Patents by Inventor Hok Kan Lau
Hok Kan Lau 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).
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Patent number: 11854703Abstract: Systems and methods for providing a novel framework to simulate the appearance of pathology on patients who otherwise lack that pathology. The systems and methods include a “simulator” that is a generative adversarial network (GAN). Rather than generating images from scratch, the systems and methods discussed herein simulate the addition of diseases-like appearance on existing scans of healthy patients. Focusing on simulating added abnormalities, as opposed to simulating an entire image, significantly reduces the difficulty of training GANs and produces results that more closely resemble actual, unmodified images. In at least some implementations, multiple GANs are used to simulate pathological tissues on scans of healthy patients to artificially increase the amount of available scans with abnormalities to address the issue of data imbalance with rare pathologies.Type: GrantFiled: June 10, 2019Date of Patent: December 26, 2023Assignee: ARTERYS INC.Inventors: Hok Kan Lau, Jesse Lieman-Sifry, Sean Patrick Sall, Berk Dell Norman, Daniel Irving Golden, John Axerio-Cilies, Matthew Joseph Didonato
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Publication number: 20230106440Abstract: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.Type: ApplicationFiled: December 8, 2022Publication date: April 6, 2023Inventors: Daniel Irving GOLDEN, Fabien Rafael David BECKERS, John AXERIO-CILIES, Matthieu LE, Jesse LIEMAN-SIFRY, Anitha Priya KRISHNAN, Sean Patrick SALL, Hok Kan LAU, Matthew Joseph DIDONATO, Robert George NEWTON, Torin Arni TAERUM, Shek Bun LAW, Carla Rosa LEIBOWITZ, Angélique Sophie CALMON
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Patent number: 11551353Abstract: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.Type: GrantFiled: November 15, 2018Date of Patent: January 10, 2023Assignee: Arterys Inc.Inventors: Daniel Irving Golden, Fabien Rafael David Beckers, John Axerio-Cilies, Matthieu Le, Jesse Lieman-Sifry, Anitha Priya Krishnan, Sean Patrick Sall, Hok Kan Lau, Matthew Joseph Didonato, Robert George Newton, Torin Arni Taerum, Shek Bun Law, Carla Rosa Leibowitz, Angélique Sophie Calmon
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Publication number: 20220155398Abstract: Systems and methods for providing improved eddy current correction (ECC) in medical imaging environments. One or more of the embodiments disclosed herein provide a deep learning-based convolutional neural network (CNN) model trained to automatically generate an ECC mask which may be composited with two-dimensional (2D) scan slices or four-dimensional (4D) scan slices and made viewable through, for example, a web application, and made manipulable through a user interface thereof.Type: ApplicationFiled: February 11, 2020Publication date: May 19, 2022Inventors: Berk Dell NORMAN, Jesse LIEMAN-SIFRY, Sean Patrick SALL, Daniel Irving GOLDEN, Hok Kan LAU
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Publication number: 20210249142Abstract: Systems and methods for providing a novel framework to simulate the appearance of pathology on patients who otherwise lack that pathology. The systems and methods include a “simulator” that is a generative adversarial network (GAN). Rather than generating images from scratch, the systems and methods discussed herein simulate the addition of diseases-like appearance on existing scans of healthy patients. Focusing on simulating added abnormalities, as opposed to simulating an entire image, significantly reduces the difficulty of training GANs and produces results that more closely resemble actual, unmodified images. In at least some implementations, multiple GANs are used to simulate pathological tissues on scans of healthy patients to artificially increase the amount of available scans with abnormalities to address the issue of data imbalance with rare pathologies.Type: ApplicationFiled: June 10, 2019Publication date: August 12, 2021Inventors: Hok Kan LAU, Jesse LIEMAN-SIFRY, Sean Patrick SALL, Berk Dell NORMAN, Daniel Irving GOLDEN, John AXERIO-CILIES, Matthew Joseph DIDONATO
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Publication number: 20210216878Abstract: Systems and methods for providing a novel framework for unsupervised coregistration using convolutional neural network (CNN) models. The CNN models may perform image coregistration using fully unsupervised learning. Advantageously, the CNN models may also explicitly stabilizes images or transfers contour masks across images. Global alignment may be learned via affine deformations in addition to a dense deformation field, and an unsupervised loss function may be maintained. The CNN models may apply an additional spatial transformation layer at the end of a transformation step, which provides the ability to fine-tune previously predicted transformation so that the CNN models may correct previous transformation errors.Type: ApplicationFiled: August 21, 2019Publication date: July 15, 2021Inventors: Berk Dell NORMAN, Sean Patrick SALL, Jesse LIEMAN-SIFRY, Martin SIMONOVSKY, Daniel Irving GOLDEN, Hok Kan LAU
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Patent number: 10902598Abstract: Systems and methods for automated segmentation of anatomical structures (e.g., heart). Convolutional neural networks (CNNs) may be employed to autonomously segment parts of an anatomical structure represented by image data, such as 3D MRI data. The CNN utilizes two paths, a contracting path and an expanding path. In at least some implementations, the expanding path includes fewer convolution operations than the contracting path. Systems and methods also autonomously calculate an image intensity threshold that differentiates blood from papillary and trabeculae muscles in the interior of an endocardium contour, and autonomously apply the image intensity threshold to define a contour or mask that describes the boundary of the papillary and trabeculae muscles. Systems and methods also calculate contours or masks delineating the endocardium and epicardium using the trained CNN model, and anatomically localize pathologies or functional characteristics of the myocardial muscle using the calculated contours or masks.Type: GrantFiled: January 25, 2018Date of Patent: January 26, 2021Assignee: Arterys Inc.Inventors: Daniel Irving Golden, Matthieu Le, Jesse Lieman-Sifry, Hok Kan Lau
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Publication number: 20200380675Abstract: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.Type: ApplicationFiled: November 15, 2018Publication date: December 3, 2020Inventors: Daniel Irving GOLDEN, Fabien Rafael David BECKERS, John AXERIO-CILIES, Matthieu LE, Jesse LIEMAN-SIFRY, Anitha Priya KRISHNAN, Sean Patrick SALL, Hok Kan LAU, Matthew Joseph DIDONATO, Robert George NEWTON, Torin Arni TAERUM, Shek Bun LAW, Carla Rosa LEIBOWITZ, Angélique Sophie CALMON
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Publication number: 20200193603Abstract: Systems and methods for automated segmentation of anatomical structures (e.g., heart). Convolutional neural networks (CNNs) may be employed to autonomously segment parts of an anatomical structure represented by image data, such as 3D MRI data. The CNN utilizes two paths, a contracting path and an expanding path. In at least some implementations, the expanding path includes fewer convolution operations than the contracting path. Systems and methods also autonomously calculate an image intensity threshold that differentiates blood from papillary and trabeculae muscles in the interior of an endocardium contour, and autonomously apply the image intensity threshold to define a contour or mask that describes the boundary of the papillary and trabeculae muscles. Systems and methods also calculate contours or masks delineating the endocardium and epicardium using the trained CNN model, and anatomically localize pathologies or functional characteristics of the myocardial muscle using the calculated contours or masks.Type: ApplicationFiled: February 25, 2020Publication date: June 18, 2020Inventors: Daniel Irving Golden, Matthieu Le, Jesse Lieman-Sifry, Hok Kan Lau
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Patent number: 10600184Abstract: Systems and methods for automated segmentation of anatomical structures (e.g., heart). Convolutional neural networks (CNNs) may be employed to autonomously segment parts of an anatomical structure represented by image data, such as 3D MRI data. The CNN utilizes two paths, a contracting path and an expanding path. In at least some implementations, the expanding path includes fewer convolution operations than the contracting path. Systems and methods also autonomously calculate an image intensity threshold that differentiates blood from papillary and trabeculae muscles in the interior of an endocardium contour, and autonomously apply the image intensity threshold to define a contour or mask that describes the boundary of the papillary and trabeculae muscles. Systems and methods also calculate contours or masks delineating the endocardium and epicardium using the trained CNN model, and anatomically localize pathologies or functional characteristics of the myocardial muscle using the calculated contours or masks.Type: GrantFiled: January 25, 2018Date of Patent: March 24, 2020Assignee: ARTERYS INC.Inventors: Daniel Irving Golden, Matthieu Le, Jesse Lieman-Sifry, Hok Kan Lau
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Publication number: 20200085382Abstract: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.Type: ApplicationFiled: May 30, 2018Publication date: March 19, 2020Inventors: Torin Arni Taerum, Hok Kan Lau, Sean Sall, Matthieu Le, John Axerio-Cilies, Daniel Irving Golden, Jesse Lieman-Sifry, Tristan Jugdev
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Publication number: 20180218497Abstract: Systems and methods for automated segmentation of anatomical structures (e.g., heart). Convolutional neural networks (CNNs) may be employed to autonomously segment parts of an anatomical structure represented by image data, such as 3D MRI data. The CNN utilizes two paths, a contracting path and an expanding path. In at least some implementations, the expanding path includes fewer convolution operations than the contracting path. Systems and methods also autonomously calculate an image intensity threshold that differentiates blood from papillary and trabeculae muscles in the interior of an endocardium contour, and autonomously apply the image intensity threshold to define a contour or mask that describes the boundary of the papillary and trabeculae muscles. Systems and methods also calculate contours or masks delineating the endocardium and epicardium using the trained CNN model, and anatomically localize pathologies or functional characteristics of the myocardial muscle using the calculated contours or masks.Type: ApplicationFiled: January 25, 2018Publication date: August 2, 2018Inventors: Daniel Irving Golden, Matthieu Le, Jesse Lieman-Sifry, Hok Kan Lau
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Publication number: 20180218502Abstract: Systems and methods for automated segmentation of anatomical structures (e.g., heart). Convolutional neural networks (CNNs) may be employed to autonomously segment parts of an anatomical structure represented by image data, such as 3D MRI data. The CNN utilizes two paths, a contracting path and an expanding path. In at least some implementations, the expanding path includes fewer convolution operations than the contracting path. Systems and methods also autonomously calculate an image intensity threshold that differentiates blood from papillary and trabeculae muscles in the interior of an endocardium contour, and autonomously apply the image intensity threshold to define a contour or mask that describes the boundary of the papillary and trabeculae muscles. Systems and methods also calculate contours or masks delineating the endocardium and epicardium using the trained CNN model, and anatomically localize pathologies or functional characteristics of the myocardial muscle using the calculated contours or masks.Type: ApplicationFiled: January 25, 2018Publication date: August 2, 2018Inventors: Daniel Irving Golden, Matthieu Le, Jesse Lieman-Sifry, Hok Kan Lau