Dr. Deepa Sheth Profile

Dr. Deepa Sheth

at Univ of Chicago

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Publications (6)

SPIE Journal Paper | 2 May 2024 Open Access

Computerized assessment of background parenchymal enhancement on breast dynamic contrast-enhanced-MRI including electronic lesion removal

Lindsay Douglas, Jordan Fuhrman, Qiyuan Hu, Alexandra Edwards, Deepa Sheth, Hiroyuki Abe, Maryellen Giger

JMI, Vol. 11, Issue 03, 034501, (May 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.3.034501

KEYWORDS: Breast, Image segmentation, Education and training, Visualization, Magnetic resonance imaging, Tumors, Statistical analysis, Binary data, Image classification, Breast cancer

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PurposeCurrent clinical assessment qualitatively describes background parenchymal enhancement (BPE) as minimal, mild, moderate, or marked based on the visually perceived volume and intensity of enhancement in normal fibroglandular breast tissue in dynamic contrast-enhanced (DCE)-MRI. Tumor enhancement may be included within the visual assessment of BPE, thus inflating BPE estimation due to angiogenesis within the tumor. Using a dataset of 426 MRIs, we developed an automated method to segment breasts, electronically remove lesions, and calculate scores to estimate BPE levels.ApproachA U-Net was trained for breast segmentation from DCE-MRI maximum intensity projection (MIP) images. Fuzzy c-means clustering was used to segment lesions; the lesion volume was removed prior to creating projections. U-Net outputs were applied to create projection images of both, affected, and unaffected breasts before and after lesion removal. BPE scores were calculated from various projection images, including MIPs or average intensity projections of first- or second postcontrast subtraction MRIs, to evaluate the effect of varying image parameters on automatic BPE assessment. Receiver operating characteristic analysis was performed to determine the predictive value of computed scores in BPE level classification tasks relative to radiologist ratings.ResultsStatistically significant trends were found between radiologist BPE ratings and calculated BPE scores for all breast regions (Kendall correlation, p<0.001). Scores from all breast regions performed significantly better than guessing (p<0.025 from the z-test). Results failed to show a statistically significant difference in performance with and without lesion removal. BPE scores of the affected breast in the second postcontrast subtraction MIP after lesion removal performed statistically greater than random guessing across various viewing projections and DCE time points.ConclusionsResults demonstrate the potential for automatic BPE scoring to serve as a quantitative value for objective BPE level classification from breast DCE-MR without the influence of lesion enhancement.

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SPIE Journal Paper | 20 November 2023 Open Access

U-Net breast lesion segmentations for breast dynamic contrast-enhanced magnetic resonance imaging

Lindsay Douglas, Roma Bhattacharjee, Jordan Fuhrman, Karen Drukker, Qiyuan Hu, Alexandra Edwards, Deepa Sheth, Maryellen Giger

JMI, Vol. 10, Issue 06, 064502, (November 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.6.064502

KEYWORDS: Image segmentation, Breast, 3D image processing, 3D imaging standards, Magnetic resonance imaging, Education and training, Cross validation, 3D modeling, 3D image enhancement, Artificial intelligence

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Proceedings Article | 15 February 2021 Presentation + Paper

Comparison of 2D and 3D U-Net breast lesion segmentations on DCE-MRI

Roma Bhattacharjee, Lindsay Douglas, Karen Drukker, Qiyuan Hu, Jordan Fuhrman, Deepa Sheth, Maryellen Giger

Proceedings Volume 11597, 115970D (2021) https://doi.org/10.1117/12.2581846

KEYWORDS: Image segmentation, Breast, Magnetic resonance imaging, Image processing algorithms and systems, Feature extraction, Volume rendering, Medical imaging, Fuzzy logic, Convolutional neural networks, Computer aided diagnosis and therapy

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Proceedings Article | 15 February 2021 Presentation + Paper

Electronic removal of lesions for more robust BPE scoring on breast DCE-MRI

Lindsay Douglas, Deepa Sheth, Maryellen Giger

Proceedings Volume 11597, 115970C (2021) https://doi.org/10.1117/12.2581872

KEYWORDS: Breast, Image segmentation, Tumors, Statistical analysis, Receivers, Magnetic resonance imaging, Binary data

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Proceedings Article | 6 July 2018 Paper

Deep learning in computer-aided diagnosis incorporating mammographic characteristics of both tumor and parenchyma stroma

Hui Li, Deepa Sheth, Kayla Mendel, Li Lan, Maryellen Giger

Proceedings Volume 10718, 1071806 (2018) https://doi.org/10.1117/12.2318282

KEYWORDS: Tumors, Computer aided diagnosis and therapy, Breast, Feature extraction, Breast cancer, Mammography, Statistical analysis, Medical imaging, Image classification

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Proceedings Article | 27 February 2018 Presentation + Paper

Transfer learning with convolutional neural networks for lesion classification on clinical breast tomosynthesis

Kayla Mendel, Hui Li, Deepa Sheth, Maryellen Giger

Proceedings Volume 10575, 105750T (2018) https://doi.org/10.1117/12.2294973

KEYWORDS: Digital breast tomosynthesis, Breast, Convolutional neural networks, Computer aided diagnosis and therapy, Feature extraction, Breast cancer, Digital mammography, Image classification, Databases

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