Dr. Jia Wu Profile

Dr. Jia Wu

at Univ of Pennsylvania

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Author

Publications (2)

Proceedings Article | 20 March 2015 Paper

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

Ning Yu, Jia Wu, Susan Weinstein, Bilwaj Gaonkar, Brad Keller, Ahmed Ashraf, YunQing Jiang, Christos Davatzikos, Emily Conant, Despina Kontos

Proceedings Volume 9414, 94140O (2015) https://doi.org/10.1117/12.2081943

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

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Proceedings Article | 20 March 2015 Paper

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

Jia Wu, Yangming Ou, Susan Weinstein, Emily Conant, Ning Yu, Vahid Hoshmand, Brad Keller, Ahmed Ashraf, Mark Rosen, Angela DeMichele, Christos Davatzikos, Despina Kontos

Proceedings Volume 9414, 94141Z (2015) https://doi.org/10.1117/12.2081938

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

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