Patents by Inventor Anant Madabhushi

Anant Madabhushi has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 10254358
    Abstract: Methods and apparatus associated with producing a quantification of differences associated with biochemical recurrence (BcR) in a region of tissue demonstrating prostate cancer (PCa) are described. One example apparatus includes a set of logics, and a data store that stores a set of magnetic resonance (MR) images acquired from a population of subjects. The set of logics includes an image acquisition logic that acquires a diagnostic image of a region of tissue in a patient demonstrating PCa, a morphology logic that extracts a shape feature, a volume feature, or an intensity feature from the diagnostic image or from a member of the set of MR images, a differential atlas construction logic that constructs a statistical shape differential atlas from the set of MR images, and a quantification logic that produces a quantification of differences based on the shape feature, the volume feature, or the intensity feature, and the differential atlas.
    Type: Grant
    Filed: November 13, 2017
    Date of Patent: April 9, 2019
    Assignee: Case Western Reserve University
    Inventors: Anant Madabhushi, Mirabela Rusu
  • Publication number: 20190087693
    Abstract: Embodiments predict early stage NSCLC recurrence, and include an image acquisition circuit configured to access an image of a region of tissue demonstrating early-stage NSCLC including a plurality of cellular nuclei; a nuclei detecting and segmentation circuit configured to detect a member of the plurality; and classify the member as a tumor infiltrating lymphocyte (TIL) nucleus or non-TIL nucleus; a spatial TIL feature circuit configured to extract spatial TIL features from the plurality, the spatial TIL features including a first subset of features based on the spatial arrangement of TIL nuclei, and a second subset of features based on the spatial relationship between TIL nuclei and non-TIL nuclei; and an NSCLC recurrence classification circuit configured to compute a probability that region will experience recurrence based on the spatial TIL features; and generate a classification of the region as likely or unlikely to experience recurrence based on the probability.
    Type: Application
    Filed: August 24, 2018
    Publication date: March 21, 2019
    Inventors: Anant Madabhushi, Xiangxue Wang, Vamsidhar Velcheti
  • Publication number: 20190087532
    Abstract: Embodiments predict early stage NSCLC recurrence, and include processors configured to access a pathology image of a region of tissue demonstrating early stage NSCLC; extract a set of pathomic features from the pathology image; access a radiological image of the region of tissue; extract a set of radiomic features from the radiological image; generate a combined feature set that includes at least one member of the set of pathomic features, and at least one member of the set of radiomic features; compute a probability that the region of tissue will experience NSCLC recurrence based, at least in part, on the combined feature set; and classify the region of tissue as recurrent or non-recurrent based, at least in part, on the probability. Embodiments may display the classification, or generate a personalized treatment plan based on the classification.
    Type: Application
    Filed: September 14, 2018
    Publication date: March 21, 2019
    Inventors: Anant Madabhushi, Xiangxue Wang, Pranjal Vaidya, Vamsidhar Velcheti
  • Patent number: 10235755
    Abstract: Methods, apparatus, and other embodiments associated with classifying a region of tissue represented in a digitized whole slide image (WSI) using iterative gradient-based quasi-Monte Carlo (QMC) sampling. One example apparatus includes an image acquisition circuit that acquires a WSI of a region of tissue demonstrating cancerous pathology, an adaptive sampling circuit that selects a subset of tiles from the WSI using an iterative QMC Sobol sequence sampling approach, an invasiveness circuit that determines a probability of a presence of invasive pathology in a member of the subset of tiles, a probability map circuit that generates an invasiveness probability map based on the probability, a probability gradient circuit that generates a gradient image based on the invasiveness probability map, and a classification circuit that classifies the region of tissue based on the probability map. A prognosis or treatment plan may be provided based on the classification of the WSI.
    Type: Grant
    Filed: June 29, 2018
    Date of Patent: March 19, 2019
    Assignee: Case Western Reserve University
    Inventors: Anant Madabhushi, Angel Alfonso Cruz Roa, Fabio Gonzalez
  • Publication number: 20180365829
    Abstract: Embodiments classify lung nodules by accessing a 3D radiological image of a region of tissue, the 3D image including a plurality of voxels and slices, a slice having a thickness; segmenting the nodule represented in the 3D image across contiguous slices, the nodule having a 3D volume and 3D interface, where the 3D interface includes an interface voxel; partitioning the 3D interface into a plurality of nested shells, a nested shell including a plurality of 2D slices, a 2D slice including a boundary pixel; extracting a set of intra-perinodular textural transition (Ipris) features from the 2D slices based on a normal of a boundary pixel of the 2D slices; providing the Ipris features to a machine learning classifier which computes a probability that the nodule is malignant, based, at least in part, on the set of Ipris features; and generating a classification of the nodule based on the probability.
    Type: Application
    Filed: June 20, 2018
    Publication date: December 20, 2018
    Inventors: Anant Madabhushi, Mehdi Alilou
  • Publication number: 20180353149
    Abstract: Embodiments associated with classifying a region of tissue using features extracted from nodules and surrounding structures. One example apparatus includes a feature extraction circuit configured to automatically extract a first set of quantitative features from a nodule represented in at least one CT image, and automatically extract a second set of quantitative features from the lung parenchyma region immediately surrounding the nodule represented in the at least one CT image; a feature selection circuit configured to select an optimally predictive feature set from the first set of quantitative features and the second set of quantitative features; and a training circuit configured to train a classifier using the optimally predictive feature set to assign malignancy risk to a lung nodule represented in a CT image of a region of tissue demonstrating lung nodules. A prognosis or treatment plan may be provided based on the malignancy risk.
    Type: Application
    Filed: July 24, 2018
    Publication date: December 13, 2018
    Inventors: Anant Madabhushi, Mahdi Orooji, Mirabela Rusu, Philip Linden, Robert Gilkeson, Nathaniel Mason Braman, Mehdi Alilou
  • Publication number: 20180342058
    Abstract: Embodiments access an image of a region of interest (ROI) demonstrating cancerous pathology; extract radiomic features from the ROI; define a radiomic feature expression scene based on the ROI and radiomic features; generate a cluster map by superpixel clustering the expression scene; generate an expression map by repartitioning the cluster map into expression levels; compute a textural and spatial phenotypes for the expression map based on the expression levels; construct a radiomic spatial textural (RADISTAT) descriptor by concatenating the textural and spatial phenotypes; provide the RADISTAT descriptor to a machine learning classifier; receive, from the machine learning classifier, a first probability that the ROI is a responder or non-responder, or a second probability that the ROI will experience long-term survival or short-term survival, based, at least in part, on the RADISTAT descriptor; and generate a classification of the ROI as a responder or non-responder, or long-term survivor or short-term surv
    Type: Application
    Filed: May 23, 2018
    Publication date: November 29, 2018
    Inventors: Anant Madabhushi, Satish Viswanath, Jacob Antunes, Pallavi Tiwari
  • Publication number: 20180336395
    Abstract: Embodiments predict biochemical recurrence (BCR) or metastasis by accessing a set of images of a region of tissue demonstrating cancerous pathology, including a tumor region and a tumor adjacent benign (TAB) region, the set of images including a first stain type image, and a second stain type image; segmenting cellular nuclei represented in the first and second image; generating a combined feature set by extracting at least one feature from each of a tumor region and TAB region represented in the first image, and a tumor region and TAB region represented in the second image, providing the combined feature set to a machine learning classifier; receiving, from the classifier, a probability that the region of tissue will experience BCR or metastasis; and generating a classification of the region of tissue as likely to experience BCR or metastasis, or unlikely to experience BCR or metastasis.
    Type: Application
    Filed: May 18, 2018
    Publication date: November 22, 2018
    Inventors: Anant Madabhushi, Anna Gawlik, George Lee
  • Patent number: 10127660
    Abstract: Methods, apparatus, and other embodiments associated with predicting Crohn's Disease (CD) patient response to immunosuppressive (IS) therapy using radiomic features extracted from diagnostic magnetic resonance enterography (MRE). One example apparatus includes an image acquisition circuit that acquires an MRE image of a region of tissue demonstrating CD pathology, a segmentation circuit that segments a region of interest (ROI) from the diagnostic radiological image, a classification circuit that extracts a set of discriminative features from the ROI and that distinguishes the ROI as a responder or non-responder to IS therapy, and a CD prediction circuit that generates a radiomic enterographic (RET) score based on the diagnostic radiological image or the set of discriminative features. A prognosis or treatment plan may be provided based on the RET score.
    Type: Grant
    Filed: November 28, 2016
    Date of Patent: November 13, 2018
    Assignee: Case Western Reserve University
    Inventors: Anant Madabhushi, Cheng Lu, Satish Viswanath
  • Publication number: 20180322631
    Abstract: Methods, apparatus, and other embodiments associated with classifying a region of tissue represented in a digitized whole slide image (WSI) using iterative gradient-based quasi-Monte Carlo (QMC) sampling. One example apparatus includes an image acquisition circuit that acquires a WSI of a region of tissue demonstrating cancerous pathology, an adaptive sampling circuit that selects a subset of tiles from the WSI using an iterative QMC Sobol sequence sampling approach, an invasiveness circuit that determines a probability of a presence of invasive pathology in a member of the subset of tiles, a probability map circuit that generates an invasiveness probability map based on the probability, a probability gradient circuit that generates a gradient image based on the invasiveness probability map, and a classification circuit that classifies the region of tissue based on the probability map. A prognosis or treatment plan may be provided based on the classification of the WSI.
    Type: Application
    Filed: June 29, 2018
    Publication date: November 8, 2018
    Inventors: Anant Madabhushi, Angel Alfonso Cruz Roa, Fabio Gonzalez
  • Publication number: 20180276497
    Abstract: Embodiments predict prostate cancer (PCa) biochemical recurrence (BCR) employing an image acquisition circuit that accesses a pre-treatment image of a region of tissue demonstrating PCa; a segmentation circuit that segments a prostate capsule represented in the image; a registration circuit that registers the segmented prostate with a BCR? median template, and generates a registered surface of interest (SOI) mask by registering an SOI mask with the registered prostate; a mask circuit that generates a patient-specific SOI mask from the registered prostate and the registered SOI mask, and generates a patient-specific SOI mesh from the patient-specific SOI mask; a field effect induced organ distension (FOrge) circuit extracts a set of FOrge features from the patient-specific SOI mesh, and computes a probability that the region of tissue will experience BCR; and a classification circuit classifies the region of tissue as likely to experience BCR based on the probability.
    Type: Application
    Filed: March 13, 2018
    Publication date: September 27, 2018
    Inventors: Anant Madabhushi, Soumya Ghose
  • Publication number: 20180276498
    Abstract: Embodiments predict prostate cancer (PCa) biochemical recurrence (BCR) employing an image acquisition circuit that accesses a first pre-treatment image and a second pre-treatment image of a region of tissue demonstrating PCa, a distension feature circuit that extracts a set of distension features from the first pre-treatment image, and computes a first probability of PCa BCR based on the set of distension features, a radiomics circuit that extracts a set of radiomics features from the second pre-treatment image, and computes a second probability of PCa recurrence based on the set of radiomics feature, a combined tumor induced organ distension with tumor radiomics (COnTRa) circuit that computes a joint probability that the region of tissue will experience PCa BCR based on the first probability and the second probability, and a display circuit that displays the joint probability.
    Type: Application
    Filed: March 16, 2018
    Publication date: September 27, 2018
    Inventors: Anant Madabhushi, Rakesh Shiradkar, Soumya Ghose
  • Patent number: 10078895
    Abstract: Methods and apparatus predict non-small cell lung cancer (NSCLC) recurrence using radiomic features extracted from digitized hematoxylin and eosin (H&E) stained slides of a region of tissue demonstrating NSCLC. One example apparatus includes an image acquisition circuit that acquires an image of a region of tissue demonstrating NSCLC, a segmentation circuit that segments a cellular nucleus from the image, a feature extraction circuit that extracts a set of features from the image, a tumor infiltrating lymphocyte (TIL) identification circuit that classifies the segmented nucleus as a TIL or non-TIL, a graphing circuit that constructs a TIL graph and computes a set of TIL graph statistical features, and a classification circuit that computes a probability that the region will experience NSCLC recurrence. The classification circuit may compute a quantitative continuous image-based risk score based on the probability or the image. A treatment plan may be provided based on the risk score.
    Type: Grant
    Filed: December 23, 2016
    Date of Patent: September 18, 2018
    Assignee: Case Western Reserve University
    Inventors: Anant Madabhushi, Xiangxue Wang, Vamsidhar Velcheti, German Corredor Prada
  • Publication number: 20180253591
    Abstract: Embodiments include an image acquisition circuit configured to access an image of a region of tissue demonstrating cancerous pathology, a nuclei detection and graphing circuit configured to detect cellular nuclei represented in the image; and construct a nuclear sub-graph based on the detected cellular nuclei, where a node of the sub-graph is a nuclear centroid of a cellular nucleus; a cell run length (CRF) circuit configured to compute a CRF vector based on the sub-graph; compute a set of CRF features based on the CRF vector and the sub-graph; and generate a CRF signature based, at least in part, on the set of CRF features; and a classification circuit configured to compute a probability that the region of tissue will experience cancer progression, based, at least in part, on the CRF signature; and generate a classification of the region of tissue as a progressor or non-progressor.
    Type: Application
    Filed: February 21, 2018
    Publication date: September 6, 2018
    Inventors: Anant Madabhushi, Cheng Lu
  • Publication number: 20180253841
    Abstract: Embodiments include apparatus for predicting cancer recurrence based on local co-occurrence of cell morphology (LoCoM). The apparatus includes image acquisition circuitry that identifies and segments at least one cellular nucleus represented in an image of a region of tissue demonstrating cancerous pathology; local nuclei graph (LNG) circuitry that constructs an LNG based on the at least one cellular nucleus, and computes a set of nuclear morphology features for a nucleus represented in the LNG; LoCoM circuitry that constructs a co-occurrence matrix based on the nuclear morphology features, computes a set of LoCoM features for the co-occurrence matrix, and computes a LoCoM signature for the image based on the set of LoCoM features; progression circuitry that generates a probability that the region of tissue will experience cancer progression based on the LoCoM signature, and classifies the region of tissue as a progressor or non-progressor based on the probability.
    Type: Application
    Filed: February 19, 2018
    Publication date: September 6, 2018
    Inventors: Anant Madabhushi, Cheng Lu
  • Patent number: 10064594
    Abstract: Methods, apparatus, and other embodiments associated with classifying a region of tissue using quantified vessel tortuosity are described. One example apparatus includes an image acquisition logic that acquires an image of a region of tissue demonstrating cancerous pathology, a delineation logic that distinguishes nodule tissue within the image from the background of the image, a perinodular zone logic that defines a perinodular zone based on the nodule, a feature extraction logic that extracts a set of features from the image including a set of tortuosity features, a probability logic that computes a probability that the nodule is benign, and a classification logic that classifies the nodule tissue based, at least in part, on the set of features or the probability. A prognosis or treatment plan may be provided based on the classification of the image.
    Type: Grant
    Filed: August 2, 2016
    Date of Patent: September 4, 2018
    Assignee: Case Western Reserve University
    Inventors: Anant Madabhushi, Mahdi Orooji, Mirabela Rusu, Philip Linden, Robert Gilkeson, Nathaniel Mason Braman, Mehdi Alilou
  • Publication number: 20180242906
    Abstract: One embodiment includes an image acquisition circuit that accesses a pre-treatment and a post-treatment image of a region of tissue demonstrating non-small cell lung cancer (NSCLC), a segmentation and registration circuit that annotates the tumor represented in the images, and that registers the pre-treatment image with the post-treatment image; a feature extraction circuit that selects a set of pre-treatment and a set of post-treatment quantitative vessel tortuosity (QVT) features from the registered image; a delta-QVT circuit that generates a set of delta-QVT features by computing a difference between the set of post-treatment QVT features and the set of pre-treatment QVT features; and a classification circuit that generates a probability that the region of tissue will respond to immunotherapy based on the difference, and that classifies the region of tissue as a responder or non-responder. Embodiments may generate an immunotherapy treatment plan based on the classification.
    Type: Application
    Filed: February 9, 2018
    Publication date: August 30, 2018
    Inventors: Anant Madabhushi, Yuanqi Xie, Vamsidhar Velcheti
  • Publication number: 20180247410
    Abstract: One embodiment include an image acquisition circuit that accesses a pre-treatment and a post-treatment image of a region of tissue demonstrating non-small cell lung cancer (NSCLC), a segmentation and registration circuit that annotates the tumor represented in the images, and that registers the pre-treatment image with the post-treatment image; a feature extraction circuit that selects a set of pre-treatment and a set of post-treatment radiomic features from the registered image; a delta radiomics circuit that generates a set of delta radiomic features by computing a difference between the set of post-treatment radiomic features and the set of pre-treatment radiomic features; and a classification circuit that generates a probability that the region of tissue will respond to immunotherapy based on the difference, and that classifies the region of tissue as a responder or non-responder. Embodiments may generate an immunotherapy treatment plan based, at least in part, on the classification.
    Type: Application
    Filed: December 22, 2017
    Publication date: August 30, 2018
    Inventors: Anant Madabhushi, Yuanqi Xie, Vamsidhar Velcheti
  • Publication number: 20180242905
    Abstract: Embodiments classify a region of tissue demonstrating non-small cell lung cancer using quantified vessel tortuosity (QVT). One example apparatus includes annotation circuitry configured to segment a lung region from surrounding anatomy in the region of tissue represented in a radiological image and segment a nodule from the lung region by defining a nodule boundary; vascular segmentation circuitry configured to generate a three dimensional (3D) segmented vasculature by segmenting a vessel associated with the nodule, and to identify a center line of the 3D segmented vasculature; QVT feature extraction circuitry configured to extract a set of QVT features from the radiological image; and classification circuitry configured to compute a probability that the region of tissue will respond to immunotherapy and generate a classification that the region of tissue is a responder or a non-responder based, at least in part, on the probability.
    Type: Application
    Filed: January 30, 2018
    Publication date: August 30, 2018
    Inventors: Anant Madabhushi, Mehdi Alilou, Vamsidhar Velcheti
  • Patent number: 10055842
    Abstract: Methods, apparatus, and other embodiments distinguish disease phenotypes and mutational status using co-occurrence of local anisotropic gradient orientations (CoLIAGe) and Laws features. One example apparatus includes a set of circuits that acquires a radiologic image (e.g., MRI image) of a region of tissue demonstrating breast cancer, computes a gradient orientation for a pixel in the MRI image, computes a significant orientation for the pixel based on the gradient orientation, constructs a feature vector that captures a discretized entropy distribution for the image based on the significant orientation, extracts a set of texture features from the MRI image, and classifies the phenotype of the breast cancer based on the feature vector and the set of texture features. Embodiments of example apparatus may generate and display a heatmap of entropy values for the image. Example methods and apparatus may operate substantially in real-time, or may operate in two, three, or more dimensions.
    Type: Grant
    Filed: January 3, 2017
    Date of Patent: August 21, 2018
    Assignee: Case Western Reserve University
    Inventors: Prateek Prasanna, Nathaniel Braman, Anant Madabhushi, Vinay Varadan, Lyndsay Harris, Salendra Singh