Dr. Hannah L. Gilmore Profile

Dr. Hannah L. Gilmore

SPIE Involvement:

Author

Area of Expertise:

Breast Pathology

I am the Director of Surgical Pathology at University Hospitals Case Medical Center and lead the breast pathology subspecialty service. As a pathologist and member of the Case Comprehensive Cancer, I have been heavily involved in the acquisition and use of human tissues for research purposes for breast as well as other organ systems. My current academic interests include both clinical and translational cancer research where I study biomarker expression and genomic profiling of the intrinsic biologic subtypes of invasive breast cancer, epithelial-mesenchymal transition and mediators of metastasis. I have a particular interest in digital imaging and the integration of pathologic, radiologic and genomic data to study the biology of cancer.

SPIE Journal Paper | 13 July 2021 Open Access

Computational pathology reveals unique spatial patterns of immune response in H&E images from COVID-19 autopsies: preliminary findings

Paula Toro, Kaustav Bera, Pingfu Fu, Lisa Barton, Hannah Gilmore, Sanjay Mukhopadhyay, Anant Madabhushi, Germán Corredor, Dylan Rasmussen, Vidya Sankar Viswanathan, Christina Buzzy, Edana Stroberg, Eric Duval

JMI, Vol. 8, Issue S1, 017501, (July 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.S1.017501

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SPIE Journal Paper | 8 February 2019

Convolutional neural network initialized active contour model with adaptive ellipse fitting for nuclear segmentation on breast histopathological images

Jun Xu, Lei Gong, Guanhao Wang, Cheng Lu, Hannah Gilmore, Shaoting Zhang, Anant Madabhushi

JMI, Vol. 6, Issue 01, 017501, (February 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.1.017501

KEYWORDS: Image segmentation, Convolutional neural networks, Performance modeling, Data modeling, Breast, Breast cancer, Sensors, Cancer, Tissues, Computing systems

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Automated detection and segmentation of nuclei from high-resolution histopathological images is a challenging problem owing to the size and complexity of digitized histopathologic images. In the context of breast cancer, the modified Bloom–Richardson Grading system is highly correlated with the morphological and topological nuclear features are highly correlated with Modified Bloom–Richardson grading. Therefore, to develop a computer-aided prognosis system, automated detection and segmentation of nuclei are critical prerequisite steps. We present a method for automated detection and segmentation of breast cancer nuclei named a convolutional neural network initialized active contour model with adaptive ellipse fitting (CoNNACaeF). The CoNNACaeF model is able to detect and segment nuclei simultaneously, which consist of three different modules: convolutional neural network (CNN) for accurate nuclei detection, (2) region-based active contour (RAC) model for subsequent nuclear segmentation based on the initial CNN-based detection of nuclear patches, and (3) adaptive ellipse fitting for overlapping solution of clumped nuclear regions. The performance of the CoNNACaeF model is evaluated on three different breast histological data sets, comprising a total of 257 H&E-stained images. The model is shown to have improved detection accuracy of F-measure 80.18%, 85.71%, and 80.36% and average area under precision-recall curves (AveP) 77%, 82%, and 74% on a total of 3 million nuclei from 204 whole slide images from three different datasets. Additionally, CoNNACaeF yielded an F-measure at 74.01% and 85.36%, respectively, for two different breast cancer datasets. The CoNNACaeF model also outperformed the three other state-of-the-art nuclear detection and segmentation approaches, which are blue ratio initialized local region active contour, iterative radial voting initialized local region active contour, and maximally stable extremal region initialized local region active contour models.

Proceedings Article | 23 March 2016 Paper

Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+ breast cancer whole slide images

David Romo-Bucheli, Andrew Janowczyk, Eduardo Romero, Hannah Gilmore, Anant Madabhushi

Proceedings Volume 9791, 979106 (2016) https://doi.org/10.1117/12.2211368

KEYWORDS: Binary data, Breast cancer, Cancer, Tissues, Neural networks, Oncology, Tumors, Receptors, Precision measurement, Detection and tracking algorithms

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Early stage estrogen receptor positive (ER+) breast cancer (BCa) treatment is based on the presumed aggressiveness and likelihood of cancer recurrence. The primary conundrum in treatment and management of early stage ER+ BCa is identifying which of these cancers are candidates for adjuvant chemotherapy and which patients will respond to hormonal therapy alone. This decision could spare some patients the inherent toxicity associated with adjuvant chemotherapy. Oncotype DX (ODX) and other gene expression tests have allowed for distinguishing the more aggressive ER+ BCa requiring adjuvant chemotherapy from the less aggressive cancers benefiting from hormonal therapy alone. However these gene expression tests tend to be expensive, tissue destructive and require physical shipping of tissue blocks for the test to be done. Interestingly breast cancer grade in these tumors has been shown to be highly correlated with the ODX risk score. Unfortunately studies have shown that Bloom-Richardson (BR) grade determined by pathologists can be highly variable. One of the constituent categories in BR grading is the quantification of tubules. The goal of this study was to develop a deep learning neural network classifier to automatically identify tubule nuclei from whole slide images (WSI) of ER+ BCa, the hypothesis being that the ratio of tubule nuclei to overall number of nuclei would correlate with the corresponding ODX risk categories. The performance of the tubule nuclei deep learning strategy was evaluated with a set of 61 high power fields. Under a 5-fold cross-validation, the average precision and recall measures were 0:72 and 0:56 respectively. In addition, the correlation with the ODX risk score was assessed in a set of 7513 high power fields extracted from 174 WSI, each from a different patient (At most 50 high power fields per patient study were used). The ratio between the number of tubule and non-tubule nuclei was computed for each WSI. The results suggests that for BCa cases with both low ODX score and low BR grade, the mean tubule nuclei ratio was significantly higher than that obtained for the BCa cases with both high ODX score and high BR grade (p < 0:01). The low ODX and low BR grade cases also presented a significantly higher average tubule nuclei ratio when compared with the rest of the BCa cases (p < 0:05). Finally, the BCa cases that presented both a high ODX and high BR grade show a mean tubule nuclei to total number of nuclei ratio which was significantly smaller than that obtained for the rest of BCa cases (p < 0:01).

SPIE Journal Paper | 10 October 2014

Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features

Haibo Wang, Angel Cruz Roa, Ajay Basavanhally, Hannah Gilmore, Natalie Shih, Mike Feldman, John Tomaszewski, Fabio Gonzalez, Anant Madabhushi

JMI, Vol. 1, Issue 03, 034003, (October 2014) https://doi.org/10.1117/12.10.1117/1.JMI.1.3.034003

KEYWORDS: Breast cancer, Pathology, Feature extraction, Image segmentation, Computing systems, Neurons, RGB color model, Scanners, Convolutional neural networks, Microscopes

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Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is the mitotic count, which involves quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at multiple high power fields (HPFs) on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Although handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely supervised feature generation methods, there is an appeal in attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. We present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color, and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing the performance by leveraging the disconnected feature sets. Evaluation on the public ICPR12 mitosis dataset that has 226 mitoses annotated on 35 HPFs (400× magnification) by several pathologists and 15 testing HPFs yielded an F-measure of 0.7345. Our approach is accurate, fast, and requires fewer computing resources compared to existent methods, making this feasible for clinical use.

Proceedings Article | 20 March 2014 Paper

Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection

Haibo Wang, Angel Cruz-Roa, Ajay Basavanhally, Hannah Gilmore, Natalie Shih, Mike Feldman, John Tomaszewski, Fabio Gonzalez, Anant Madabhushi

Proceedings Volume 9041, 90410B (2014) https://doi.org/10.1117/12.2043902

KEYWORDS: Feature extraction, RGB color model, Computing systems, Scanners, Breast cancer, Data modeling, Microscopes, Image segmentation, Convolutional neural networks, Glasses

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Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is mitotic count, which involves quantifying the number of cells in the process of dividing (i.e. undergoing mitosis) at a specific point in time. Currently mitosis counting is done manually by a pathologist looking at multiple high power fields on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical or textural attributes of mitoses or features learned with convolutional neural networks (CNN). While handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely unsupervised feature generation methods, there is an appeal to attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. In this paper, we present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing performance by leveraging the disconnected feature sets. Evaluation on the public ICPR12 mitosis dataset that has 226 mitoses annotated on 35 High Power Fields (HPF, x400 magnification) by several pathologists and 15 testing HPFs yielded an F-measure of 0.7345. Apart from this being the second best performance ever recorded for this MITOS dataset, our approach is faster and requires fewer computing resources compared to extant methods, making this feasible for clinical use.

Proceedings Article | 20 March 2014 Paper

Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks

Angel Cruz-Roa, Ajay Basavanhally, Fabio González, Hannah Gilmore, Michael Feldman, Shridar Ganesan, Natalie Shih, John Tomaszewski, Anant Madabhushi

Proceedings Volume 9041, 904103 (2014) https://doi.org/10.1117/12.2043872

KEYWORDS: Tissues, Visualization, Tumors, Image segmentation, Convolution, Breast cancer, RGB color model, Pathology, Convolutional neural networks, Tumor growth modeling

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This paper presents a deep learning approach for automatic detection and visual analysis of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BCa). Deep learning approaches are learn-from-data methods involving computational modeling of the learning process. This approach is similar to how human brain works using different interpretation levels or layers of most representative and useful features resulting into a hierarchical learned representation. These methods have been shown to outpace traditional approaches of most challenging problems in several areas such as speech recognition and object detection. Invasive breast cancer detection is a time consuming and challenging task primarily because it involves a pathologist scanning large swathes of benign regions to ultimately identify the areas of malignancy. Precise delineation of IDC in WSI is crucial to the subsequent estimation of grading tumor aggressiveness and predicting patient outcome. DL approaches are particularly adept at handling these types of problems, especially if a large number of samples are available for training, which would also ensure the generalizability of the learned features and classifier. The DL framework in this paper extends a number of convolutional neural networks (CNN) for visual semantic analysis of tumor regions for diagnosis support. The CNN is trained over a large amount of image patches (tissue regions) from WSI to learn a hierarchical part-based representation. The method was evaluated over a WSI dataset from 162 patients diagnosed with IDC. 113 slides were selected for training and 49 slides were held out for independent testing. Ground truth for quantitative evaluation was provided via expert delineation of the region of cancer by an expert pathologist on the digitized slides. The experimental evaluation was designed to measure classifier accuracy in detecting IDC tissue regions in WSI. Our method yielded the best quantitative results for automatic detection of IDC regions in WSI in terms of F-measure and balanced accuracy (71.80%, 84.23%), in comparison with an approach using handcrafted image features (color, texture and edges, nuclear textural and architecture), and a machine learning classifier for invasive tumor classification using a Random Forest. The best performing handcrafted features were fuzzy color histogram (67.53%, 78.74%) and RGB histogram (66.64%, 77.24%). Our results also suggest that at least some of the tissue classification mistakes (false positives and false negatives) were less due to any fundamental problems associated with the approach, than the inherent limitations in obtaining a very highly granular annotation of the diseased area of interest by an expert pathologist.

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