International Business Machines Corporation

Deepika Kakrania

Tanveer Fathima Syeda-Mahmood

Yu Long Cao

Simona Cohen

Ahmed El Harouni

Amram Abutbul

Rui Zhang

Hongzhi Wang

Gopalkrishna Veni

John T Timm

Mechanisms are provided to implement a multi-layer analytics framework. The multi-layer analytics framework obtains a plurality of analytics from one or more analytics source computing systems, at least two analytics being written in different computer programming languages. The multi-layer analytics framework applies a wrapper to each of the analytics in the plurality of analytics to thereby generate wrapped analytics. The wrapper provides a unified interface for executing the analytics in the plurality of analytics regardless of the particular computer programming language used to create the analytics. The multi-layer analytics framework registers the wrapped analytics in an analytics registry, and executes an analytics pipeline comprising wrapped analytics in the analytics registry to perform an analytics operation based on the unified interface of the wrappers of the wrapped analytics.

Analytics framework for selection and execution of analytics in a distributed environment

A multi-layer analytics framework is provided that obtains a plurality of analytics from one or more analytics source computing systems. The framework applies a wrapper to each of the analytics, where the wrapper provides a unified interface for executing the analytics regardless of the particular computer programming language used to create the analytics. The framework registers the wrapped analytics in an analytics registry, receives a request to perform an analytics operation on an input dataset, from a request computing system, and automatically generates an analytics pipeline comprising a plurality of wrapped analytics retrieved from the analytics registry. The framework executes the analytics pipeline and returns results of executing the analytics pipeline to the requestor computing system.

Registration, composition, and execution of analytics in a distributed environment

A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions executed by the processor to specifically configure the processor to implement a multi-atlas segmentation engine. An offline registration component performs registration of a plurality of atlases with a set of image templates to thereby generate and store, in a first registration storage device, a plurality of offline registrations. The atlases are annotated training medical images and the image templates are non-annotated medical images. The multi-atlas segmentation engine receives a target image. An image selection component selects a subset of image templates in the set of image templates based on the target image. An online registration component performs registration of the subset of image templates with the target image to generate a plurality of online registrations. The multi-atlas segmentation engine retrieves offline registrations corresponding to the subset of image templates from the first registration storage device. The multi-atlas segmentation engine performs segmentation of the target image based on the retrieved offline registrations corresponding to the subset of image templates and the plurality of online registrations. The segmentation applies labels to anatomical structures present in the target image based on the retrieved offline registrations and the plurality of online registrations to thereby output a modified target image.

Template based anatomical segmentation of medical images

Ranking of data and retrieval of data or relationships of the data responsive to the ranking. A data model is represented as a graph, with edges in the graph linking related concepts, and an assertion represented in the graph as a reified n-ary relation. The graph is ingested though traversal and storage of the node paths. The traversal includes concepts and categories. In addition, neighborhood of one or more adjacent concepts are followed and stored in relation to the node paths. The ingested graph is converted into a set of flat document structures supported by information ranking and a retrieval framework.

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San Jose, CA, United States of America

Deepika Kakrania