Abstract: A method for execution by a computing entity includes obtaining first and second learning objects regarding a topic. The method further includes deriving a first set of knowledge test-points for the first learning object regarding the topic based on a first set of knowledge bullet-points. The method further includes deriving a second set of knowledge test-points for the second learning object regarding the topic based on the second set of knowledge bullet-points. The method further includes generating a first knowledge assessment asset for the first learning object regarding the topic based on the first set of knowledge test-points, an illustrative asset, and a first descriptive asset of the first learning object. The method further includes generating a second knowledge assessment asset for the second learning object regarding the topic based on the second set of knowledge test-points, the illustrative asset, and a second descriptive asset of the second learning object.

Abstract: A method for execution by a computing entity includes obtaining first lesson asset information regarding a first topic and obtaining second lesson asset information regarding a second topic. The method further includes generating a set of common lesson illustrative assets based on a first lesson illustrative asset of the first lesson asset information and based on a second lesson illustrative asset of the second lesson asset information. The method further includes generating combined lesson asset information regarding the first and second topics utilizing the common lesson illustrative assets. The method further includes linking the combined lesson asset information and the set of common lesson illustrative assets to form at least a portion of a multi-disciplined learning tool.

Abstract: A method for execution by a computing entity to create a multi-disciplined learning tool regarding an abstract environment topic includes creating first-passes of a first and second learning objects for first and second pieces of information regarding the abstract environment topic to include first and second sets of knowledge bullet-points regarding the first and second pieces of information. The method further includes obtaining a synthetic asset based on the first and second set of knowledge bullet-points. The method further includes creating second-passes of the first and second learning objects to further include first and second descriptive assets regarding the first and second pieces of information based on the first and second sets of knowledge bullet-points and the synthetic asset. The method further includes linking the second-passes of the first and second learning objects together to form at least a portion of the multi-disciplined learning tool.

Abstract: Aspects of the subject disclosure may include, for example, a method comprising: receiving, by a processing system including a processor, an input three-dimensional dataset comprising a first plurality of two-dimensional images of all or a portion of a subject; applying, by the processing system, bias field correction to the input three-dimensional dataset to generate a corrected three-dimensional dataset comprising a second plurality of two-dimensional images; and generating, by the processing system, a labeled three-dimensional dataset comprising a third plurality of two-dimensional images, wherein the labeled three-dimensional dataset further comprises one or more labels indicating an anatomical structure, and wherein the labeled three-dimensional dataset is generated via a convolutional neural network based upon the corrected three-dimensional dataset and based upon a previously trained three-dimensional dataset. Additional embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a method comprising: receiving, by a processing system including a processor, an input three-dimensional dataset comprising a first plurality of two-dimensional images of all or a portion of a subject; applying, by the processing system, bias field correction to the input three-dimensional dataset to generate a corrected three-dimensional dataset comprising a second plurality of two-dimensional images; and generating, by the processing system, a labeled three-dimensional dataset comprising a third plurality of two-dimensional images, wherein the labeled three-dimensional dataset further comprises one or more labels indicating an anatomical structure, and wherein the labeled three-dimensional dataset is generated via a convolutional neural network based upon the corrected three-dimensional dataset and based upon a previously trained three-dimensional dataset. Additional embodiments are disclosed.

Abstract: A method for creating a multi-disciplined learning tool regarding a topic includes creating first-passes of a first and second learning objects for first and second pieces of information regarding the topic to include first and second sets of knowledge bullet-points regarding the first and second pieces of information. The method further includes obtaining an illustrative asset based on the first and second set of knowledge bullet-points. The method further includes creating second-passes of the first and second learning objects to further include first and second descriptive assets regarding the first and second pieces of information based on the first and second sets of knowledge bullet-points and the illustrative asset. The method further includes linking the second-passes of the first and second learning objects together to form at least a portion of the multi-disciplined learning tool.

Abstract: A method for improving learning comprehension regarding a lesson includes modifying a fundamental illustrative model to illustrate a first set of learning assets of a first learning object using an illustration approach to produce a first learning illustrative model. The lesson includes learning objects which includes the first learning object. The fundamental illustrative model is based on illustrative assets of the lesson. The method further includes obtaining a first comprehension evaluation for the first learning illustrative model and modifying the illustration approach based on the first comprehension evaluation for the first learning illustrative model to produce an updated illustration approach. The method further includes modifying the fundamental illustrative model to illustrate a second set of learning assets of a second learning object using the updated illustration approach to produce a second learning illustrative model.

Abstract: A method for assessing learning comprehension regarding a topic includes modifying a fundamental illustrative model to illustrate a first set of assessment assets of a first learning object of learning objects to produce a first assessment illustrative model. The fundamental illustrative model is based on illustrative assets of a lesson that includes the learning objects. The method further includes obtaining a first assessment response for the first assessment illustrative model. When the first assessment response is favorable, the method further includes modifying the fundamental illustrative model to illustrate a second set of assessment assets of a second learning object of the learning objects to produce a second assessment illustrative model and obtaining a second assessment response for the second assessment illustrative model.

Abstract: A method for creating an assessment within a multi-disciplined learning tool regarding a topic includes deriving a first set of knowledge test-points for a first learning object regarding the topic based on a first set of knowledge bullet-points, an illustrative asset, and a first descriptive asset of the first learning object. The method further includes deriving a second set of knowledge test-points for a second learning object regarding the topic based on a second set of knowledge bullet-points, the illustrative asset, and a second descriptive asset of the second learning object. The method further includes generating a knowledge assessment asset based on the first and second knowledge test-points.

Abstract: A method for utilizing a multi-disciplined learning tool regarding a topic includes generating a fundamental illustrative model based on illustrative assets of a lesson that includes learning objects. The method further includes modifying the fundamental illustrative model to illustrate first and second sets of teaching assets of a first learning object to produce first and second learning illustrative models. When enabled, the method further includes modifying the fundamental illustrative model to illustrate third and fourth sets of teaching assets of a second learning object to produce third and fourth learning illustrative models. The method further includes outputting at least the first and second learning illustrative models.

Abstract: A method for improving learning comprehension regarding a lesson includes issuing a first learning illustrative model to a set of learner computing entities. The method further includes obtaining a first comprehension evaluation associated with a first learner computing entity for the first learning illustrative model. The method further includes obtaining a second comprehension evaluation associated with a second learner computing entity for the first learning illustrative model. The method further includes modifying the fundamental illustrative model to illustrate a second set of learning assets of a second learning object based on at least one of the first and second comprehension evaluations to produce a second learning illustrative model. The method further includes sending the second learning illustrative model to the set of learner computing entities.

Abstract: Aspects of the subject disclosure may include, for example, a method comprising: receiving, by a processing system including a processor, an input three-dimensional dataset comprising a first plurality of two-dimensional images of all or a portion of a subject; applying, by the processing system, bias field correction to the input three-dimensional dataset to generate a corrected three-dimensional dataset comprising a second plurality of two-dimensional images; and generating, by the processing system, a labeled three-dimensional dataset comprising a third plurality of two-dimensional images, wherein the labeled three-dimensional dataset further comprises one or more labels indicating an anatomical structure, and wherein the labeled three-dimensional dataset is generated via a convolutional neural network based upon the corrected three-dimensional dataset and based upon a previously trained three-dimensional dataset. Additional embodiments are disclosed.

Abstract: A method includes a processing module of a computing system generating a virtual lecture environment utilizing a group of object representations, where some of the object representations are associated with corresponding three dimensional physical objects. The method continues with the processing module receiving educator lecture inputs during a lecture recording timeframe. The method continues with the processing module generating a learner assessment plan for assessing comprehension of a virtual lecture based on the educator lecture inputs. The method continues with the processing module linking the educator lecture inputs of the lecture recording timeframe with the virtual lecture environment to produce the virtual lecture. The method continues with the processing module receiving learner interaction information during a lecture playing timeframe of the virtual lecture.