Patents by Inventor Sheshadri Thiruvenkadam
Sheshadri Thiruvenkadam 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: 11810301Abstract: A method for image segmentation includes receiving an input image. The method further includes obtaining a deep learning model having a triad of predictors. Furthermore, the method includes processing the input image by a shape model in the triad of predictors to generate a segmented shape image. Moreover, the method includes presenting the segmented shape image via a display unit.Type: GrantFiled: April 9, 2021Date of Patent: November 7, 2023Assignee: General Electric CompanyInventors: Harihan Ravishankar, Vivek Vaidya, Sheshadri Thiruvenkadam, Rahul Venkataramani, Prasad Sudhakar
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Publication number: 20210233244Abstract: A method for image segmentation includes receiving an input image. The method further includes obtaining a deep learning model having a triad of predictors. Furthermore, the method includes processing the input image by a shape model in the triad of predictors to generate a segmented shape image. Moreover, the method includes presenting the segmented shape image via a display unit.Type: ApplicationFiled: April 9, 2021Publication date: July 29, 2021Inventors: Harihan Ravishankar, Vivek Vaidya, Sheshadri Thiruvenkadam, Rahul Venkataramani, Prasad Sudhakar
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Patent number: 11017269Abstract: 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.Type: GrantFiled: June 21, 2017Date of Patent: May 25, 2021Assignee: General Electric CompanyInventors: Sheshadri Thiruvenkadam, Sohan Rashmi Ranjan, Vivek Prabhakar Vaidya, Hariharan Ravishankar, Rahul Venkataramani, Prasad Sudhakar
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Patent number: 10997724Abstract: 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).Type: GrantFiled: December 14, 2017Date of Patent: May 4, 2021Assignee: General Electric CompanyInventors: Hariharan Ravishankar, Vivek Prabhakar Vaidya, Sheshadri Thiruvenkadam, Rahul Venkataramani, Prasad Sudhakar
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Publication number: 20200043170Abstract: 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).Type: ApplicationFiled: December 14, 2017Publication date: February 6, 2020Inventors: Hariharan RAVISHANKAR, Vivek Prabhakar VAIDYA, Sheshadri THIRUVENKADAM, Rahul VENKATARAMANI, Prasad SUDHAKAR
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Patent number: 10436858Abstract: An imaging system and method are disclosed. An MR image and measured B0 field map of a target volume in a subject are reconstructed, where the MR image includes one or more bright and/or dark regions. One or more distinctive constituent materials corresponding to the bright regions are identified. Each dark region is iteratively labeled as one or more ambiguous constituent materials. Susceptibility values corresponding to each distinctive and iteratively labeled ambiguous constituent material is assigned. A simulated B0 field map is iteratively generated based on the assigned susceptibility values. A similarity metric is determined between the measured and simulated B0 field maps. Constituent materials are identified in the dark regions based on the similarity metric to ascertain corresponding susceptibility values. The MRI data is corrected based on the assigned and ascertained susceptibility values. A diagnostic assessment of the target volume is determined based on the corrected MRI data.Type: GrantFiled: December 2, 2015Date of Patent: October 8, 2019Assignee: General Electric CompanyInventors: Dattesh Dayanand Shanbhag, Rakesh Mullick, Sheshadri Thiruvenkadam, Florian Wiesinger, Sudhanya Chatterjee, Kevin Matthew Koch
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Publication number: 20190266448Abstract: 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.Type: ApplicationFiled: June 21, 2017Publication date: August 29, 2019Inventors: Sheshadri Thiruvenkadam, Sohan Rashmi Ranjan, Vivek Prabhakar Vaidya, Hariharan Ravishankar, Rahul Venkataramani, Prasad Sudhakar
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Patent number: 10262425Abstract: A method for synchronization of a longitudinal data set from a subject includes receiving a first ensemble registration estimate having a first reference image corresponding to a first image ensemble and receiving a second image ensemble different from the first image ensemble. The method includes determining a second reference image based on the second image ensemble and the first reference image. Further, the method includes determining a second ensemble registration estimate based on the first ensemble registration estimate, the second reference image, the first image ensemble and the second image ensemble using an optimization technique. The method further includes generating a synchronized image ensemble corresponding to the first image ensemble and the second image ensemble based on the second ensemble registration estimate. The method also includes determining a medical condition of the subject by a medical practitioner based on the synchronized image ensemble.Type: GrantFiled: June 29, 2017Date of Patent: April 16, 2019Assignee: GENERAL ELECTRIC COMPANYInventors: Chandan Kumar Mallappa Aladahalli, Krishna Seetharam Shriram, Dattesh Dayanand Shanbhag, Sheshadri Thiruvenkadam, Sandeep Suryanarayana Kaushik, Rakesh Mullick
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Publication number: 20180197317Abstract: The present discussion relates to the use of deep learning techniques to accelerate iterative reconstruction of images, such as CT, PET, and MR images. The present approach utilizes deep learning techniques so as to provide a better initialization to one or more steps of the numerical iterative reconstruction algorithm by learning a trajectory of convergence from estimates at different convergence status so that it can reach the maximum or minimum of a cost function faster.Type: ApplicationFiled: January 6, 2017Publication date: July 12, 2018Inventors: Lishui Cheng, Bruno Kristiaan Bernard De Man, Sheshadri Thiruvenkadam, Sangtae Ahn, Lin Fu, Hao Lai
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Publication number: 20180005389Abstract: A method for synchronization of a longitudinal data set from a subject includes receiving a first ensemble registration estimate having a first reference image corresponding to a first image ensemble and receiving a second image ensemble different from the first image ensemble. The method includes determining a second reference image based on the second image ensemble and the first reference image. Further, the method includes determining a second ensemble registration estimate based on the first ensemble registration estimate, the second reference image, the first image ensemble and the second image ensemble using an optimization technique. The method further includes generating a synchronized image ensemble corresponding to the first image ensemble and the second image ensemble based on the second ensemble registration estimate. The method also includes determining a medical condition of the subject by a medical practitioner based on the synchronized image ensemble.Type: ApplicationFiled: June 29, 2017Publication date: January 4, 2018Inventors: Chandan Kumar Mallappa Aladahalli, Krishna Seetharam Shriram, Dattesh Dayanand Shanbhag, Sheshadri Thiruvenkadam, Sandeep Suryanarayana Kaushik, Rakesh Mullick
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Publication number: 20170371010Abstract: An imaging system and method are disclosed. An MR image and measured B0 field map of a target volume in a subject are reconstructed, where the MR image includes one or more bright and/or dark regions. One or more distinctive constituent materials corresponding to the bright regions are identified. Each dark region is iteratively labeled as one or more ambiguous constituent materials. Susceptibility values corresponding to each distinctive and iteratively labeled ambiguous constituent material is assigned. A simulated B0 field map is iteratively generated based on the assigned susceptibility values. A similarity metric is determined between the measured and simulated B0 field maps. Constituent materials are identified in the dark regions based on the similarity metric to ascertain corresponding susceptibility values. The MRI data is corrected based on the assigned and ascertained susceptibility values. A diagnostic assessment of the target volume is determined based on the corrected MRI data.Type: ApplicationFiled: December 2, 2015Publication date: December 28, 2017Inventors: Dattesh Dayanand Shanbhag, Rakesh Mullick, Sheshadri Thiruvenkadam, Florian Wiesinger, Sudhanya Chatterjee, Kevin Matthew Koch
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Patent number: 9760991Abstract: A system and method for estimating image intensity bias and segmentation tissues is presented. The system and method includes obtaining a first image data set and at least a second image data set, wherein the first and second image data sets are representative of an anatomical region in a subject of interest. Furthermore, the system and method includes generating a baseline bias map by processing the first image data set. The system and method also includes determining a baseline body mask by processing the second image data set. In addition, the system and method includes estimating a bias map corresponding to a sub-region in the anatomical region based on the baseline body mask. Moreover, the system and method includes segmenting one or more tissues in the anatomical region based on the bias map.Type: GrantFiled: April 21, 2014Date of Patent: September 12, 2017Assignee: GENERAL ELECTRIC COMPANYInventors: Sheshadri Thiruvenkadam, Rakesh Mullick, Sandeep Kaushik, Hua Qian, Dattesh Shanbhag, Florian Wiesinger
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Patent number: 9471976Abstract: A method implemented using at least one processor includes receiving time-varying image dataset generated by a medical imaging modality. The image dataset corresponds to a bed position and is affected by quasi-periodic motion data. The method also includes applying a signal decomposition technique to the time-varying image dataset to generate a plurality of dataset components and a plurality of motion signals. The method also includes determining reference data based on the time-varying image dataset, wherein the reference data is representative of a direction of the quasi-periodic motion. The method further includes deriving polarity of each of the plurality of motion signals based on the reference data to generate a plurality of sign corrected motion signals. The method also includes determining a gating signal corresponding to the bed position based on at least one of the plurality of sign corrected motion signals.Type: GrantFiled: February 20, 2015Date of Patent: October 18, 2016Assignee: General Electric CompanyInventors: Sheshadri Thiruvenkadam, Krishna Seetharam Shriram, Ravindra Mohan Manjeshwar, Srikrishnan Viswanathan, Kris Filip Johan Jules Thielemans
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Publication number: 20160247274Abstract: A method implemented using at least one processor includes receiving time-varying image dataset generated by a medical imaging modality. The image dataset corresponds to a bed position and is affected by quasi-periodic motion data. The method also includes applying a signal decomposition technique to the time-varying image dataset to generate a plurality of dataset components and a plurality of motion signals. The method also includes determining reference data based on the time-varying image dataset, wherein the reference data is representative of a direction of the quasi-periodic motion. The method further includes deriving polarity of each of the plurality of motion signals based on the reference data to generate a plurality of sign corrected motion signals. The method also includes determining a gating signal corresponding to the bed position based on at least one of the plurality of sign corrected motion signals.Type: ApplicationFiled: February 20, 2015Publication date: August 25, 2016Inventors: Sheshadri Thiruvenkadam, Krishna Seetharam Shriram, Ravindra Mohan Manjeshwar, Srikrishnan Viswanathan, Kris Filip Johan Jules Thielemans
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Publication number: 20160071263Abstract: A system and method for estimating image intensity bias and segmentation tissues is presented. The system and method includes obtaining a first image data set and at least a second image data set, wherein the first and second image data sets are representative of an anatomical region in a subject of interest. Furthermore, the system and method includes generating a baseline bias map by processing the first image data set. The system and method also includes determining a baseline body mask by processing the second image data set. In addition, the system and method includes estimating a bias map corresponding to a sub-region in the anatomical region based on the baseline body mask. Moreover, the system and method includes segmenting one or more tissues in the anatomical region based on the bias map.Type: ApplicationFiled: April 21, 2014Publication date: March 10, 2016Inventors: Sheshadri Thiruvenkadam, Rakesh Mullick, Sandeep Kaushik, Hua Qian, Dattesh Shanbhag, Florian Wiesinger
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Patent number: 9204817Abstract: In one embodiment, a method includes performing a magnetic resonance (MR) imaging sequence to acquire MR image slices or volumes of a first station representative of a portion of a patient; applying a first phase field algorithm to the first station to determine a body contour of the patient in the first station; identifying a contour of a first anatomy of interest within the body contour of the first station using the first phase field algorithm or a second phase field algorithm; segmenting the first anatomy of interest based on the identified contour of the first anatomy of interest; correlating first attenuation information to the segmented first anatomy of interest; and modifying a positron emission tomography (PET) image based at least on the first correlated attenuation information.Type: GrantFiled: April 19, 2012Date of Patent: December 8, 2015Assignee: General Electric CompanyInventors: Sheshadri Thiruvenkadam, Dattesh Dayanand Shanbhag, Rakesh Mullick, Florian Wiesinger, Sandeep Suryanarayana Kaushik
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Patent number: 9147258Abstract: Methods and systems for segmentation in echocardiography are provided. One method includes obtaining echocardiographic images and defining a search space within the echocardiographic images using a pair of one-dimensional (1D) profiles. The method also includes using an energy based function constrained by non-local temporal priors within the defined search space to automatically segment a contour of a cardiac structure with the 1D profiles.Type: GrantFiled: February 26, 2013Date of Patent: September 29, 2015Assignee: General Electric CompanyInventors: Sheshadri Thiruvenkadam, Navneeth Subamanian, Mithun Das Gupta
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Patent number: 8942445Abstract: Exemplary embodiments of the present disclosure are directed to correcting lung density variations in positron emission tomography (PET) images of a subject using a magnetic resonance (MR) image. A pulmonary vasculature and an outer extent of a lung cavity can be identified in a MR image corresponding to a thoracic region of the subject in response to an intensity associated with pixels in the MR image. The pixels within the outer extent of the lung cavity are classified as corresponding to the pulmonary vasculature or the lung tissue. Exemplary embodiments of the present disclosure can apply attenuation coefficients to a reconstruction of the PET image based on the classification of the pixels within the outer extent of the lung cavity.Type: GrantFiled: September 14, 2012Date of Patent: January 27, 2015Assignee: General Electric CompanyInventors: Thomas Kwok-Fah Foo, Christopher Judson Hardy, Albert Henry Roger Lonn, Ravindra Mohan Manjeshwar, Dattesh Dayanand Shanbhag, Sheshadri Thiruvenkadam
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Patent number: 8867814Abstract: A method for generating a positron emission tomography (PET) attenuation correction map. The method includes obtaining a magnetic resonance (MR) image dataset of a subject of interest, obtaining a positron emission tomography (PET) emission dataset of the subject of interest, segmenting the MR image dataset to identify at least one object of interest, determining a volume of the object of interest, and generating a PET attenuation correction map using the determined volume. A medical imaging system and a non-transitory computer readable medium are also described herein.Type: GrantFiled: October 4, 2012Date of Patent: October 21, 2014Assignee: General Electric CompanyInventors: Albert Henry Roger Lonn, Scott David Wollenweber, Dattesh Dayanand Shanbhag, Sheshadri Thiruvenkadam
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Publication number: 20140233818Abstract: Methods and systems for segmentation in echocardiography are provided. One method includes obtaining echocardiographic images and defining a search space within the echocardiographic images using a pair of one-dimensional (1D) profiles. The method also includes using an energy based function constrained by non-local temporal priors within the defined search space to automatically segment a contour of a cardiac structure with the 1D profiles.Type: ApplicationFiled: February 26, 2013Publication date: August 21, 2014Applicant: General Electric CompanyInventors: Sheshadri Thiruvenkadam, Navneeth Subamanian, Mithun Das Gupta