Carestream Health, Inc.

Tencent America LLC

Zhimin Huo

Jing Jing Zhang

Wei Fan

Lianyi Han

Hui Tang

David H Foos

Shih-Yao Lin

Xiaohui Wang

Chao Huang

Yusheng Xie

Chang-Ying Joseph Yang

Hui Zhao

Shoupu Chen

Minjie Chen

Qian Zhen

Fan Xu

William J Sehnert

Hui Luo

Kun Wang

Xiaozhong Chen

Xu Liu

Mark S Purdum

Lawrence Allen Ray

Jiebo Luo

Richard Alan Simon

Samuel Richard

Tao Yang

Mary E Couwenhoven

Marco Bucci

Wei Hao

Patrick Bernard Heffernan

Qinran Chen

Zhen Qian

Ruiping Li

Daoxian Heidi Zhang

Frank Zhao

Lynn M La Pietra

Shihyao Lin

Zhiqiang Lao

Jianqing Y Bennett

Huihai Lu

Ke Nie

Mantao Xu

Hui Wang

A method, computer program, and computer system are provided for image segmentation. Image data, such as biological image data, is received. One or more objects associated with the received image data is detected. One or more regions of interest are determined within the receive image data corresponding to one or more segments based on the detected objects.

System and method for instance segmentation in pathological images using object-guided framework

A method and user device for determining a unified Parkinson's disease rating scale (UPDRS) value associated with a user of the user device include obtaining video data associated with a movement of a body part of the user. The UPDRS value is determined using a model and the video data associated with the movement of the body part of the user. The UPDRS value is provided to permit an evaluation of the user based on the UPDRS value.

User device based Parkinson's disease detection

A method and user device for providing a unified Parkinson's disease rating scale (UPDRS) value by a user device includes generating, by a sensor of the user device, sensor data based on a user of the user device manipulating the user device. The UPDRS value is determined based on the sensor data and a deep neural network (DNN) model. The UPDRS value is provided to permit an evaluation of the user based on the UPDRS value.

User device based parkinson disease detection

There is included an apparatus and system including video capture code configured to control a camera of a mobile phone to capture a video of at least a portion of skin, extraction code configured to extract at least one photoplethysmogram (PPG) signal from the video, determination code configured to determine a peripheral oxygen saturation (SpO2) value based on the PPG signal with respect to a blood flow at the portion of skin, and display code configured to control a display of the mobile phone to display the SpO2 value.

SpO2 miniprogram: AI SpO2 measurement app

A method and apparatus include receiving a three dimensional (3D) non-contrast computed tomography (NCCT) image of a head including a hematoma. A plurality of two dimensional (2D) images of the head including the hematoma are generated using the 3D NCCT image of the head including the hematoma. A plurality of 2D hematoma images are generated using a first 2D convolutional neural network (CNN) based on the plurality of 2D images. A 3D region of interest (ROI) that encompasses the hematoma is identified based on the plurality of 2D hematoma images. A plurality of 2D images that correspond to the ROI are generated. A hematoma expansion (HE) prediction score is determined using a second CNN based on the plurality of 2D images that correspond to the ROI. The HE prediction score is provided.

Two and a half dimensional convolutional neural network for predicting hematoma expansion in non-contrast head computerized tomography images

A system and method for generating a rib suppressed radiographic image using deep learning computation. The method includes using a convolutional neural network module trained with pairs of a chest x-ray image and its counterpart bone suppressed image. The bone suppressed image is obtained using a bone suppression algorithm applied to the chest x-ray image. The convolutional neural network module is then applied to a chest x-ray image or the bone suppressed image to generate an enhanced bone suppressed image.

Bone suppression for chest radiographs using deep learning

A method acquires a reconstructed tomographic volume image of anatomy of a patient and constructs a primary axis for a bone of interest in the imaged anatomy. The method forms a sectioned image of the bone in the volume image according to a first plane that is defined to extend along the bone and to extend through two or more articular surfaces. A primary axis for the bone is estimated, wherein the primary axis is midway between outer edges of the bone image that intersect the first sectioning plane. The bone is sectioned in the volume image by a second plane that is orthogonal to the first plane and that extends through the estimated primary axis. The method recalculates the position of the constructed primary axis according to the sectioning of the bone by the second plane. The recalculated constructed primary axis for the bone of interest is displayed.

Bone segmentation and display for 3D extremity imaging

A method for processing volume image data obtains two-dimensional projection images of a subject on an image detector, wherein each of the images has the detector and a radiation source at a different scan angles. The image data is arranged as an image stack with corresponding pixel data from the detector in register for each of the images in the stack. A partial subset of projection images showing a metal object is identified. Information relative to the metal object propagates from the partial subject of projection images to the remaining images of the stack. For each of one or more stacked projection images, region growing defines a metal mask and adjusts image data values defined by the mask. The volume image data is reconstructed using region growing results. A 2D image is rendered and displayed from the reconstructed volume image data.

Metal artifacts reduction for cone beam CT

A method for processing a radiographic image of a patient, executed at least in part on a host processor, initiates exposure in response to an instruction entered from an operator console and obtains radiographic image data from a digital detector that is subjected to the exposure. The obtained radiographic image data is analyzed according to a set of predefined criteria for diagnostic suitability of the image. One or more results of the diagnostic suitability analysis at the operator console is indicated and a listing of one or more corrective actions at the operator console according to the indicated results is provided.

Integrated portable digital x-ray imaging system

A method for clavicle suppression in a chest x-ray image. The method identifies the lung fields in the x-ray image and detects at least one portion of a clavicle ridge that lies outside the lung fields. Edges of a clavicle on each side of the detected clavicle ridge are detected, edge detection extended for the clavicle edges into the lung fields, and the clavicle defined within the x-ray image according to the edge detection. The clavicle is suppressed within the x-ray image to generate a clavicle-suppressed x-ray image and the clavicle-suppressed x-ray image is displayed, stored, or transmitted.

Clavicle suppression in radiographic images

A method for rib suppression in a volume chest x-ray image, executed at least in part by a computer captures a first set of unsegmented projection images, each at a corresponding angle, and forms a second set of segmented projection images by detecting rib features in a first projection image to form a first segmented projection image and generating a base model according to the detected rib features for the first projection image. Each of one or more additional projection images from the first set is processed to add members to the second set by a repeated sequence of generating a predictive model; detecting rib features using the predictive model; adjusting the base model according to detected rib features; and correcting rib detection in one or more members of the second set. The volume chest x-ray image is reconstructed according to the segmented projection images and is displayed.

Tomosynthesis reconstruction with rib suppression

Integrated portable digital X-ray imaging system

Growing community of inventors

Pittsford, NY, United States of America

Zhimin Huo