Dr. Rachel E. Sparks Profile

Dr. Rachel E. Sparks

at King's College London

SPIE Involvement:

Conference Program Committee | Author

SPIE Journal Paper | 28 March 2017

Connecting Markov random fields and active contour models: application to gland segmentation and classification

Jun Xu, James Monaco, Rachel Sparks, Anant Madabhushi

JMI, Vol. 4, Issue 02, 021107, (March 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.2.021107

KEYWORDS: Tissues, Image segmentation, Performance modeling, Magnetorheological finishing, Prostate, Data modeling, Actinium, Tumor growth modeling, Visual process modeling, Statistical modeling

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Proceedings Article | 21 March 2014 Paper

Spectral embedding-based registration (SERg) for multimodal fusion of prostate histology and MRI

Eileen Hwuang, Mirabela Rusu, Sudha Karthigeyan, Shannon Agner, Rachel Sparks, Natalie Shih, John Tomaszewski, Mark Rosen, Michael Feldman, Anant Madabhushi

Proceedings Volume 9034, 90343P (2014) https://doi.org/10.1117/12.2044317

KEYWORDS: Image registration, Magnetic resonance imaging, Prostate, Independent component analysis, Chromium, Feature extraction, Image restoration, Image segmentation, Brain, Neuroimaging

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Multi-modal image registration is needed to align medical images collected from different protocols or imaging sources, thereby allowing the mapping of complementary information between images. One challenge of multimodal image registration is that typical similarity measures rely on statistical correlations between image intensities to determine anatomical alignment. The use of alternate image representations could allow for mapping of intensities into a space or representation such that the multimodal images appear more similar, thus facilitating their co-registration. In this work, we present a spectral embedding based registration (SERg) method that uses non-linearly embedded representations obtained from independent components of statistical texture maps of the original images to facilitate multimodal image registration. Our methodology comprises the following main steps: 1) image-derived textural representation of the original images, 2) dimensionality reduction using independent component analysis (ICA), 3) spectral embedding to generate the alternate representations, and 4) image registration. The rationale behind our approach is that SERg yields embedded representations that can allow for very different looking images to appear more similar, thereby facilitating improved co-registration. Statistical texture features are derived from the image intensities and then reduced to a smaller set by using independent component analysis to remove redundant information. Spectral embedding generates a new representation by eigendecomposition from which only the most important eigenvectors are selected. This helps to accentuate areas of salience based on modality-invariant structural information and therefore better identifies corresponding regions in both the template and target images. The spirit behind SERg is that image registration driven by these areas of salience and correspondence should improve alignment accuracy. In this work, SERg is implemented using Demons to allow the algorithm to more effectively register multimodal images. SERg is also tested within the free-form deformation framework driven by mutual information. Nine pairs of synthetic T1-weighted to T2-weighted brain MRI were registered under the following conditions: five levels of noise (0%, 1%, 3%, 5%, and 7%) and two levels of bias field (20% and 40%) each with and without noise. We demonstrate that across all of these conditions, SERg yields a mean squared error that is 81.51% lower than that of Demons driven by MRI intensity alone. We also spatially align twenty-six ex vivo histology sections and in vivo prostate MRI in order to map the spatial extent of prostate cancer onto corresponding radiologic imaging. SERg performs better than intensity registration by decreasing the root mean squared distance of annotated landmarks in the prostate gland via both Demons algorithm and mutual information-driven free-form deformation. In both synthetic and clinical experiments, the observed improvement in alignment of the template and target images suggest the utility of parametric eigenvector representations and hence SERg for multimodal image registration.

Proceedings Article | 21 March 2014 Paper

Spatially aware expectation maximization (SpAEM): application to prostate TRUS segmentation

Mahdi Orooji, Rachel Sparks, B. Nicolas Bloch, Ernest Feleppa, Dean Barratt, Anant Madabhushi

Proceedings Volume 9034, 90343Y (2014) https://doi.org/10.1117/12.2043981

KEYWORDS: Prostate, Image segmentation, Ultrasonography, Expectation maximization algorithms, Feature extraction, Image processing, Image filtering, Statistical analysis, Medical imaging, Cancer

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Proceedings Article | 29 March 2013 Paper

Identifying in vivo DCE MRI parameters correlated with ex vivo quantitative microvessel architecture: A radiohistomorphometric approach

Asha Singanamalli, Rachel Sparks, Mirabela Rusu, Natalie Shih, Amy Ziober, John Tomaszewski, Mark Rosen, Michael Feldman, Anant Madabhushi

Proceedings Volume 8676, 867604 (2013) https://doi.org/10.1117/12.2008136

KEYWORDS: Tumors, Magnetic resonance imaging, Feature extraction, Tissues, Image segmentation, Targeting Task Performance metric, In vivo imaging, Prostate cancer, Prostate, Pathology

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We introduce a novel radiohistomorphometric method for quantitative correlation and subsequent discovery of imaging markers for aggressive prostate cancer (CaP). While this approach can be employed in the context any imaging modality and disease domain, we seek to identify quantitative dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) attributes that are highly correlated with density and architecture of tumor microvessels, surrogate markers of CaP aggressiveness. This retrospective study consisted of five Gleason score matched patients who underwent 3 Tesla multiparametric MRI prior to radical prostatectomy (RP). The excised gland was sectioned and quartered with a rotary knife. For each serial section, digitized images of individual quadrants were reconstructed into pseudo whole mount sections via previously developed stitching program. The individual quadrants were stained with vascular marker CD31 and annotated for CaP by an expert pathologist. The stained microvessel regions were quantitatively characterized in terms of density and architectural arrangement via graph algorithms, yielding a series of quantitative histomorphometric features. The reconstructed pseudo whole mount histologic sections were non-linearly co-registered with DCE MRI to identify tumor extent on MRI on a voxel-by-voxel basis. Pairwise correlations between kinetic and microvessel features within CaP annotated regions on the two modalities were computed to identify highly correlated attributes. Preliminary results of the radiohistomorphometric correlation identified 8 DCE MRI kinetic features that were highly and significantly (p<0.05) correlated with a number of microvessel parameters. Most of the identified imaging features were related to rate of washout (Rwo) and initial area under the curve (IAUC). Association of those attributes with Gleason patterns showed that the identified imaging features clustered most of the tumors with primary Gleason pattern of 3 together. These results suggest that Rwo and IAUC may be promising candidate imaging markers for identification of aggressive CaP in vivo.

Proceedings Article | 8 March 2013 Paper

Fully automated prostate magnetic resonance imaging and transrectal ultrasound fusion via a probabilistic registration metric

Rachel Sparks, B. Nicholas Bloch, Ernest Feleppa, Dean Barratt, Anant Madabhushi

Proceedings Volume 8671, 86710A (2013) https://doi.org/10.1117/12.2007610

KEYWORDS: Prostate, Magnetic resonance imaging, Biopsy, Signal attenuation, Image registration, Image segmentation, Fourier transforms, Curium, Ultrasonography, Tellurium

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Proceedings Article | 23 February 2012 Paper

Gleason grading of prostate histology utilizing manifold regularization via statistical shape model of manifolds

Rachel Sparks, Anant Madabhushi

Proceedings Volume 8315, 83151J (2012) https://doi.org/10.1117/12.912887

KEYWORDS: Prostate, Statistical modeling, Tissues, Biopsy, Prostate cancer, Visualization, Image classification, Computing systems, Decision support systems, Image segmentation

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Gleason patterns of prostate cancer histopathology, characterized primarily by morphological and architectural attributes of histological structures (glands and nuclei), have been found to be highly correlated with disease aggressiveness and patient outcome. Gleason patterns 4 and 5 are highly correlated with more aggressive disease and poorer patient outcome, while Gleason patterns 1-3 tend to reflect more favorable patient outcome. Because Gleason grading is done manually by a pathologist visually examining glass (or digital) slides, subtle morphologic and architectural differences of histological attributes may result in grading errors and hence cause high inter-observer variability. Recently some researchers have proposed computerized decision support systems to automatically grade Gleason patterns by using features pertaining to nuclear architecture, gland morphology, as well as tissue texture. Automated characterization of gland morphology has been shown to distinguish between intermediate Gleason patterns 3 and 4 with high accuracy. Manifold learning (ML) schemes attempt to generate a low dimensional manifold representation of a higher dimensional feature space while simultaneously preserving nonlinear relationships between object instances. Classification can then be performed in the low dimensional space with high accuracy. However ML is sensitive to the samples contained in the dataset; changes in the dataset may alter the manifold structure. In this paper we present a manifold regularization technique to constrain the low dimensional manifold to a specific range of possible manifold shapes, the range being determined via a statistical shape model of manifolds (SSMM). In this work we demonstrate applications of the SSMM in (1) identifying samples on the manifold which contain noise, defined as those samples which deviate from the SSMM, and (2) accurate out-of-sample extrapolation (OSE) of newly acquired samples onto a manifold constrained by the SSMM. We demonstrate these applications of the SSMM in the context of distinguishing between Gleason patterns 3 and 4 using glandular morphologic features in a prostate histopathology dataset of 58 patient studies. Identifying and eliminating noisy samples from the manifold via the SSMM results in a statistically significant improvement in classification accuracy (CA), 93.0 ± 1.0% with removal of noisy samples compared to a CA of 90.9 ± 1.1% without removal of samples. The use of the SSMM for OSE of new independent test instances also shows statistically significant improvement in CA, 87.1±0.8% with the SSMM compared to 85.6±0.1% without the SSMM. Similar improvements were observed for the synthetic Swiss Roll and Helix datasets.

Proceedings Article | 11 March 2011 Paper

Content-based image retrieval utilizing explicit shape descriptors: applications to breast MRI and prostate histopathology

Rachel Sparks, Anant Madabhushi

Proceedings Volume 7962, 79621I (2011) https://doi.org/10.1117/12.878428

KEYWORDS: Image retrieval, Breast, Prostate, Medical imaging, Model-based design, Chemical species, Shape analysis, Databases, Image registration, Diagnostics

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Content-based image retrieval (CBIR) systems, in the context of medical image analysis, allow for a user to compare a query image to previously archived database images in terms of diagnostic and/or prognostic similarity. CBIR systems can therefore serve as a powerful computerized decision support tool for clinical diagnostics and also serve as a useful learning tool for medical students, residents, and fellows. An accurate CBIR system relies on two components, (1) image descriptors which are related to a previously defined notion of image similarity and (2) quantification of image descriptors in order to accurately characterize and capture the a priori defined image similarity measure. In many medical applications, the morphology of an object of interest (e.g. breast lesions on DCE-MRI or glands on prostate histopathology) may provide important diagnostic and prognostic information regarding the disease being investigated. Morphological attributes can be broadly categorized as being (a) model-based (MBD) or (b) non-model based (NMBD). Most computerized decision support tools leverage morphological descriptors (e.g. area, contour variation, and compactness) which belong to the latter category in that they do not explicitly model morphology for the object of interest. Conversely, descriptors such as Fourier descriptors (FDs) explicitly model the object of interest. In this paper, we present a CBIR system that leverages a novel set of MBD called Explicit Shape Descriptors (ESDs) which accurately describe the similarity between the morphology of objects of interest. ESDs are computed by: (a) fitting shape models to objects of interest, (b) pairwise comparison between shape models, and (c) a nonlinear dimensionality reduction scheme to extract a concise set of morphological descriptors in a reduced dimensional embedding space. We utilized our ESDs in the context of CBIR in three datasets: (1) the synthetic MPEG-7 Set B containing 1400 silhouette images, (2) DCE-MRI of 91 breast lesions, (3) and digitized prostate histopathology dataset comprised of 888 glands. For each dataset, each image was sequentially selected as a query image and the remaining images in the database were ranked according to how similar they were to the query image based on the ESDs. From this ranking, area under the precision-recall curve (AUPRC) was calculated and averaged over all possible query images, for each of the three datasets. For the MPEG-7 dataset bull's eye accuracy for our CBIR system is 78.65%, comparable to several state of the art shape modeling approaches. For the breast DCE-MRI dataset, ESDs outperforms a set of NMBDs with an AUPRC of 0.55 ± 0.02. For the prostate histopathology dataset, ESDs and FDs perform equivalently with an AUPRC of 0.40 ± .01, but outperform NMBDs.

Showing 5 of 7 publications

Conference Committee Involvement (6)

Image Processing

16 February 2025 | San Diego, California, United States

Image Processing

19 February 2024 | San Diego, California, United States

Image Processing

20 February 2023 | San Diego, California, United States

Image Processing

20 February 2022 | San Diego, California, United States

Image Processing

15 February 2021 | Online Only, California, United States

Showing 5 of 6 Conference Committees

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