Dr. Sarah E. Gerard Profile

Dr. Sarah E. Gerard

at Univ of Iowa

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Publications (2)

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Proceedings Article | 29 March 2024 Poster + Paper

Modeling decision making in breast cancer screening using deep learning: usability of a Gabor convolutional layer

Karthika Kelat, Sarah Gerard, Claudia Mello-Thoms

Proceedings Volume 12929, 1292914 (2024) https://doi.org/10.1117/12.3008759

KEYWORDS: Breast cancer, Mammography, Data modeling, Eye models, Decision making, Deep learning

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This study focuses on investigating radiologists' decision-making processes in breast cancer screening, with the aim of exploring the potential of a decision prediction model trained using individual radiologists' decisions. We built decision prediction models based on the radiologists' eye position recordings and the locations where they indicated that a malignant mass was present. We considered 120 mammogram cases read by eight radiologists with different expertise levels. The decisions made were classified into three categories: True Positives (TP), False Negatives (FN), and False Positives (FP), based on the radiologists' marks and the ground truth. Notably, the data for each radiologist was used to train independent radiologist specific models. The marked areas (TPs, FPs) and the False Negative areas were cropped and fed into both base models VGG19 and ResNet50, which were pretrained with the ImageNet dataset. We enhanced both base models by incorporating a Gabor filter layer. The Gabor filter layer, implemented as a 2D convolutional layer with fixed weights, utilizes Gabor filters to extract essential Gabor features from the input. As a result, our approach yields four models tailored for decision prediction for each radiologist – VGG19 and ResNet50 each with and without Gabor filters. The models were analyzed and compared to assess their performance and potential benefits. The results underscored the significance of the radiologist's expertise and consistency in making decisions in determining the model's accuracy. When radiologists' responses are inconsistent regarding similar features across different cases, predicting the decisions using the individual models becomes challenging. Consequently, the models’ performance displayed variation based on individual radiologists’ data.

Proceedings Article | 2 March 2018 Presentation + Paper

Sensitivity analysis of Jacobian determinant used in treatment planning for lung cancer

Wei Shao, Sarah Gerard, Yue Pan, Taylor Patton, Joseph Reinhardt, Oguz Durumeric, John Bayouth, Gary Christensen

Proceedings Volume 10574, 1057418 (2018) https://doi.org/10.1117/12.2293920

KEYWORDS: Lung, Computed tomography, Error analysis, Image registration, Lung cancer, Biological research, Radiotherapy, Visualization, Tissues, Clinical trials

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Four-dimensional computed tomography (4DCT) is regularly used to visualize tumor motion in radiation therapy for lung cancer. These 4DCT images can be analyzed to estimate local ventilation by finding a dense correspondence map between the end inhalation and the end exhalation CT image volumes using deformable image registration. Lung regions with ventilation values above a threshold are labeled as regions of high pulmonary function and are avoided when possible in the radiation plan. This paper investigates a sensitivity analysis of the relative Jacobian error to small registration errors. We present a linear approximation of the relative Jacobian error. Next, we give a formula for the sensitivity of the relative Jacobian error with respect to the Jacobian of perturbation displacement field. Preliminary sensitivity analysis results are presented using 4DCT scans from 10 individuals. For each subject, we generated 6400 random smooth biologically plausible perturbation vector fields using a cubic B-spline model. We showed that the correlation between the Jacobian determinant and the Frobenius norm of the sensitivity matrix is close to -1, which implies that the relative Jacobian error in high-functional regions is less sensitive to noise. We also showed that small displacement errors on the average of 0.53 mm may lead to a 10% relative change in Jacobian determinant. We finally showed that the average relative Jacobian error and the sensitivity of the system for all subjects are positively correlated (close to +1), i.e. regions with high sensitivity has more error in Jacobian determinant on average.

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