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公开(公告)号：US20200074701A1
公开(公告)日：2020-03-05
申请号：US16556135
申请日：2019-08-29
Inventor： Jianming Liang , Nima Tajbakhsh , Jaeyul Shin
Abstract: Detecting a pulmonary embolism (PE) in an image dataset of a blood vessel involves obtaining a volume of interest (VOI) in the blood vessel, generating a plurality of PE candidates within the VOI, generating a set of voxels for each PE candidate, estimating for each PE candidate an orientation of the blood vessel that contains the PE candidate, given the set of voxels for the PE candidate, and generating a visualization of the blood vessel that contains the PE candidate using the estimated orientation of the blood vessel that contains the PE candidate.
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公开(公告)号：US20200074271A1
公开(公告)日：2020-03-05
申请号：US16556130
申请日：2019-08-29
Inventor： Jianming Liang , Zongwei Zhou , Md Mahfuzur Rahman Siddiquee , Nima Tajbakhsh
Abstract: Disclosed are provided systems, methods, and apparatuses for implementing a multi-resolution neural network for use with imaging intensive applications including medical imaging. For example, a system having means to execute a neural network model formed from a plurality of layer blocks including an encoder layer block which precedes a plurality of decoder layer blocks includes: means for associating a resolution value with each of the plurality of layer blocks; means for processing via the encoder layer block a respective layer block input including a down-sampled layer block output processing, via decoder layer blocks, a respective layer block input including an up-sampled layer block output and a layer block output of a previous layer block associated with a prior resolution value of a layer block which precedes the respective decoder layer block; and generating the respective layer block output by summing or concatenating the processed layer block inputs.
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13.发明授权公开(公告)号：US10157467B2
公开(公告)日：2018-12-18
申请号：US15231730
申请日：2016-08-08
Applicant: Arizona Board of Regents on Behalf of Arizona State University , Mayo Foundation For Medical Education And Research
Inventor： Esra Dincer , Michael Gotway , Jianming Liang
Abstract: A system and method for detecting central pulmonary embolisms in a subject's vasculature is provided. In some aspects, the method includes receiving, using the input, a set of images representing a vasculature of the subject's lungs, automatically analyzing the set of images to segment the main arteries associated with the subject's lungs and separate the main arteries from surrounding tissues. The method also includes automatically extracting central pulmonary embolism candidates from the set of images after segmenting and separating the main arteries, and automatically evaluating the central pulmonary embolism candidates in three-dimensional (3D) space by applying a series of rules. The method further includes automatically displaying a report indicating evaluated central pulmonary embolism candidates on a display.
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公开(公告)号：US10055843B2
公开(公告)日：2018-08-21
申请号：US15562088
申请日：2016-03-31
Applicant: MAYO FOUNDATION FOR MEDICAL EDUCATION AND RESEARCH , ARIZONA BOARD OF REGENTS on Behalf of Arizona State University
Inventor： Nima Tajbakhsh , Suryakanth R. Gurudu , Jianming Liang
CPC classification number: G06T7/0012 , A61B5/0084 , A61B5/4255 , A61B5/6852 , A61B5/6873 , A61B2576/02 , G06T1/0007 , G06T7/40 , G06T2207/10016 , G06T2207/10068 , G06T2207/30032
Abstract: A system and methods for detecting polyps using optical images acquired during a colonoscopy. In some aspects, a method includes receiving the set of optical images from the input and generating polyp candidates by analyzing the received set of optical images. The method also includes generating a plurality of image patches around locations associated with each polyp candidate, applying a set of convolutional neural networks to the corresponding image patches, and computing probabilities indicative of a maximum response for each convolutional neural network. The method further includes identifying polyps using the computed probabilities for each polyp candidate, and generating a report indicating identified polyps.
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公开(公告)号：US09924927B2
公开(公告)日：2018-03-27
申请号：US15049935
申请日：2016-02-22
Inventor： Jae Yul Shin , Nima Tajbakhsh , Jianming Liang
IPC: A61B8/00 , A61B8/08 , A61B5/0402 , A61B5/00 , A61B5/0456 , A61B5/107 , A61B5/02
CPC classification number: A61B8/5284 , A61B5/02007 , A61B5/0402 , A61B5/0456 , A61B5/1075 , A61B5/489 , A61B5/7267 , A61B5/7289 , A61B5/7485 , A61B8/085 , A61B8/0891 , A61B8/469 , A61B8/5223 , A61B8/5292
Abstract: A system for automatically determining a thickness of a wall of an artery of a subject includes an ECG monitoring device that captures an electrocardiogram (ECG) signal from the subject, and an ultrasound video imaging device, coupled to the ECG monitoring device, that receives the ECG signal from the ECG monitoring device, and captures a corresponding ultrasound video of the wall of the artery of the subject. The system produces a plurality of frames of video comprising the ultrasound video of the wall of the artery of the subject and an image of the ECG signal. A processor is configured to select a subset of the plurality of frames of the ultrasound video based on the image of the (ECG) signal, locate automatically a region of interest (ROI) in each frame of the subset of the plurality of frames of the video using a machine-based artificial neural network and measure automatically a thickness of the wall of the artery in each ROI using the machine-based artificial neural network.
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Patent Agency Ranking
公开(公告)号：US12229920B2
公开(公告)日：2025-02-18
申请号：US17477088
申请日：2021-09-16
Inventor： Jianming Liang , Zongwei Zhou , Nima Tajbakhsh , Md Mahfuzur Rahman Siddiquee
Abstract: Described herein are means for implementing fixed-point image-to-image translation using improved Generative Adversarial Networks (GANs). For instance, an exemplary system is specially configured for implementing a new framework, called a Fixed-Point GAN, which improves upon prior known methodologies by enhancing the quality of the images generated through global, local, and identity transformation. The Fixed-Point GAN as introduced and described herein, improves many applications dependant on image-to-image translation, including those in the field of medical image processing for the purposes of disease detection and localization. Other related embodiments are disclosed.
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公开(公告)号：US20250014721A1
公开(公告)日：2025-01-09
申请号：US18761131
申请日：2024-07-01
Inventor： ZiYu Fan , Jianming Liang
IPC: G16H30/40 , G06T5/40 , G06T5/92 , G06V10/26 , G06V10/32 , G06V10/764 , G06V10/774 , G06V10/82
Abstract: A generic unified deep model for learning from multiple tasks, in the context of medical image analysis includes means for receiving a training dataset of medical images; training the AI model to generate a trained AI model using a pre-processing operation, a Swin Transformer-based segmentation operation, and a post-processing operation, in which application of a Non-Maximum Suppression (NMS) algorithm generates object detection and classification output parameters for the AI model by removing overlapping detections and selecting a best set of detections according to a determined confidence score for the detections remaining; and outputting the trained AI model for use with medical image analysis.
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公开(公告)号：US20240412367A1
公开(公告)日：2024-12-12
申请号：US18674642
申请日：2024-05-24
Inventor： Jianming Liang
IPC: G06T7/00 , G06T7/11 , G06V10/764 , G06V10/778
Abstract: A system implements self-supervised learning through contrastive learning using an image transformer. The transformer receives medical images for training an Artificial Intelligence (AI) model, and executes a first cropping and prediction operation by (i) cropping a first patch P from a first random location L from an image A selected from the plurality of medical images and (ii) training a classification head to predict that the first patch P is part of the image A. The transformer executes a second cropping and prediction operation by (iii) cropping a second patch P from a second random location L from the image A selected from the plurality of medical images and (iv) training the classification head to predict that the second patch P forms no part of an image B selected from the plurality of medical images. The transformer issues a determination that the image B is different than the image A.
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19.发明授权公开(公告)号：US12094190B2
公开(公告)日：2024-09-17
申请号：US17675929
申请日：2022-02-18
Inventor： Diksha Goyal , Jianming Liang
IPC: G06V10/778 , G06T7/00 , G06T7/11 , G06T7/194 , G06V10/774 , G06V10/82
CPC classification number: G06V10/7788 , G06T7/0012 , G06T7/11 , G06T7/194 , G06V10/7747 , G06V10/82 , G06T2200/24 , G06T2207/20081 , G06T2207/20084 , G06T2207/20092 , G06T2207/30004
Abstract: Medical image segmentation using interactive refinement, in which the trained deep models are then utilized for the processing of medical imaging are described. Operating a two-step deep learning training framework including receiving original input images at the deep learning training framework; generating an initial prediction image specifying image segmentation by base segmentation model; receiving user input guidance signals; routing each of (i) the original input images, (ii) the initial prediction image, and (iii) the user input guidance signals to an InterCNN; generating a refined prediction image specifying refined image segmentation by processing each of the (i) the original input images, (ii) the initial prediction image, and (iii) the user input guidance signals through the InterCNN to render the refined prediction image incorporating the user input guidance signals; and outputting a refined segmentation mask to the deep learning training framework as a guidance signal.
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公开(公告)号：US11922628B2
公开(公告)日：2024-03-05
申请号：US17224886
申请日：2021-04-07
Inventor： Zongwei Zhou , Vatsal Sodha , Jiaxuan Pang , Jianming Liang
IPC: G06V10/00 , G06F18/21 , G06F18/214 , G06N3/045 , G06N3/088 , G06T3/00 , G06T7/11 , G06V10/26 , G06V10/77 , G16H30/40
CPC classification number: G06T7/11 , G06F18/2155 , G06F18/2163 , G06N3/045 , G06N3/088 , G06T3/00 , G06V10/26 , G06V10/7715 , G16H30/40 , G06V2201/03
Abstract: Described herein are means for generation of self-taught generic models, named Models Genesis, without requiring any manual labeling, in which the Models Genesis are then utilized for the processing of medical imaging. For instance, an exemplary system is specially configured for learning general-purpose image representations by recovering original sub-volumes of 3D input images from transformed 3D images. Such a system operates by cropping a sub-volume from each 3D input image; performing image transformations upon each of the sub-volumes cropped from the 3D input images to generate transformed sub-volumes; and training an encoder-decoder architecture with skip connections to learn a common image representation by restoring the original sub-volumes cropped from the 3D input images from the transformed sub-volumes generated via the image transformations. A pre-trained 3D generic model is thus provided, based on the trained encoder-decoder architecture having learned the common image representation which is capable of identifying anatomical patterns in never before seen 3D medical images having no labeling and no annotation. More importantly, the pre-trained generic models lead to improved performance in multiple target tasks, effective across diseases, organs, datasets, and modalities.
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