Dr. Zahra Ghanian Profile

Dr. Zahra Ghanian

Research Fellow, PhD

SPIE Involvement:

Author

Area of Expertise:

Medical Imaging and Image Analysis , Machine/Deep Learning , Computer Aided Diagnosis , Optical Microscopy and Spectroscopy , Biophotonics , Wireless Circuit and System Design

Publications (8)

Proceedings Article | 7 April 2023 Poster + Paper

Towards appropriate use of test phantoms in training deep learning models for mammographic image conversion

Zahra Ghanian, Andreu Badal, Nicholas Petrick, Berkman Sahiner

Proceedings Volume 12463, 124634T (2023) https://doi.org/10.1117/12.2655377

KEYWORDS: Adversarial training, Mammography, Modulation transfer functions, Systems modeling, Overfitting, Target acquisition, Breast, Neural networks, Image acquisition

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SPIE Journal Paper | 19 November 2018

Computational insertion of microcalcification clusters on mammograms: reader differentiation from native clusters and computer-aided detection comparison

Zahra Ghanian, Aria Pezeshk, Nicholas Petrick, Berkman Sahiner

JMI, Vol. 5, Issue 04, 044502, (November 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.4.044502

KEYWORDS: Mammography, Computer aided diagnosis and therapy, Breast, Computer-aided diagnosis, Medical imaging, Image blending, Digital mammography, Tissues, Digital breast tomosynthesis, Image acquisition

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Mammographic computer-aided detection (CADe) devices are typically first developed and assessed for a specific “original” acquisition system. When developers are ready to apply their CADe device to a mammographic acquisition system, they typically assess the device with images acquired using the system. Collecting large repositories of clinical images containing verified lesion locations acquired by a system is costly and time consuming. We previously developed an image blending technique that allows users to seamlessly insert regions of interest (ROIs) from one medical image into another image. Our goal is to assess the performance of this technique for inserting microcalcification clusters from one mammogram into another, with the idea that when fully developed, our technique may be useful for reducing the clinical data burden in the assessment of a CADe device for use with an image acquisition system. We first perform a reader study to assess whether experienced observers can distinguish between computationally inserted and native clusters. For this purpose, we apply our insertion technique to 55 clinical cases. ROIs containing microcalcification clusters from one breast of a patient are inserted into the contralateral breast of the same patient. The analysis of the reader ratings using receiver operating characteristic (ROC) methodology indicates that inserted clusters cannot be reliably distinguished from native clusters (area under the ROC curve = 0.58 ± 0.04). Furthermore, CADe sensitivity is evaluated on mammograms of 68 clinical cases with native and inserted microcalcification clusters using a commercial CADe system. The average by-case sensitivities for native and inserted clusters are equal, 85.3% (58/68). The average by-image sensitivities for native and inserted clusters are 72.3% and 67.6%, respectively, with a difference of 4.7% and a 95% confidence interval of [−2.1 11.6]. These results demonstrate the potential for using the inserted microcalcification clusters for assessing mammographic CADe devices.

Proceedings Article | 7 March 2018 Paper

Towards the use of computationally inserted lesions for mammographic CAD assessment

Zahra Ghanian, Aria Pezeshk, Nicholas Petrick , Berkman Sahiner

Proceedings Volume 10577, 105770L (2018) https://doi.org/10.1117/12.2293800

KEYWORDS: Mammography, Computer aided diagnosis and therapy, Breast, Digital mammography, Image blending, Image processing, Computer aided design, Imaging systems, Breast cancer, Image acquisition

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Computer-aided detection (CADe) devices used for breast cancer detection on mammograms are typically first developed and assessed for a specific “original” acquisition system, e.g., a specific image detector. When CADe developers are ready to apply their CADe device to a new mammographic acquisition system, they typically assess the CADe device with images acquired using the new system. Collecting large repositories of clinical images containing verified cancer locations and acquired by the new image acquisition system is costly and time consuming. Our goal is to develop a methodology to reduce the clinical data burden in the assessment of a CADe device for use with a different image acquisition system. We are developing an image blending technique that allows users to seamlessly insert lesions imaged using an original acquisition system into normal images or regions acquired with a new system. In this study, we investigated the insertion of microcalcification clusters imaged using an original acquisition system into normal images acquired with that same system utilizing our previously-developed image blending technique. We first performed a reader study to assess whether experienced observers could distinguish between computationally inserted and native clusters. For this purpose, we applied our insertion technique to clinical cases taken from the University of South Florida Digital Database for Screening Mammography (DDSM) and the Breast Cancer Digital Repository (BCDR). Regions of interest containing microcalcification clusters from one breast of a patient were inserted into the contralateral breast of the same patient. The reader study included 55 native clusters and their 55 inserted counterparts. Analysis of the reader ratings using receiver operating characteristic (ROC) methodology indicated that inserted clusters cannot be reliably distinguished from native clusters (area under the ROC curve, AUC=0.58±0.04). Furthermore, CADe sensitivity was evaluated on mammograms with native and inserted microcalcification clusters using a commercial CADe system. For this purpose, we used full field digital mammograms (FFDMs) from 68 clinical cases, acquired at the University of Michigan Health System. The average sensitivities for native and inserted clusters were equal, 85.3% (58/68). These results demonstrate the feasibility of using the inserted microcalcification clusters for assessing mammographic CAD devices.

Proceedings Article | 16 February 2017 Paper

Time-lapse microscopy of lung endothelial cells under hypoxia

Shima Mehrvar, Zahra Ghanian, Ganesh Kondouri, Amadou Camara, Mahsa Ranji

Proceedings Volume 10068, 1006822 (2017) https://doi.org/10.1117/12.2254866

KEYWORDS: Fetus, Time lapse microscopy, Hypoxia, Oxygen, Lung, Injuries, Image segmentation

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Proceedings Article | 2 March 2015 Paper

Optical Studies of Oxidative Stress in Pulmonary Artery Endothelial Cells

Zahra Ghanian, Reyhaneh Sepehr, Annie Eis, Ganesh Kondouri, Mahsa Ranji

Proceedings Volume 9328, 932807 (2015) https://doi.org/10.1117/12.2076658

KEYWORDS: Oxygen, Injuries, Arteries, Potassium, Cyanide, Luminescence, Signal processing, Microscopy, Electron transport, Fetus

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Proceedings Article | 4 March 2014 Paper

Cytometric analysis of retinopathies in retinal trypsin digests

Zahra Ghanian, Kevin Staniszewski, Christine Sorenson, Nader Sheibani, Mahsa Ranji

Proceedings Volume 8947, 89471Y (2014) https://doi.org/10.1117/12.2036852

KEYWORDS: Retina, Capillaries, Image segmentation, Feature extraction, Luminescence, Cell death, Error analysis, Injuries, Binary data, Medicine

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Proceedings Article | 25 February 2013 Paper

Optical imaging of oxidative stress in retinitis pigmentosa (RP) in rodent model

Zahra Ghanian, Sepideh Maleki, Sandeep Gopalakrishnan, Reyhaneh Sepehr, Janis Eells, Mahsa Ranji

Proceedings Volume 8591, 85910S (2013) https://doi.org/10.1117/12.2004843

KEYWORDS: Near infrared, Retina, Eye, Tissues, Eye models, Tissue optics, Optical imaging, Animal model studies, Luminescence, Image processing

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SPIE Journal Paper | 4 January 2013 Open Access

Optical imaging of mitochondrial redox state in rodent model of retinitis pigmentosa

Sepideh Maleki, Sandeep Gopalakrishnan, Zahra Ghanian, Reyhaneh Sepehr, Heather Schmitt, Janis Eells, Mahsa Ranji

JBO, Vol. 18, Issue 01, 016004, (January 2013) https://doi.org/10.1117/12.10.1117/1.JBO.18.1.016004

KEYWORDS: Retina, Tissue optics, Tissues, Eye, Optical imaging, Oxygen, Luminescence, Eye models, In vivo imaging, 3D image processing

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