Taxonomies

Taxonomies allow you to define the features you'd like to annotate in your project and apply them to your data quickly and accurately. They ensure all annotations follow a structured schema, which is automatically imported to the left hand sidebar of RedBrick AI's Annotation Tool.
Objects
The features that you will annotate in your images are known as Objects. Objects are defined with a Label Type and Category (e.g., "Edema" & Segmentation), and RedBrick AI supports the following Object Label Types: 
Object Label Type
2D Image 
3D Image 
2D Video
Segmentation
✅
✅
​
Landmarks
✅
✅
✅
Angle Measurement
✅
✅
​
Length Measurement
✅
✅
​
Bounding Box
✅
✅
✅
Ellipse
✅
✅
​
Polygon
✅
​
✅
Polyline
✅
​
✅
Attributes
Attributes allow you to add a deeper level of classification to your Object Labels. Attributes are commonly used to collect more information about a particular object (e.g. "True/False" for an Object titled "tumor malignancy"). RedBrick AI offers the following Attribute Types: 
Attribute Type
Description
Boolean
A checkbox that can be either True or False
Select
A dropdown that can be a single value from a list of predefined values
Multi-select
A dropdown that can have multiple values from a list of predefined values
Textfield
A text input that can record free form text
Classification
Classifications can be created at the Study, Series and Instance Levels. Just like Attribute Types, Classifications can be Booleans, Selects, Multi-selects, or Text fields, and you can create multiple Classification options for your Taxonomy if necessary.
Study-Level Classifications are a classification for an entire Task. 
Series-Level Classifications are applied to a single series (e.g. the T1 sequence from an entire MRI study). 
Instance-Level Classifications are applied to a single frame of a video and are only available for 2D video formats.
Classification Type
2D Image
3D Image
2D Video
Study
✅
✅
✅
Series
✅
✅
✅
Instance
​
​
✅
Creating and Using Taxonomies
To create a new Taxonomy, navigate to the Taxonomy page in the left hand side bar of the RedBrick web app and click on Create Taxonomy. All taxonomies must contain at least one Object Label or Classification in order to be successfully created. 
Taxonomies are created and stored at the Organization level.  When creating a new project, you have to select a taxonomy that you will use for the project. 
Modifying Taxonomies
Taxonomies can be modified and saved in the Taxonomy page. 
Taxonomies that are being used in projects cannot be deleted
If you delete an Object Category, Attribute, or Classification from your Taxonomy, all existing associated annotations will need to be updated.
Taxonomy V1 vs. Taxonomy V2
We released Taxonomy V2 on October 20th, 2022 with several upgrades that made Taxonomies much more robust and flexible. For now, we are continuing to provide support for the legacy Taxonomy V1 as well as Taxonomy V2 to ensure that our clients won't experience any disruptions with their projects. 
Taxonomy V1 can no longer be created in the RedBrick UI or by Python SDK. We are planning to eventually deprecate support for Taxonomy V1. 
We strongly suggest that all clients utilize Taxonomy V2 for all new Projects.
Some of the many improvements associated with Taxonomy V2 include: 
1.The ability to pre-define an annotation type with a category, which drastically simplifies the label creation workflow; 
2.The ability to change Category names, Attribute names and Attribute options;
3.More robust support for deleting Categories and Attributes; 
4.Study-Level, Series-Level and Instance-Level Classification; 
5.The ability to nest Object Labels, Study-Level and Series-Level Classifications;
6.The ability to add HTML strings to Object Labels (e.g. to serve as a tooltip or brief visual guide for annotators);
The upgrades that came along with Taxonomy V2 are not compatible with Taxonomy V1. To make use of the host of new features and improvements, create a new Taxonomy V2 and apply it to a new project.
Nested Taxonomies
Taxonomies now support the nesting of Object Labels, Study-Level and Series-Level Classifications via the RedBrick AI Python SDK. By adding the parents attribute to your Taxonomy, you can create and/or designate Parent Tiers for a given Object Label.
You can create Parent Tiers by adding the parents:[] attribute to any Object Label, Study-Level, Series-Level, or Instance-Level Classification within your Taxonomy. Parent Tiers are created and assigned from left to right and in descending order, which means the first string in parents:[] will always be a Tier 1 Parent, the second string will be a Tier 2 Parent, and so on.
For full documentation, please see our Taxonomy Object reference.
For an example of a two-tiered Object Label structure, please see the example code below:
org.create_taxonomy_new(
    "Clinical Study 1",
        
    object_types=
    [
        {
            "category": "Herniated Disc",
            "labelType": "SEGMENTATION",
            "attributes": [],
            "color": "#7FFFD4",
            "classId": 0,
            # Creates the Tier 1 Parent 'Spine Pathologies' and Tier 2 Parent 'Disc Pathologies'
            "parents": ['Spine Pathologies', 'Disc Pathologies'],
        },
        {
            "category": "Bulging Disc",
            "labelType": "SEGMENTATION",
            "attributes": [],
            "color": "#DEB887",
            "classId": 1,
            "parents": ['Spine Pathologies', 'Disc Pathologies'],
        },
        {
            "category": "Degenerated Disc",
            "labelType": "SEGMENTATION",
            "attributes": [],
            "color": "#00FFFF",
            "classId": 2,
            "parents": ['Spine Pathologies', 'Disc Pathologies'],
        },
        {
            "category": "Vertebral Fracture",
            "labelType": "SEGMENTATION",
            "attributes": [],
            "color": "#FF7F50",
            "classId": 3,
            "parents": ['Spine Pathologies'],
        },
    ],
)
The above Taxonomy will be nested in the Annotation Tool as well. Tiers can also be collapsed or expanded as necessary, allowing you to easily navigate through Label Tiers and save screen space.
HTML Tooltips
RedBrick AI allows users to upload custom HTML tooltips to any Object Label, Study-Level Classification, Series-Level, or Instance-Level Classification. For larger, more complex Taxonomies, these tooltips can be a great form of input for annotators or serve as a record for any internal standards that may be associated with the annotation itself.
All HTML elements can be included within the hints:[] attribute, but images must be inserted using <img src> and a relevant link.
For security reasons, we do not allow scripts to be executed within HTML Tooltips.
You can use the hints:[] attribute to insert a string of HTML that will display in a tooltip next to an annotation upon hover. Please see the following code for an example of an Object Label with an HTML tooltip.
object_types=
    [
        {
           "category": "Herniated Disc",
           "labelType": "SEGMENTATION",
           "attributes": [],
           "color": "#7FFFD4",
           "classId": 0,
           "parents": ['Spine Pathologies', 'Disc Pathologies'],
           "hint": '<h2>Annotate each instance separately!</h2><a href=https://en.wikipedia.org/wiki/Spinal_disc_herniation>Reference</a><p>Send qs to Jason</p>'
         },
]
The above code displays as follows in the Annotation Tool:
The HTML Tooltip that appears while hovering your cursor over the "?" icon
​
​
​
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Object Label Type	2D Image	3D Image	2D Video
Segmentation	✅	✅
Landmarks	✅	✅	✅
Angle Measurement	✅	✅
Length Measurement	✅	✅
Bounding Box	✅	✅	✅
Ellipse	✅	✅
Polygon	✅		✅
Polyline	✅		✅

Attribute Type	Description
Boolean	A checkbox that can be either True or False
Select	A dropdown that can be a single value from a list of predefined values
Multi-select	A dropdown that can have multiple values from a list of predefined values
Textfield	A text input that can record free form text

Classification Type	2D Image	3D Image	2D Video
Study	✅	✅	✅
Series	✅	✅	✅
Instance			✅

Last modified 17d ago