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公开(公告)号：US11160528B2
公开(公告)日：2021-11-02
申请号：US15372688
申请日：2016-12-08
Applicant: General Electric Company
Inventor： Chandan Kumar Mallappa Aladahalli , Krishna Seetharam Shriram , Vivek Prabhakar Vaidya , Arathi Sreekumari , Jiayu Chen , Hidenori Shikata
Abstract: A method for assisted reading of automated ultrasound image volumes includes receiving a plurality of scan images generated from an imaging device, wherein the plurality of scan images comprises a chest wall region. The method further includes determining a chest wall model representative of the chest wall region based on the plurality of scan images. The method also includes determining a plurality of segmented scan images segmented along the chest wall region based on the chest wall model. In addition, the method includes determining lesion information using an automated lesion detection technique applied to the plurality of segmented scan images. The method also includes displaying the plurality of scan images along with at least one of the lesion information and the chest wall model.
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公开(公告)号：US11017269B2
公开(公告)日：2021-05-25
申请号：US16334091
申请日：2017-06-21
Applicant: General Electric Company
Inventor： Sheshadri Thiruvenkadam , Sohan Rashmi Ranjan , Vivek Prabhakar Vaidya , Hariharan Ravishankar , Rahul Venkataramani , Prasad Sudhakar
Abstract: A method for determining optimized deep learning architecture includes receiving a plurality of training images and a plurality of real time images corresponding to a subject. The method further includes receiving, by a medical practitioner, a plurality of learning parameters comprising a plurality of filter classes and a plurality of architecture parameters. The method also includes determining a deep learning model based on the plurality of learning parameters and the plurality of training images, wherein the deep learning model comprises a plurality of reusable filters. The method further includes determining a health condition of the subject based on the plurality of real time images and the deep learning model. The method also includes providing the health condition of the subject to the medical practitioner.
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公开(公告)号：US09277902B2
公开(公告)日：2016-03-08
申请号：US14088068
申请日：2013-11-22
Applicant: General Electric Company
Inventor： Rakesh Mullick , Vivek Prabhakar Vaidya , Fei Zhao , Xiaoxing Li , Vidya Kamath , Kunlin Cao , Soma Biswas
CPC classification number: A61B8/5223 , A61B8/0825 , A61B8/085 , A61B8/14 , A61B8/4483 , G06T7/0012 , G06T7/13 , G06T7/44 , G06T2207/10132 , G06T2207/30068 , G06T2207/30096
Abstract: A method is provided for detecting lesions in ultrasound images. The method includes acquiring an ultrasound image, generating a Fisher-tippett (FT) distribution-based edge feature map from the acquired ultrasound image, generating gradient concentration (GC) scores for pixels of the acquired ultrasound image using the FT distribution-based edge feature map, and identifying a candidate lesion region within the acquired ultrasound image based on the GC scores.
Abstract translation: 提供了一种用于检测超声图像中的损伤的方法。 该方法包括获取超声图像，从获取的超声图像生成基于Fisher-tippett（FT）分布的边缘特征图，使用基于FT分布的边缘特征产生所获取的超声图像的像素的梯度浓度（GC）分数 基于GC分数绘制并识别所获取的超声图像内的候选病变区域。
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公开(公告)号：US10997724B2
公开(公告)日：2021-05-04
申请号：US16469373
申请日：2017-12-14
Applicant: General Electric Company
Inventor： Hariharan Ravishankar , Vivek Prabhakar Vaidya , Sheshadri Thiruvenkadam , Rahul Venkataramani , Prasad Sudhakar
Abstract: A method for image segmentation includes receiving an input image (102). The method further includes obtaining a deep learning model (104) having a triad of predictors (116, 118, 120). Furthermore, the method includes processing the input image by a shape model in the triad of predictors (116, 118, 120) to generate a segmented shape image (110). Moreover, the method includes presenting the segmented shape image via a display unit (128).
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公开(公告)号：US20190266448A1
公开(公告)日：2019-08-29
申请号：US16334091
申请日：2017-06-21
Applicant: General Electric Company
Inventor： Sheshadri Thiruvenkadam , Sohan Rashmi Ranjan , Vivek Prabhakar Vaidya , Hariharan Ravishankar , Rahul Venkataramani , Prasad Sudhakar
Abstract: A method for determining optimized deep learning architecture includes receiving a plurality of training images and a plurality of real time images corresponding to a subject. The method further includes receiving, by a medical practitioner, a plurality of learning parameters comprising a plurality of filter classes and a plurality of architecture parameters. The method also includes determining a deep learning model based on the plurality of learning parameters and the plurality of training images, wherein the deep learning model comprises a plurality of reusable filters. The method further includes determining a health condition of the subject based on the plurality of real time images and the deep learning model. The method also includes providing the health condition of the subject to the medical practitioner.
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Patent Agency Ranking
公开(公告)号：US20150282782A1
公开(公告)日：2015-10-08
申请号：US14247265
申请日：2014-04-08
Applicant: General Electric Company
Inventor： Fei Zhao , Vivek Prabhakar Vaidya , Rakesh Mullick , Soma Biswas , Chuyang Ye
IPC: A61B8/08
CPC classification number: A61B8/0825 , A61B8/483 , A61B8/5207 , A61B8/5223 , A61B8/5269 , G06F19/321 , G06T7/0012 , G06T7/12 , G06T2207/10136 , G06T2207/20116 , G06T2207/30068 , G16H50/20
Abstract: A method for detecting a lesion in an anatomical region of interest is presented. The method includes identifying one or more candidate mass regions in each of a plurality of 3D ultrasound images acquired at different view angles from the anatomical region of interest. Single-view features corresponding to each candidate mass region are identified. For a candidate mass region, a similarity metric between the single-view features corresponding to the candidate mass region and the single-view features corresponding to the other candidate mass regions is determined. The candidate mass region is classified based at least on the similarity metric. A system for imaging and a non-transitory computer readable media for detection of the lesion are also presented.
Abstract translation: 提出了一种用于检测感兴趣的解剖学区域中的病变的方法。 该方法包括识别从与感兴趣的解剖区域不同的视角获取的多个3D超声图像中的每一个中的一个或多个候选质量区域。 识别与每个候选质量区域对应的单视图特征。 对于候选质量区域，确定对应于候选质量区域的单视图特征与对应于其他候选质量区域的单视图特征之间的相似性度量。 至少基于相似性度量对候选质量区进行分类。 还提出了用于成像的系统和用于检测病变的非暂时计算机可读介质。
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公开(公告)号：US10238368B2
公开(公告)日：2019-03-26
申请号：US14033452
申请日：2013-09-21
Applicant: General Electric Company
Inventor： Rakesh Mullick , Pavan Kumar Veerabhadra Annangi , Xiaoxing Li , Vidya Pundalik Kamath , Fei Zhao , Vivek Prabhakar Vaidya , Soma Biswas
Abstract: A method is provided for detecting lesions in ultrasound images. The method includes acquiring ultrasound information, determining discriminative descriptors that describe the texture of a candidate lesion region, and classifying each of the discriminative descriptors as one of a top boundary pixel, a lesion interior pixel, a lower boundary pixel, or a normal tissue pixel. The method also includes determining a pattern of transitions between the classified discriminative descriptors, and classifying the candidate lesion region as a lesion or normal tissue based on the pattern of transitions between the classified discriminative descriptors.
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公开(公告)号：US20170172540A1
公开(公告)日：2017-06-22
申请号：US15372688
申请日：2016-12-08
Applicant: General Electric Company
Inventor： Chandan Kumar Mallappa Aladahalli , Krishna Seetharam Shriram , Vivek Prabhakar Vaidya , Arathi Sreekumari , Jiayu Chen , Hidenori Shikata
CPC classification number: A61B8/0858 , A61B8/085 , A61B8/463 , A61B8/5223 , A61B8/56
Abstract: A method for assisted reading of automated ultrasound image volumes includes receiving a plurality of scan images generated from an imaging device, wherein the plurality of scan images comprises a chest wall region. The method further includes determining a chest wall model representative of the chest wall region based on the plurality of scan images. The method also includes determining a plurality of segmented scan images segmented along the chest wall region based on the chest wall model. In addition, the method includes determining lesion information using an automated lesion detection technique applied to the plurality of segmented scan images. The method also includes displaying the plurality of scan images along with at least one of the lesion information and the chest wall model.
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公开(公告)号：US20150142462A1
公开(公告)日：2015-05-21
申请号：US14524696
申请日：2014-10-27
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Vivek Prabhakar Vaidya , Hima Mukesh Patel
IPC: G06F19/00
CPC classification number: G06F19/3418 , G06F19/321
Abstract: A method and system for communicating medical data is presented. Patient data, scan parameters, and/or reference information may be received from a patient unit communicatively coupled to a remote unit over a communication network. The patient data may include at least an image corresponding to a patient. Further, one or more anatomical regions in the image may be identified. Additionally, ranks corresponding to the one or more anatomical regions may be computed based on the patient data, the scan parameters, and/or the reference information. Further, one or more portions of the image corresponding to the one or more anatomical regions may be iteratively transmitted from the patient unit to the remote unit based on the computed ranks.
Abstract translation: 提出了一种用于传送医疗数据的方法和系统。 患者数据，扫描参数和/或参考信息可以从通信地通过通信网络耦合到远程单元的患者单元接收。 患者数据可以至少包括与患者对应的图像。 此外，可以识别图像中的一个或多个解剖区域。 另外，可以基于患者数据，扫描参数和/或参考信息来计算对应于一个或多个解剖区域的等级。 此外，可以基于所计算的等级，将与一个或多个解剖区域对应的图像的一个或多个部分从患者单元迭代地发送到远程单元。
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公开(公告)号：US20150087982A1
公开(公告)日：2015-03-26
申请号：US14033452
申请日：2013-09-21
Applicant: General Electric Company
Inventor： Rakesh Mullick , Pavan Kumar Veerabhadra Annangi , Xiaoxing Li , Vidya Pundalik Kamath , Fei Zhao , Vivek Prabhakar Vaidya , Soma Biswas
CPC classification number: A61B8/5223 , A61B8/085 , G06T7/0012 , G06T7/41 , G06T2207/10132 , G06T2207/20076 , G06T2207/30068 , G06T2207/30096
Abstract: A method is provided for detecting lesions in ultrasound images. The method includes acquiring ultrasound information, determining discriminative descriptors that describe the texture of a candidate lesion region, and classifying each of the discriminative descriptors as one of a top boundary pixel, a lesion interior pixel, a lower boundary pixel, or a normal tissue pixel. The method also includes determining a pattern of transitions between the classified discriminative descriptors, and classifying the candidate lesion region as a lesion or normal tissue based on the pattern of transitions between the classified discriminative descriptors.
Abstract translation: 提供了一种用于检测超声图像中的损伤的方法。 该方法包括获取超声信息，确定描述候选病变区域的纹理的区分描述符，并将每个鉴别描述符分类为顶边界像素，病变内部像素，下边界像素或正常组织像素之一 。 该方法还包括确定分类的辨别描述符之间的转换模式，并且基于分类的识别描述符之间的转换模式将候选病变区域划分为病变或正常组织。
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