and Thakur, Sunitha B and Deasy, Joseph O and Morris, Elizabeth A
Journal of Magnetic Resonance Imaging, ISSN 1053-1807, 11/2015, Volume 42, Issue 5, pp. 1398 - 1406
Purpose To investigate the association between a validated, gene‐expression‐based, aggressiveness assay, Oncotype Dx RS, and morphological and texture‐based... 
invasive ductal carcinoma | breast cancer subtypes | genotype | DIAGNOSIS | TAMOXIFEN | TEXTURE ANALYSIS | RISK | TUMORS | LESIONS | MOLECULAR PORTRAITS | DX(TM) RECURRENCE-SCORE | NEOADJUVANT CHEMOTHERAPY | GENE-EXPRESSION | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | Carcinoma, Ductal, Breast - genetics | Gene Expression - genetics | Image Enhancement | Humans | Middle Aged | Magnetic Resonance Imaging - methods | Breast Neoplasms - genetics | Contrast Media | Image Processing, Computer-Assisted | Breast Neoplasms - pathology | Adult | Carcinoma, Ductal, Breast - pathology | Female | Aged | Retrospective Studies | Genomics - methods | Breast - pathology | Cohort Studies | Gadolinium DTPA | Genetic research | Genetic aspects | Breast cancer | Cancer | Nuclear magnetic resonance--NMR | Regression analysis | Morphology
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6. Full Text Breast cancer molecular subtype classifier that incorporates MRI features
by Sutton, Elizabeth J and Dashevsky, Brittany Z and Oh, Jung Hun and Veeraraghavan, Harini and Apte, Aditya P and Thakur, Sunitha B and Morris, Elizabeth A and Deasy, Joseph O
Journal of Magnetic Resonance Imaging, ISSN 1053-1807, 07/2016, Volume 44, Issue 1, pp. 122 - 129
Purpose To use features extracted from magnetic resonance (MR) images and a machine‐learning method to assist in differentiating breast cancer molecular... 
MRI texture | molecular subtypes | breast cancer | machine‐learning | Molecular subtypes | Breast cancer | Machine-learning | machine-learning | DIAGNOSIS | IMAGES | RESONANCE-IMAGING FEATURES | PATHOLOGICAL FINDINGS | PHENOTYPES | TUMORS | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | LESIONS | COMPUTER-AIDED DETECTION | Breast Neoplasms - classification | Diagnosis, Differential | Reproducibility of Results | Humans | Image Interpretation, Computer-Assisted - methods | Middle Aged | Magnetic Resonance Imaging - methods | Triple Negative Breast Neoplasms - diagnostic imaging | Breast Neoplasms - metabolism | Algorithms | Sensitivity and Specificity | Biomarkers, Tumor - metabolism | Adult | Female | Aged | Image Enhancement - methods | Breast Neoplasms - diagnostic imaging | Observer Variation | Rankings | Healthcare industry software | Magnetic resonance imaging | Health insurance industry | Machine learning | Accuracy
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7. Full Text Multiple Resolution Residually Connected Feature Streams for Automatic Lung Tumor Segmentation From CT Images
by Jiang, Jue and Hu, Yu-Chi and Liu, Chia-Ju and Halpenny, Darragh and Hellmann, Matthew D and Deasy, Joseph O and Mageras, Gig and Veeraraghavan, Harini
IEEE Transactions on Medical Imaging, ISSN 0278-0062, 01/2019, Volume 38, Issue 1, pp. 134 - 144
Volumetric lung tumor segmentation and accurate longitudinal tracking of tumor volume changes from computed tomography images are essential for monitoring... 
Deep learning | lung cancer | Image segmentation | Image resolution | detection | Lung | Streaming media | Feature extraction | segmentation | longitudinal | Tumors | Cancer | NODULES | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | ENGINEERING, BIOMEDICAL | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | ENGINEERING, ELECTRICAL & ELECTRONIC | Formulations | Automation | Sensitivity analysis | Medical imaging | Segmentation | Image processing | PD-1 protein | Lung nodules | Image detection | Nodules | Consortia | Datasets | Computed tomography | Computation | Immunotherapy | Data sets | Computer aided tomography | Longitudinal | Detection | Lung cancer
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8. Full Text Appearance Constrained Semi-Automatic Segmentation from DCE-MRI is Reproducible and Feasible for Breast Cancer Radiomics: A Feasibility Study
by Veeraraghavan, Harini and Dashevsky, Brittany Z and Onishi, Natsuko and Sadinski, Meredith and Morris, Elizabeth and Deasy, Joseph O and Sutton, Elizabeth J
Scientific Reports, ISSN 2045-2322, 12/2018, Volume 8, Issue 1, pp. 4838 - 11
We present a segmentation approach that combines GrowCut (GC) with cancer-specific multi-parametric Gaussian Mixture Model (GCGMM) to produce accurate and... 
NEOADJUVANT CHEMOTHERAPY | IMAGES | MULTIDISCIPLINARY SCIENCES | TEXTURE ANALYSIS | CLASSIFICATION | LESIONS | PREDICTION | Image processing | Magnetic resonance imaging | Segmentation | Invasiveness | Feasibility studies | Breast cancer | ErbB-2 protein
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9. Full Text Dynamic multiatlas selection‐based consensus segmentation of head and neck structures from CT images
by Haq, Rabia and Berry, Sean L and Deasy, Joseph O and Hunt, Margie and Veeraraghavan, Harini
Medical Physics, ISSN 0094-2405, 12/2019, Volume 46, Issue 12, pp. 5612 - 5622
Purpose Manual delineation of head and neck (H&N) organ‐at‐risk (OAR) structures for radiation therapy planning is time consuming and highly variable.... 
head and neck | atlas segmentation | computed tomography images
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10. Full Text Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis
by Lakhman, Yulia and Veeraraghavan, Harini and Chaim, Joshua and Feier, Diana and Goldman, Debra A and Moskowitz, Chaya S and Nougaret, Stephanie and Sosa, Ramon E and Vargas, Hebert Alberto and Soslow, Robert A and Abu-Rustum, Nadeem R and Hricak, Hedvig and Sala, Evis
European Radiology, ISSN 0938-7994, 07/2017, Volume 27, Issue 7, pp. 2903 - 2915
To investigate whether qualitative magnetic resonance (MR) features can distinguish leiomyosarcoma (LMS) from atypical leiomyoma (ALM) and assess the... 
Magnetic Resonance Imaging | Atypical Uterine Leiomyoma | Uterine Leiomyoma | Texture Analysis | Uterine Leiomyosarcoma | MANAGEMENT | SPATIAL-FREQUENCY | RESOLUTION | CLASSIFICATION | CLINICAL PRESENTATION | BENIGN | ANGIOMYOLIPOMA | SARCOMAS | SMOOTH-MUSCLE TUMORS | RENAL-CELL-CARCINOMA | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | Diagnosis, Differential | Reproducibility of Results | Uterine Neoplasms - pathology | Humans | Middle Aged | Magnetic Resonance Imaging - methods | Feasibility Studies | Leiomyoma - pathology | Young Adult | Adolescent | Aged, 80 and over | Adult | Female | Aged | Retrospective Studies | Leiomyosarcoma - diagnosis | Leiomyoma | Diagnosis | Magnetic resonance imaging | Leiomyosarcoma | Automation | Segmentation | Image processing | Feasibility studies | Clustering | Statistics | Patients | Hemorrhage | Texture | Readers | Image segmentation | Sensitivity | Accuracy | Uterus | Mathematical analysis | Surgery | Classification | Qualitative analysis | Resonance | Diagnostic systems | Differentiation | Borders | Surface layers | Life Sciences | Retrospective studies | Reproducibility of results | Uterine Neoplasms/pathology | Cancer
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11. Full Text A novel representation of inter-site tumour heterogeneity from pre-treatment computed tomography textures classifies ovarian cancers by clinical outcome
by Vargas, Hebert Alberto and Veeraraghavan, Harini and Micco, Maura and Nougaret, Stephanie and Lakhman, Yulia and Meier, Andreas A and Sosa, Ramon and Soslow, Robert A and Levine, Douglas A and Weigelt, Britta and Aghajanian, Carol and Hricak, Hedvig and Deasy, Joseph and Snyder, Alexandra and Sala, Evis
European Radiology, ISSN 0938-7994, 9/2017, Volume 27, Issue 9, pp. 3991 - 4001
To evaluate the associations between clinical outcomes and radiomics-derived inter-site spatial heterogeneity metrics across multiple metastatic lesions on CT... 
Medicine & Public Health | Diagnostic Radiology | Internal Medicine | Interventional Radiology | Imaging / Radiology | Ultrasound | Radiogenomics | Texture | Survival | Neuroradiology | Ovarian cancer | Radiomics | THERAPY | SUBTYPES | RESISTANCE | CARCINOMA | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | RELEVANT GENE SIGNATURES | FEATURES | Oncogene Proteins - genetics | Humans | Middle Aged | Ovarian Neoplasms - pathology | Tomography, X-Ray Computed - methods | Treatment Outcome | Ovarian Neoplasms - mortality | Cyclin E - genetics | Nucleic Acid Amplification Techniques | Ovarian Neoplasms - genetics | Adult | Female | Aged | Gene Amplification - genetics | Retrospective Studies | Medicine, Experimental | Medical research | CT imaging | Metastasis | Patient outcomes | Similarity | Ovarian carcinoma | Spatial discrimination | Entropy | Patients | Clinical outcomes | Metastases | Shade | Heterogeneity | Analogies | Computed tomography | Computation | Correlation analysis | Quality | Surgery | E1 gene | Clusters | Spatial heterogeneity | Pretreatment | Lesions | Tumors | Life Sciences | Treatment outcome | Middle aged | Ovarian neoplasms | Oncogene proteins | Cancer | radiomics | radiogenomics | ovarian cancer | texture | survival
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12. Full Text Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI‐radiomic features
by Tixier, Florent and Um, Hyemin and Young, Robert J and Veeraraghavan, Harini
Medical Physics, ISSN 0094-2405, 08/2019, Volume 46, Issue 8, pp. 3582 - 3591
Purpose The use of radiomic features as biomarkers of treatment response and outcome or as correlates to genomic variations requires that the computed features... 
robustness | radiomics | segmentation | glioblastoma | MRI | INFORMATION | TEXTURE FEATURES | MULTIMODAL MRI | MODEL | PREDICTION | IMAGES | CHEMORADIATION | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | PET
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13. Full Text Patch‐Based Generative Adversarial Neural Network Models for Head and Neck MR‐Only Planning
by Klages, Peter and Benslimane, Ilyes and Riyahi, Sadegh and Jiang, Jue and Hunt, Margie and Deasy, Joseph O and Veeraraghavan, Harini and Tyagi, Neelam
Medical Physics, ISSN 0094-2405, 11/2019
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14. Full Text Vision 20/20: Perspectives on automated image segmentation for radiotherapy
by Sharp, Gregory and Fritscher, Karl D and Pekar, Vladimir and Peroni, Marta and Shusharina, Nadya and Veeraraghavan, Harini and Yang, Jinzhong
Medical Physics, ISSN 0094-2405, 05/2014, Volume 41, Issue 5, pp. 050902 - n/a
Due to rapid advances in radiation therapy (RT), especially image guidance and treatment adaptation, a fast and accurate segmentation of medical images is a... 
biological organs | image processing | Medical imaging | Segmentation | Medical image segmentation | Anatomy | Medical image contrast | Digital computing or data processing equipment or methods, specially adapted for specific applications | Radiation therapy equipment | Databases | Computed tomography | Magnetic resonance imaging | image segmentation | Image data processing or generation, in general | radiation therapy | Dosimetry | medical image processing | Cancer | segmentation | ATLAS-BASED SEGMENTATION | AUTO-SEGMENTATION | CLINICAL TARGET VOLUME | INTENSITY-MODULATED RADIOTHERAPY | ACTIVE SHAPE MODELS | CELL LUNG-CANCER | HEAD-AND-NECK | NECK CT IMAGES | LYMPH-NODE REGIONS | COMPUTED-TOMOGRAPHY IMAGES | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | Magnetic Resonance Imaging - instrumentation | Radiotherapy, Computer-Assisted - instrumentation | Artificial Intelligence | Humans | Radiotherapy, Computer-Assisted - methods | Image Processing, Computer-Assisted - instrumentation | Magnetic Resonance Imaging - methods | Tomography, X-Ray Computed - methods | Software | Image Processing, Computer-Assisted - methods | Pattern Recognition, Automated - methods | Tomography, X-Ray Computed - instrumentation | REVIEWS | HEALTH HAZARDS | IMAGES | RADIOLOGY AND NUCLEAR MEDICINE | PATHOLOGY | RADIOTHERAPY | PLANNING | Vision 20
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