Dr. Jonathan C. Chappelow Profile

Dr. Jonathan C. Chappelow

Research Scientist

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Publications (10)

Proceedings Article | 23 February 2012 Paper

Incorporating the whole-mount prostate histology reconstruction program Histostitcher into the extensible imaging platform (XIP) framework

Robert Toth, Jonathan Chappelow, Christoph Vetter, Oliver Kutter, Christoph Russ, Michael Feldman, John Tomaszewski, Natalie Shih, Anant Madabhushi

Proceedings Volume 8315, 83151K (2012) https://doi.org/10.1117/12.912938

KEYWORDS: Visualization, Magnetic resonance imaging, Human-machine interfaces, In vivo imaging, MATLAB, Prostate, Reconstruction algorithms, Computer aided diagnosis and therapy, Prostate cancer, Medical imaging

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There is a need for identifying quantitative imaging (e.g. MRI) signatures for prostate cancer (CaP), so that computer-aided diagnostic methods can be trained to detect disease extent in vivo. Determining CaP extent on in vivo MRI is difficult to do; however, with the availability of ex vivo surgical whole mount histological sections (WMHS) for CaP patients undergoing radical prostatectomy, co-registration methods can be applied to align and map disease extent onto pre-operative MR imaging from the post-operative histology. Yet obtaining digitized images of WHMS for co-registration with the pre-operative MRI is cumbersome since (a) most digital slide scanners are unable to accommodate the entire section, and (b) significant technical expertise is required for whole mount slide preparation. Consequently, most centers opt to construct quartered sections of each histology slice. Prior to co-registration with MRI, however, these quartered sections need to be digitally stitched together to reconstitute a digital, pseudo WMHS. Histostitcher© is an interactive software program that uses semi-automatic registration tools to digitally stitch quartered sections into pseudo WMHS. Histostitcher© was originally developed using the GUI tools provided by the Matlab programming interface, but the clinical use was limited due to the inefficiency of the interface. The limitations of the Matlab based GUI include (a) an inability to edit the fiducials, (b) the rendering being extremely slow, and (c) lack of interactive and rapid visualization tools. In this work, Histostitcher© has been integrated into the eXtensible Imaging Platform (XIP ^TM ) framework (a set of libraries containing functionalities for analyzing and visualizing medical image data). XIP ^TM lends the stitching tool much greater flexibility and functionality by (a) allowing interactive and seamless navigation through the full resolution histology images, (b) the ability to easily add, edit, or remove fiducials and annotations in order to register the quadrants and map the disease extent. In this work, we showcase examples of digital stitching of quartered histological sections into pseudo-WHMS using Histostitcher © via the new XIP ^TM interface. This tool will be particularly useful in clinical trials and large cohort studies where a quick, interactive way of digitally reconstructing pseudo WMHS is required.

Proceedings Article | 16 March 2011 Paper

Empirical evaluation of bias field correction algorithms for computer-aided detection of prostate cancer on T2w MRI

Satish Viswanath, Daniel Palumbo, Jonathan Chappelow, Pratik Patel, B. Nicholas Bloch, Neil Rofsky, Robert Lenkinski, Elizabeth Genega, Anant Madabhushi

Proceedings Volume 7963, 79630V (2011) https://doi.org/10.1117/12.878813

KEYWORDS: Magnetic resonance imaging, Prostate, Computer aided diagnosis and therapy, Prostate cancer, Detection and tracking algorithms, Image segmentation, In vivo imaging, Tissues, Linear filtering

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In magnetic resonance imaging (MRI), intensity inhomogeneity refers to an acquisition artifact which introduces a non-linear variation in the signal intensities within the image. Intensity inhomogeneity is known to significantly affect computerized analysis of MRI data (such as automated segmentation or classification procedures), hence requiring the application of bias field correction (BFC) algorithms to account for this artifact. Quantitative evaluation of BFC schemes is typically performed using generalized intensity-based measures (percent coefficient of variation, %CV ) or information-theoretic measures (entropy). While some investigators have previously empirically compared BFC schemes in the context of different domains (using changes in %CV and entropy to quantify improvements), no consensus has emerged as to the best BFC scheme for any given application. The motivation for this work is that the choice of a BFC scheme for a given application should be dictated by application-specific measures rather than ad hoc measures such as entropy and %CV. In this paper, we have attempted to address the problem of determining an optimal BFC algorithm in the context of a computer-aided diagnosis (CAD) scheme for prostate cancer (CaP) detection from T2-weighted (T2w) MRI. One goal of this work is to identify a BFC algorithm that will maximize the CaP classification accuracy (measured in terms of the area under the ROC curve or AUC). A secondary aim of our work is to determine whether measures such as %CV and entropy are correlated with a classifier-based objective measure (AUC). Determining the presence or absence of these correlations is important to understand whether domain independent BFC performance measures such as %CV , entropy should be used to identify the optimal BFC scheme for any given application. In order to answer these questions, we quantitatively compared 3 different popular BFC algorithms on a cohort of 10 clinical 3 Tesla prostate T2w MRI datasets (comprising 39 2D MRI slices): N3 , PABIC, and the method of Cohen et al. Results of BFC via each of the algorithms was evaluated in terms of %CV , entropy, as well as classifier AUC for CaP detection from T2w MRI. The CaP classifier was trained and evaluated on a per-pixel basis using annotations of CaP obtained via registration of T2w MRI and ex vivo whole-mount histology sections. Our results revealed that different BFC schemes resulted in a maximization of different performance measures, that is, the BFC scheme identified by minimization of %CV and entropy was not the one that maximized AUC as well. Moreover, existing BFC evaluation measures (%CV , entropy) did not correlate with AUC (application-based evaluation), but did correlate with each other, suggesting that domain-specific performance measures should be considered in making a decision regarding choice of appropriate BFC scheme. Our results also revealed that N3 provided the best correction of bias field artifacts in prostate MRI data, when the goal was to identify prostate cancer.

Proceedings Article | 16 March 2011 Paper

Linked statistical shape models for multi-modal segmentation of the prostate on MRI-CT for radiotherapy planning

Najeeb Chowdhury, Jonathan Chappelow, Robert Toth, Sung Kim, Stephen Hahn, Neha Vapiwala, Haibo Lin, Stefan Both, Anant Madabhushi

Proceedings Volume 7963, 796314 (2011) https://doi.org/10.1117/12.878416

KEYWORDS: Prostate, Magnetic resonance imaging, Computed tomography, Image segmentation, Image registration, Diagnostics, Radiotherapy, Printed circuit board testing, Statistical modeling, Principal component analysis

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We present a novel framework for building a linked statistical shape model (LSSM), a statistical shape model (SSM) that links the shape variation of a structure of interest (SOI) across multiple imaging modalities. This framework is particularly relevant in scenarios where accurate delineations of a SOI's boundary on one of the modalities may not be readily available, or difficult to obtain, for training a SSM. We apply the LSSM in the context of multi-modal prostate segmentation for radiotherapy planning, where we segment the prostate on MRI and CT simultaneously. Prostate capsule segmentation is a critical step in prostate radiotherapy planning, where dose plans have to be formulated on CT. Since accurate delineations of the prostate boundary are very difficult to obtain on CT, pre-treatment MRI is now beginning to be acquired at several medical centers. Delineation of the prostate on MRI is acknowledged as being significantly simpler to do compared to CT. Hence, our framework incorporates multi-modal registration of MRI and CT to map 2D boundary delineations of prostate (obtained from an expert radiation oncologist) on MR training images onto corresponding CT images. The delineations of the prostate capsule on MRI and CT allows for 3D reconstruction of the prostate shape which facilitates the building of the LSSM. We acquired 7 MRI-CT patient studies and used the leave-one-out strategy to train and evaluate our LSSM (fLSSM), built using expert ground truth delineations on MRI and MRI-CT fusion derived capsule delineations on CT. A unique attribute of our fLSSM is that it does not require expert delineations of the capsule on CT. In order to perform prostate MRI segmentation using the fLSSM, we employed a regionbased approach where we deformed the evolving prostate boundary to optimize a mutual information based cost criterion, which took into account region-based intensity statistics of the image being segmented. The final prostate segmentation was then transferred onto the CT image using the LSSM. We compared our fLSSM against another LSSM (xLSSM), where, unlike the fLSSM, expert delineations of the capsule on both MRI and CT were employed in the model building; xLSSM representing the idealized LSSM. We also compared our fLSSM against an exclusive CT-based SSM (ctSSM), built from expert delineations of capsule on CT only. Due to the intensity-driven nature of the segmentation algorithm, the ctSSM was not able segment the prostate. On MRI, the xLSSM and fLSSM yielded almost identical results. On CT, our results suggest that the fLSSM, while not dependent on highly accurate delineations of the capsule on CT, yields comparable results to an idealized LSSM scheme (xLSSM). Hence, the fLSSM provides an accurate alternative to SSMs that require careful SOI delineations that may be difficult or laborious to obtain, while providing concurrent segmentations of the capsule on multiple modalities.

Proceedings Article | 4 March 2011 Paper

Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): detecting prostate cancer on multi-parametric MRI

Satish Viswanath, B. Nicholas Bloch, Jonathan Chappelow, Pratik Patel, Neil Rofsky, Robert Lenkinski, Elizabeth Genega, Anant Madabhushi

Proceedings Volume 7963, 79630U (2011) https://doi.org/10.1117/12.878312

KEYWORDS: Radon, Magnetic resonance imaging, Diffusion weighted imaging, Data fusion, Magnetorheological finishing, Prostate cancer, Image registration, In vivo imaging, Feature extraction, Fermium

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Currently, there is significant interest in developing methods for quantitative integration of multi-parametric (structural, functional) imaging data with the objective of building automated meta-classifiers to improve disease detection, diagnosis, and prognosis. Such techniques are required to address the differences in dimensionalities and scales of individual protocols, while deriving an integrated multi-parametric data representation which best captures all disease-pertinent information available. In this paper, we present a scheme called Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE); a powerful, generalizable framework applicable to a variety of domains for multi-parametric data representation and fusion. Our scheme utilizes an ensemble of embeddings (via dimensionality reduction, DR); thereby exploiting the variance amongst multiple uncorrelated embeddings in a manner similar to ensemble classifier schemes (e.g. Bagging, Boosting). We apply this framework to the problem of prostate cancer (CaP) detection on 12 3 Tesla pre-operative in vivo multi-parametric (T2-weighted, Dynamic Contrast Enhanced, and Diffusion-weighted) magnetic resonance imaging (MRI) studies, in turn comprising a total of 39 2D planar MR images. We first align the different imaging protocols via automated image registration, followed by quantification of image attributes from individual protocols. Multiple embeddings are generated from the resultant high-dimensional feature space which are then combined intelligently to yield a single stable solution. Our scheme is employed in conjunction with graph embedding (for DR) and probabilistic boosting trees (PBTs) to detect CaP on multi-parametric MRI. Finally, a probabilistic pairwise Markov Random Field algorithm is used to apply spatial constraints to the result of the PBT classifier, yielding a per-voxel classification of CaP presence. Per-voxel evaluation of detection results against ground truth for CaP extent on MRI (obtained by spatially registering pre-operative MRI with available whole-mount histological specimens) reveals that EMPrAvISE yields a statistically significant improvement (AUC=0.77) over classifiers constructed from individual protocols (AUC=0.62, 0.62, 0.65, for T2w, DCE, DWI respectively) as well as one trained using multi-parametric feature concatenation (AUC=0.67).

Proceedings Article | 13 March 2010 Paper

A structural-functional MRI-based disease atlas: application to computer-aided-diagnosis of prostate cancer

G. Xiao, B. Bloch, J. Chappelow, E. Genega, N. Rofsky, R. Lenkinski, A. Madabhushi

Proceedings Volume 7623, 762303 (2010) https://doi.org/10.1117/12.845554

KEYWORDS: Magnetic resonance imaging, Image registration, Cancer, Prostate cancer, Prostate, Feature extraction, Computer aided diagnosis and therapy, Medical imaging, Biopsy, In vivo imaging

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Different imaging modalities or protocols of a single patient may convey different types of information regarding a disease for the same anatomical organ/tissue. On the other hand, multi-modal/multi-protocol medical images from several different patients can also provide spatial statistics of the disease occurrence, which in turn can greatly aid in disease diagnosis and aid in improved, accurate biopsy and targeted treatment. It is therefore important to not only integrate medical images from multiple patients into a common coordinate frame (in the form of a population-based atlas), but also find the correlation between these multi-modal/multi-protocol data features and the disease spatial distribution in order to identify different quantitative structural and functional disease signatures. Most previous work on construction of anatomical atlases has focused on deriving a population-based atlas for the purpose of deriving the spatial statistics. Moreover, these models are typically derived from normal or healthy subjects, either explicitly or implicitly, where it is assumed that the inter-patient pathological variation is not large. These methods are not suitable for constructing a disease atlas, where significant differences between patients on account of disease related variations can be expected. In this paper, we present a novel framework for the construction of a multi-parametric MRI-based data-driven disease atlas consisting of multi-modal and multi-protocol data from across multiple patient studies. Our disease atlas contains 3 Tesla structural (T2) and functional (dynamic contrast enhanced (DCE)) prostate in vivo MRI with corresponding whole mount histology specimens obtained via radical prostatectomy. Our atlas construction framework comprises 3 distinct modules: (a) determination of disease spatial extent on the multi-protocol MR imagery for each patient, (b) construction of a multi-protocol MR imaging spatial atlas which captures the geographical proclivity of the disease, and (c) feature extraction and the construction of the data-driven multi-protocol MRI based prostate cancer atlas. The marriage of data driven and spatial atlases could serve as a useful tool for clinicians to identifying structural and functional imaging disease signatures so as to make better, more informed diagnoses. Each spatial location in this atlas can be associated with a high dimensional multi-attribute quantitative feature vector. Additionally, since the feature vectors are extracted from across multiple patient studies, each spatial location in the data-driven atlas can be characterized by a feature distribution (in turn characterized by a mean and standard deviation). Preliminary investigation in quantitatively correlating the disease signatures from across the spatial and data driven atlases suggests that our quantitative atlas framework could emerge as a powerful tool for discovering prostate cancer imaging signatures.

Proceedings Article | 27 February 2010 Paper

Computer-assisted targeted therapy (CATT) for prostate radiotherapy planning by fusion of CT and MRI

Jonathan Chappelow, Stefan Both, Satish Viswanath, Stephen Hahn, Michael Feldman, Mark Rosen, John Tomaszewski, Neha Vapiwala, Pratik Patel, Anant Madabhushi

Proceedings Volume 7625, 76252C (2010) https://doi.org/10.1117/12.844653

KEYWORDS: Magnetic resonance imaging, Prostate, Diagnostics, Computed tomography, Cancer, Image registration, Tumors, Radiotherapy, Printed circuit board testing, Computer aided diagnosis and therapy

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Proceedings Article | 27 March 2009 Paper

COLLINARUS: collection of image-derived non-linear attributes for registration using splines

Jonathan Chappelow, B. Nicolas Bloch, Neil Rofsky, Elizabeth Genega, Robert Lenkinski, William DeWolf, Satish Viswanath, Anant Madabhushi

Proceedings Volume 7259, 72592N (2009) https://doi.org/10.1117/12.812352

KEYWORDS: Image registration, Magnetic resonance imaging, Cancer, Prostate, In vivo imaging, Brain, Rigid registration, Neuroimaging, Image processing, Image fusion

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We present a new method for fully automatic non-rigid registration of multimodal imagery, including structural and functional data, that utilizes multiple texutral feature images to drive an automated spline based non-linear image registration procedure. Multimodal image registration is significantly more complicated than registration of images from the same modality or protocol on account of difficulty in quantifying similarity between different structural and functional information, and also due to possible physical deformations resulting from the data acquisition process. The COFEMI technique for feature ensemble selection and combination has been previously demonstrated to improve rigid registration performance over intensity-based MI for images of dissimilar modalities with visible intensity artifacts. Hence, we present here the natural extension of feature ensembles for driving automated non-rigid image registration in our new technique termed Collection of Image-derived Non-linear Attributes for Registration Using Splines (COLLINARUS). Qualitative and quantitative evaluation of the COLLINARUS scheme is performed on several sets of real multimodal prostate images and synthetic multiprotocol brain images. Multimodal (histology and MRI) prostate image registration is performed for 6 clinical data sets comprising a total of 21 groups of in vivo structural (T2-w) MRI, functional dynamic contrast enhanced (DCE) MRI, and ex vivo WMH images with cancer present. Our method determines a non-linear transformation to align WMH with the high resolution in vivo T2-w MRI, followed by mapping of the histopathologic cancer extent onto the T2-w MRI. The cancer extent is then mapped from T2-w MRI onto DCE-MRI using the combined non-rigid and affine transformations determined by the registration. Evaluation of prostate registration is performed by comparison with the 3 time point (3TP) representation of functional DCE data, which provides an independent estimate of cancer extent. The set of synthetic multiprotocol images, acquired from the BrainWeb Simulated Brain Database, comprises 11 pairs of T1-w and proton density (PD) MRI of the brain. Following the application of a known warping to misalign the images, non-rigid registration was then performed to recover the original, correct alignment of each image pair. Quantitative evaluation of brain registration was performed by direct comparison of (1) the recovered deformation field to the applied field and (2) the original undeformed and recovered PD MRI. For each of the data sets, COLLINARUS is compared with the MI-driven counterpart of the B-spline technique. In each of the quantitative experiments, registration accuracy was found to be significantly (p < 0.05) for COLLINARUS compared with MI-driven B-spline registration. Over 11 slices, the mean absolute error in the deformation field recovered by COLLINARUS was found to be 0.8830 mm.

Proceedings Article | 28 February 2009 Paper

Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol in vivo 3 Tesla MRI

Satish Viswanath, B. Nicolas Bloch, Mark Rosen, Jonathan Chappelow, Robert Toth, Neil Rofsky, Robert Lenkinski, Elizabeth Genega, Arjun Kalyanpur, Anant Madabhushi

Proceedings Volume 7260, 72603I (2009) https://doi.org/10.1117/12.811899

KEYWORDS: Magnetic resonance imaging, Prostate, Image segmentation, Image registration, In vivo imaging, Data fusion, Prostate cancer, Computer aided diagnosis and therapy, Data integration, Binary data

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Screening and detection of prostate cancer (CaP) currently lacks an image-based protocol which is reflected in the high false negative rates currently associated with blinded sextant biopsies. Multi-protocol magnetic resonance imaging (MRI) offers high resolution functional and structural data about internal body structures (such as the prostate). In this paper we present a novel comprehensive computer-aided scheme for CaP detection from high resolution in vivo multi-protocol MRI by integrating functional and structural information obtained via dynamic-contrast enhanced (DCE) and T2-weighted (T2-w) MRI, respectively. Our scheme is fully-automated and comprises (a) prostate segmentation, (b) multimodal image registration, and (c) data representation and multi-classifier modules for information fusion. Following prostate boundary segmentation via an improved active shape model, the DCE/T2-w protocols and the T2-w/ex vivo histological prostatectomy specimens are brought into alignment via a deformable, multi-attribute registration scheme. T2-w/histology alignment allows for the mapping of true CaP extent onto the in vivo MRI, which is used for training and evaluation of a multi-protocol MRI CaP classifier. The meta-classifier used is a random forest constructed by bagging multiple decision tree classifiers, each trained individually on T2-w structural, textural and DCE functional attributes. 3-fold classifier cross validation was performed using a set of 18 images derived from 6 patient datasets on a per-pixel basis. Our results show that the results of CaP detection obtained from integration of T2-w structural textural data and DCE functional data (area under the ROC curve of 0.815) significantly outperforms detection based on either of the individual modalities (0.704 (T2-w) and 0.682 (DCE)). It was also found that a meta-classifier trained directly on integrated T2-w and DCE data (data-level integration) significantly outperformed a decision-level meta-classifier, constructed by combining the classifier outputs from the individual T2-w and DCE channels.

Proceedings Article | 17 March 2008 Paper

Improving supervised classification accuracy using non-rigid multimodal image registration: detecting prostate cancer

Jonathan Chappelow, Satish Viswanath, James Monaco, Mark Rosen, John Tomaszewski, Michael Feldman, Anant Madabhushi

Proceedings Volume 6915, 69150V (2008) https://doi.org/10.1117/12.770703

KEYWORDS: Magnetic resonance imaging, Image registration, Curium, Cancer, Prostate, Computer aided diagnosis and therapy, Prostate cancer, Image segmentation, Independent component analysis, Image classification

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Computer-aided diagnosis (CAD) systems for the detection of cancer in medical images require precise labeling of training data. For magnetic resonance (MR) imaging (MRI) of the prostate, training labels define the spatial extent of prostate cancer (CaP); the most common source for these labels is expert segmentations. When ancillary data such as whole mount histology (WMH) sections, which provide the gold standard for cancer ground truth, are available, the manual labeling of CaP can be improved by referencing WMH. However, manual segmentation is error prone, time consuming and not reproducible. Therefore, we present the use of multimodal image registration to automatically and accurately transcribe CaP from histology onto MRI following alignment of the two modalities, in order to improve the quality of training data and hence classifier performance. We quantitatively demonstrate the superiority of this registration-based methodology by comparing its results to the manual CaP annotation of expert radiologists. Five supervised CAD classifiers were trained using the labels for CaP extent on MRI obtained by the expert and 4 different registration techniques. Two of the registration methods were affi;ne schemes; one based on maximization of mutual information (MI) and the other method that we previously developed, Combined Feature Ensemble Mutual Information (COFEMI), which incorporates high-order statistical features for robust multimodal registration. Two non-rigid schemes were obtained by succeeding the two affine registration methods with an elastic deformation step using thin-plate splines (TPS). In the absence of definitive ground truth for CaP extent on MRI, classifier accuracy was evaluated against 7 ground truth surrogates obtained by different combinations of the expert and registration segmentations. For 26 multimodal MRI-WMH image pairs, all four registration methods produced a higher area under the receiver operating characteristic curve compared to that obtained from expert annotation. These results suggest that in the presence of additional multimodal image information one can obtain more accurate object annotations than achievable via expert delineation despite vast differences between modalities that hinder image registration.

Proceedings Article | 7 March 2007 Paper

Multimodal image registration of ex vivo 4 Tesla MRI with whole mount histology for prostate cancer detection

Jonathan Chappelow, Anant Madabhushi, Mark Rosen, John Tomaszeweski, Michael Feldman

Proceedings Volume 6512, 65121S (2007) https://doi.org/10.1117/12.710558

KEYWORDS: Magnetic resonance imaging, Cancer, Image registration, Prostate, Image segmentation, Computer aided diagnosis and therapy, Tissues, Visualization, Prostate cancer, Electronic filtering

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