Dr. Jun Xu Profile

Dr. Jun Xu

Professor at Nanjing Univ of Information Science & Technology

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Publications (6)

Proceedings Article | 16 March 2020 Presentation + Paper

Computationally derived cytological image markers for predicting risk of relapse in acute myeloid leukemia patients following bone marrow transplantation

Sara ArabYarmohammadi, Zelin Zhang, Patrick Leo, Marjan Firouznia, Andrew Janowczyk, Haojia Li, Nathaniel Braman, Kaustav Bera, Behtash Nezami, Howard Meyerson, Jun Xu, Leland Metheny, Anant Madabhushi

Proceedings Volume 11320, 1132004 (2020) https://doi.org/10.1117/12.2549730

KEYWORDS: Fractal analysis, Image segmentation, Feature extraction, Bone, Leukemia, Blood, Shape analysis, Visualization, Transplantation, Visual process modeling

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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.

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 | 16 March 2011 Paper

Spectral embedding based active contour (SEAC): application to breast lesion segmentation on DCE-MRI

Shannon Agner, Jun Xu, Mark Rosen, Sudha Karthigeyan, Sarah Englander, Anant Madabhushi

Proceedings Volume 7963, 796305 (2011) https://doi.org/10.1117/12.878218

KEYWORDS: Image segmentation, Breast, Magnetic resonance imaging, Feature extraction, Medical imaging, Image filtering, Data modeling, Yield improvement, Tumor growth modeling, Fuzzy logic

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Proceedings Article | 4 March 2011 Paper

Incorporating domain knowledge for tubule detection in breast histopathology using O'Callaghan neighborhoods

Ajay Basavanhally, Elaine Yu, Jun Xu, Shridar Ganesan, Michael Feldman, John Tomaszewski, Anant Madabhushi

Proceedings Volume 7963, 796310 (2011) https://doi.org/10.1117/12.878092

KEYWORDS: Cancer, Spatial analysis, Image segmentation, RGB color model, Feature extraction, Deconvolution, Breast cancer, Tissues, Breast, Data modeling

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An important criterion for identifying complicated objects with multiple attributes is the use of domain knowledge which reflects the precise spatial linking of the constituent attributes. Hence, simply detecting the presence of the low-level attributes that constitute the object, even in cases where these attributes might be detected in spatial proximity to each other is usually not a robust strategy. The O'Callaghan neighborhood is an ideal vehicle for characterizing objects comprised of multiple attributes spatially connected to each other in a precise fashion because it allows for modeling and imposing spatial distance and directional constraints on the object attributes. In this work we apply the O'Callaghan neighborhood to the problem of tubule identification on hematoxylin and eosin (H & E) stained breast cancer (BCa) histopathology, where a tubule is characterized by a central lumen surrounded by cytoplasm and a ring of nuclei around the cytoplasm. The detection of tubules is important because tubular density is an important predictor in cancer grade determination. In the context of ER+ BCa, grade has been shown to be strongly linked to disease aggressiveness and patient outcome. The more standard pattern recognition approaches to detection of complex objects typically involve training classifiers for low-level attributes individually. For tubule detection, the spatial proximity of lumen, cytoplasm, and nuclei might suggest the presence of a tubule. However such an approach could also suffer from false positive errors due to the presence of fat, stroma, and other lumen-like areas that could be mistaken for tubules. In this work, tubules are identified by imposing spatial and distance constraints using O'Callaghan neighborhoods between the ring of nuclei around each lumen. In this work, cancer nuclei in each image are found via a color deconvolution scheme, which isolates the hematoxylin stain, thereby enabling automated detection of individual cell nuclei. The potential lumen areas are segmented using a Hierarchical Normalized Cut (HNCut) initialized Color Gradient based Active Contour model (CGAC). The HNCut algorithm detects lumen-like areas within the image via pixel clustering across multiple image resolutions. The pixel clustering provides initial contours for the CGAC. From the initial contours, the CGAC evolves to segment the boundaries of the potential lumen areas. A set of 22 graph-based image features characterizing the spatial linking between the tubular attributes is extracted from the O'Callaghan neighborhood in order to distinguish true from false lumens. Evaluation on 1226 potential lumen areas from 14 patient studies produces an area under the receiver operating characteristic curve (AUC) of 0.91 along with the ability to classify true lumen with 86% accuracy. In comparison to manual grading of tubular density over 105 images, our method is able to distinguish histopathology images with low and high tubular density at 89% accuracy (AUC = 0.94).

Proceedings Article | 13 March 2010 Paper

A weighted mean shift, normalized cuts initialized color gradient based geodesic active contour model: applications to histopathology image segmentation

Jun Xu, Andrew Janowczyk, Sharat Chandran, Anant Madabhushi

Proceedings Volume 7623, 76230Y (2010) https://doi.org/10.1117/12.845602

KEYWORDS: Image segmentation, Data modeling, Prostate, Tumor growth modeling, Biopsy, RGB color model, Sensors, Breast, Cancer, Expectation maximization algorithms

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While geodesic active contours (GAC) have become very popular tools for image segmentation, they are sensitive to model initialization. In order to get an accurate segmentation, the model typically needs to be initialized very close to the true object boundary. Apart from accuracy, automated initialization of the objects of interest is an important pre-requisite to being able to run the active contour model on very large images (such as those found in digitized histopathology). A second limitation of GAC model is that the edge detector function is based on gray scale gradients; color images typically being converted to gray scale prior to computing the gradient. For color images, however, the gray scale gradient results in broken edges and weak boundaries, since the other channels are not exploited for the gradient determination. In this paper we present a new geodesic active contour model that is driven by an accurate and rapid object initialization scheme-weighted mean shift normalized cuts (WNCut). WNCut draws its strength from the integration of two powerful segmentation strategies-mean shift clustering and normalized cuts. WNCut involves first defining a color swatch (typically a few pixels) from the object of interest. A multi-scale mean shift coupled normalized cuts algorithm then rapidly yields an initial accurate detection of all objects in the scene corresponding to the colors in the swatch. This detection result provides the initial boundary for GAC model. The edge-detector function of the GAC model employs a local structure tensor based color gradient, obtained by calculating the local min/max variations contributed from each color channel (e.g. R,G,B or H,S,V). Our color gradient based edge-detector function results in more prominent boundaries compared to classical gray scale gradient based function. We evaluate segmentation results of our new WNCut initialized color gradient based GAC (WNCut-CGAC) model against a popular region-based model (Chan & Vese) on a total of 60 digitized histopathology images. Across a total of 60 images, the WNCut-CGAC model yielded an average overlap, sensitivity, specificity, and positive predictive value of 73%, 83%, 97%, 84%, compared to the Chan & Vese model which had corresponding values of 64%, 75%, 95%, 72%. The rapid and accurate object initialization scheme (WNCut) and the color gradient make the WNCut-CGAC scheme, an ideal segmentation tool for very large, color imagery.

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