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# Annotation Format

## Export Folder Structure

RedBrick AI exports annotations in a JSON structure, accompanied by NIfTI-1 masks for segmentations. All data will be exported within a folder named after your project_id, with the following structure:
project_id/
├── segmentations
│   ├── study01
│   │   └── series1.nii
│   └── study02
│   ├── series1.nii
│   └── series2.nii

## Segmentations

The segmentation directory will contain a single sub-directory for each task in your export. The sub-directories will be named after the task name. A single task (depending on whether it was single series or multi-series) can have one or more segmentations.
The individual segmentation files will be in NIfTI-1 format and be named after the user-defined series name. If no series name is provided on upload, RedBrick will assign a unique name. Corresponding meta-data ex. category names will be provided in tasks.json.

There will be a single entry for each task in the items file. Please see the definition (typescript) of the JSON object below:
// Single task on RedBrick can be single/multi-series
// Required on upload and export
name: string;
series: Series[];
classification?: Classification;
currentStageName?: string;
createdBy?: string;
createdAt?: string;
updatedBy?: string;
updatedAt?: string;
preAssign?: {
[stageName: string]: string
}
};
// A single series can be 2D, 3D, video etc.
type Series = {
items: string | string[];
name?: string;
segmentations?: string | string[];
segmentMap?: {
[instanceId: number]: {
category: string | string[];
attributes?: Attributes;
};
};
landmarks3d?: Landmarks3D[];
measurements?: (MeasureLength | MeasureAngle)[];
boundingBoxes?: BoundingBox[];
polygons?: Polygon[];
polylines?: Polyline[];
classifications?: Classification[];
instanceClassifications?: InstanceClassification[];
};
// Label Types
type Landmarks3D = {
point: VoxelPoint;
category: string | string[];
attributes?: Attributes;
};
type MeasureLength = {
type: 'length';
point1: VoxelPoint;
point2: VoxelPoint;
absolutePoint1: WorldPoint;
absolutePoint2: WorldPoint;
normal: [number, number, number];
length: number;
category: string | string[];
attributes?: Attributes;
};
type MeasureAngle = {
type: 'angle';
point1: VoxelPoint;
point2: VoxelPoint;
vertex: VoxelPoint;
absolutePoint1: WorldPoint;
absolutePoint2: WorldPoint;
absoluteVertex: WorldPoint;
normal: [number, number, number];
angle: number;
category: string | string[];
attributes?: Attributes;
};
type BoundingBox = {
pointTopLeft: Point2D;
wNorm: number;
hNorm: number;
category: string | string[];
attributes?: Attributes;
// video meta-data
};
type Polygon = {
points: Point2D[];
category: string | string[];
attributes?: Attributes;
// video meta-data
};
type Polyline = {
points: Point2D[];
category: string | string[];
attributes?: Attributes;
// video meta-data
};
type Classification = {
category: string | string[];
// video meta-data
};
type InstanceClassification = {
fileIndex: number;
fileName?: string;
values: { [attributeName: string] : boolean};
}
type Attributes =
| { // Taxonomy V2 attributes
attrId?: number;
name?: string;
optionId?: number | number[];
value?: string | boolean | string[];
}[]
| { // Taxonomy V1 attributes
[attributeName: string]: string | boolean | string[];
};
frameIndex: number;
trackId: string;
keyFrame: number;
endTrack: Boolean;
};
// i is rows, j is columns, k is slice
type VoxelPoint = {
i: number;
j: number;
k: number;
};
// The position of VoxelPoint in physical space (world coordinate) computed using the Image Plane Module
type WorldPoint = {
x: number;
y: number;
z: number;
};
type Point2D = {
xnorm: number;
ynorm: number;
};

The Task object represents a single task on RedBrick AI. It contains task-level meta-data information about all the series within the task. A task can contain a single series or multiple series (ex. a full MRI study).

#### name: string

A user-defined string is defined on upload, it is required to be unique across all tasks in your project. The name is meant to be a human-readable string that can help identify tasks ex. you can set name of a task to patient/study01.

#### taskId?: string

A RedBrick AI generated a unique identifier for each task. This value will be provided on export.

#### currentStageName?: string

The current stage this task is in is when it was exported.

#### updatedBy?: string

The e-mail of the last user to make edits to this task.

#### updatedAt?: string

The datetime this task was last edited.

#### preAssign?: { [stageName : string] : string}

Prescribe during upload who will get a task assigned to them. You can define the assignment for each stage of the workflow, for example, Label and Review, {"Label": "[email protected]", "Review": "[email protected]"}.

#### classification: { attributes : [string : boolean] }

A list of attributes assigned to an entire task (or study, if the task encapsulates an entire study).

### Series

The Series object has meta-data and annotations for a single series within a task. A series can represent a single MRI/CT series, a video, or a single 2D image. If a series has annotations, you can expect one or more of the label entries to be present i.e. segmentations, polygons etc.

#### items: string | string[]

The items entry is a list of file paths that point to your data. Please have a look at the Items Listdocumentation to understand how to format for various modalities and series/study uploads.

#### nae: string

An optional user-defined string, needs to be unique across all series. Individual series will be named after this value on the labeling tool. Exported segmentation files will also be named using this value. Using the Series Instance UID here is good practice.

#### classifications: { attributes: [string : boolean] }

A list of attributes assigned to a specific Series.

#### instanceClassifications: fileIndex | fileName | [ values: {string : boolean} ]

The instanceClassifications object defines a series of boolean values that can be assigned to individual instances (e.g. frames in a video).

### Common Label Keys

Here are the definition for some common entries present in some/all label entries.

#### category: string | string[]

The class of your annotations. This value is part of your project taxonomy. If the class is nested, category will be string[].

#### attributes: {[attributeName: string]: string | boolean}

Each annotation can have accompanying attributes, that are also defined in your project taxonomy. attributeName is defined when creating your taxonomy.

#### VoxelPoint: {i: number, j: number, k: number}

VoxelPoint represents a three-dimensional point in image space, where i and j are columns and rows, and k is the slice number.

#### WorldPoint: {x: number, y: number, j: number}

WorldPoint represents a three-dimensional point in physical space/world coordinates. The world coordinates are calculated using VoxelPoint and the Image Plane Module.

#### Point2D: {xnorm: number, ynorm: number}

Point2D represents a two dimensional point. This is used to define annotation types on 2D data. xnorm has been normalized by image width, hnorm has been normalized by image height.

#### fileIndex: number

fileIndex is an integer that corresponds to a specific frame in a video series.

#### fileName: string

fileName represents the name given to an image or specific frame in a video series.

### Video Meta Data

#### frameIndex: number (video)

This specifies which frame in a video sequence the annotation was created.

#### trackId: string (video)

A unique string that identifies distinct object tracks in a video sequence.

#### keyFrame: boolean (video)

If true, this annotation was manually added on a particular video sequence. If false, this annotation is a result of linear interpolation.

#### endFrame: boolean (video)

If true, the annotation is the last annotation for a particular video track segment.

### Segmentation

#### segmentations?: string | string[]

A list of file paths of segmentation files for this series. Either a single .nii file, or multiple .nii files containing different instances.

#### segmentMap?: { [instanceId: number]: { category: string | string[]; attributes?: Attributes }};

A mapping between segmentation instance id, your taxonomy category name, and any accompanying attributes. The mapping will apply only to the current series, and instance ids must be unique across all series in a task - this is useful for instance segmentation.

### BoundingBox

Represents a two-dimensional bounding box

#### pointTopLeft: Point2D​

The location of the top-left point of the bounding box.

#### wNorm, hNorm: number

The width and height of the bounding box, normalized by the width and height of the image.

### Polygon

#### points: Point2D[]

A list of 2D points that are connected to form a polygon. This list is ordered such that,
$point_i$
is connected to
$point_{i+1}$
. The last point is also connected to the first point to close the polygon.

### MeasureLength

#### point1, point2 : VoxelPoint​

A length measurement is defined by two points, and the length measurement is the distance between the two points.

#### absolutePoint1, absolutePoint2 : WorldPoint​

Corresponding to point1, point2 these are points in physical space.

#### normal: [number, number, number]

Measurements can be made on oblique planes. normal defines the normal unit vector to the slice on which this annotation was made. For annotations made on non-oblique planes, the normal will be [0,0,1].

#### length: number

The value of the measurement in mm.

### MeasureAngle

#### point1, point2, vertex : VoxelPoint

Angle measurement is defined by three points, where the vertex is the middle point. The angle between the two vectors (vertex -> point1 and vertex -> point2) defines the angle measurement. These points are all represented in IJK image coordinate space.

#### absolutePoint1, absolutePoint2 : WorldPoint

Corresponding to point1, point2, vertex, these values are coordinates in the DICOM world coordinate system i.e. physical space.

#### normal: [number, number, number]

Measurements can be made on oblique planes. normal defines the normal unit vector to the slice on which this annotation was made. For annotations made on non-oblique planes, the normal will be [0,0,1].

#### angle: number

The value of the angle in degrees.

## Consensus Export

When exporting consensus annotations, the Tasks JSON file and the segmentations/ directory will have a slightly modified structure.

### Segmentations

Each task folder will contain all the users' segmentation files. The segmentation files can be uniquely identified by the index "_1" at the end of the file. You will be able to map between the users email and the index in tasks.json.
project_id/
├── segmentations
│   ├── study01
│ │ ├── series1_1.nii
│   │   └── series1_2.nii
│   └── study02
│   ├── series1_1.nii
│   └── series1_2.nii

// Single task on RedBrick can be single/multi-series
// Required on upload and export
name: string;
consensus: true;
consensusScore: number; // overall consensus score
// Only required on export
currentStageName?: string;
createdBy?: string;
createdAt?: string;
updatedBy?: string;
updatedAt?: string;
};
updatedBy: string;
updatedAt: string;
scores: {secondaryUserEmail: string, score: number}[]
series: Series[]
}

The consensus task object contains information about the consensus annotations for this task. There will be a single entry for every annotator who annotated this task. For example, if 3 users annotated each task in your project, the length of the consensusTasks array will be 3.

#### updatedBy: string

The e-mail of the user who annotated the task.

#### updatedAt: string

The datetime when the user last updated the task.

#### scores: {secondaryUserEmail: string, score: number}[]

The scores entry compares the current users' annotations with every other user. The scores array will be of length n-1, where n is the number of users who annotated this task. score is the similarity score between the current user, and secondaryUserEmail.

#### series: Series[]

The series entry for the current user only.