Mayo Foundation for Medical Education and Research

Accenture Global Solutions Limited

Vanderbilt University

Srinivasan Rajagopalan

Richard A Robb

Kumar Viswanathan

Vinu Varghese

Tobias Peikert

Sivasubramanian Arumugam Jalajam

Manikandan Chandran

Anand Yesuraj Prakash

Venkatesan Kamalakannan

Brian J Bartholmai

Ronald A Karwoski

Thangaraj Paramasivan

Fabien Maldonado

Peter Ashly

Loganathan Muthu

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, are described that enable identifying an insurance information associated with a property, retrieving one or more compliance rules related to the insurance information, automatically determining, based on the one or more compliance rules, whether the insurance information has a compliant status or a non-compliant status, in response to determining that the insurance information has the non-compliant status, processing the insurance information, based on one or more exception rules, to generate an exception waiver including a plurality of waiver parameters, applying the plurality of waiver parameters to a waiver model to determine a waiver confidence score, and based on the waiver confidence score selectively generate an insurance certificate for the property.

Automated regulatory compliance for insurance

Systems and methods are provided for classifying a tissue mass as malignant or benign. The method includes identifying a region of interest in the tissue mass in computed tomography (CT) imaging data, segmenting the CT imaging data to delimit at least a portion of the tissue mass into image segments, extracting a set of radiomic parameters indicative of the tissue mass. The set of radiomic parameters may include tissue mass location, tissue mass shape, tissue mass surface characteristic, or tissue mass texture distribution indicative of the amount of image segments identified. The method also includes generating a report classifying the indeterminate lung nodule as being malignant or benign based on the set of radiomic parameters of the indeterminate lung nodule.

System and method for tomography-based radiomic mass analysis

A system and method for rating the validity of multiple data processing algorithms A-Aon a set of multi-dimensional input data I where P-Pis the output data produced by the competing algorithms. Processing steps include: (1) computing the Fourier Transform or the phase congruence of the input data I, (2) computing the Fourier Transform or the phase congruence of the output data P-P, (3) computing the phase of the data computed at step 1, (4) computing the phases of the data computed at step 2, and (5) computing a similarity metric, such as the normalized cross correlation of each phase computed at step 4 with the corresponding phase computed at step 3. The similarity metrics computed at step 5 can be ordered to provide a ranking of the algorithm validity.

Techniques to rate the validity of multiple methods to process multi-dimensional data

Techniques for generating tissue support structure are presented. A curved minimal surface shape is determined as a template for generating a tissue support structure. The determined curved minimal surface shape is numerically or functionally defined to create data representing the shape. The data is exported to a rapid prototyping system to generate a plurality of tissue support structures having a curved minimal surface. The tissue support structure can have a pore sub-section and a non-pore sub-section divided by a non-intersecting two-sided surface. A number of characteristics of the tissue support structure can be modulated including porosity and mechanical strength.

Tissue support structure

A training module is used to produce a database that stores histogram landmarks for MR images acquired with different scan parameters such as TR, TE flip angle and inversion recovery time. This database is used by a transformation module which enables an MR image acquired at one prescribed set of scan parameters to be transformed into MR images that simulate MR images acquired with different scan parameters.

Method for producing multiple MR images with different contrast from a single image acquisition

Growing community of inventors

Rochester, MN, United States of America

Srinivasan Rajagopalan