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import {
utilities,
eventTarget,
Enums,
triggerEvent,
cache,
} from '@cornerstonejs/core';
import * as cornerstoneTools from '@cornerstonejs/tools';
import type { Types as cstTypes } from '@cornerstonejs/tools';
import {
segmentation as cstSegmentation,
LabelmapBaseTool,
} from '@cornerstonejs/tools';
import { Events as aiEvents } from './enums';
const { strategies } = cstSegmentation;
const { fillInsideCircle } = strategies;
// @ts-ignore
import ort from 'onnxruntime-web/webgpu';
import { vec3 } from 'gl-matrix';
const { annotation } = cornerstoneTools;
const { state: annotationState } = annotation;
const { Events } = Enums;
const { Events: toolsEvents } = cornerstoneTools.Enums;
const { segmentation } = cornerstoneTools;
const { filterAnnotationsForDisplay } = cornerstoneTools.utilities.planar;
const { IslandRemoval } = cornerstoneTools.utilities;
const { triggerSegmentationDataModified } =
segmentation.triggerSegmentationEvents;
export type ModelType = {
name: string;
key: string;
url: string;
size: number;
opt?: Record<string, unknown>;
};
/**
* clone tensor
*/
function cloneTensor(t) {
return new ort.Tensor(t.type, Float32Array.from(t.data), t.dims);
}
/*
* create feed for the original facebook model
*/
function feedForSam(emb, points, labels, modelSize = [1024, 1024]) {
const maskInput = new ort.Tensor(
new Float32Array(256 * 256),
[1, 1, 256, 256]
);
const hasMask = new ort.Tensor(new Float32Array([0]), [1]);
const originalImageSize = new ort.Tensor(new Float32Array(modelSize), [2]);
const pointCoords = new ort.Tensor(new Float32Array(points), [
1,
points.length / 2,
2,
]);
const pointLabels = new ort.Tensor(new Float32Array(labels), [
1,
labels.length,
]);
const key = (emb.image_embeddings && 'image_embeddings') || 'embeddings';
return {
image_embeddings: cloneTensor(emb[key]),
point_coords: pointCoords,
point_labels: pointLabels,
mask_input: maskInput,
has_mask_input: hasMask,
orig_im_size: originalImageSize,
};
}
/*
Create a function which will be passed to the promise
and resolve it when FileReader has finished loading the file.
*/
function getBuffer(fileData) {
return new Promise((resolve) => {
const reader = new FileReader();
reader.readAsArrayBuffer(fileData);
reader.onload = function () {
const arrayBuffer = reader.result as ArrayBuffer;
const bytes = new Float32Array(arrayBuffer);
resolve(bytes);
};
});
}
export enum Loggers {
Log = 'status',
Encoder = 'encoder',
Decoder = 'decoder',
}
/**
* The ONNXController handles the interaction between CS3D viewports and ONNX segmentation
* models to allow segmentation of volume and stack viewports using browser local
* data models. The process is that a particular view of the viewport is rendered
* using the loadImageToCanvas to generate the required model size. This will render
* without annotations/segmentation. Then, this view is passed to the encoder model which
* transforms the data into a set of information about the overall image. This encoding
* can take a while, so it is cached.
*
* To generate segmentations, the encoded model data is combined with information from the
* user in the form of annotations on the image to include or exclude regions from the segmentation,
* and allow the segmentation to be guided.
*
* Once the segmentation data has been generated, it is converted from the overlay/bitmap model into
* a CS3D segmentation map, in the segment index currently being worked on.
*
* The encoded model data is stored in browser local storage, and each model
* typically consumes about 4 mb per frame. The path for the storage is
* based on the target id and the study/series/instance attributes. That
* path is:
*
* `<modelName>/<studyUID>/<seriesUID>/<filePath>`
* where the file path is the instance UID, or a made up name based on the
* slice index and view normal for orthographic images.
*
* For encoding images, there are two sessions to consider. The currently
* displaying session has information about the image being worked on, while a
* second session allows encoding images not being currently viewed. This allows
* for background encoding of images. However, note the library does NOT allow
* encoding two images at the same time, it is merely that two images can be
* queued up for encoding at the same time and can have non-overlapping results.
*/
export default class ONNXSegmentationController {
/** Default name for a tool for inclusion points */
public static MarkerInclude = 'MarkerInclude';
/** Default name for a tool for exclusion points */
public static MarkerExclude = 'MarkerExclude';
/** Default name for a tool for box prompt */
public static BoxPrompt = 'BoxPrompt';
/** Some viewport options for loadImageToCanvas */
public static viewportOptions = {
displayArea: {
storeAsInitialCamera: true,
// Use nearest neighbour for better results on the model
interpolationType: Enums.InterpolationType.NEAREST,
imageArea: [1, 1],
imageCanvasPoint: {
// TODO - fix this so top left corner works
imagePoint: [0.5, 0.5],
canvasPoint: [0.5, 0.5],
},
} as Types.DisplayArea,
background: <Types.Point3>[0, 0, 0.2],
};
// the image size on canvas
maxWidth = 1024;
maxHeight = 1024;
// the image size supported by the model
modelWidth = 1024;
modelHeight = 1024;
tool;
/**
* Defines the URL endpoints and render sizes/setup for the various models that
* can be used.
*/
static MODELS = {
sam_l: [
{
name: 'sam-l-encoder',
url: '/sam_l/vit_l_encoder.onnx',
size: 1224,
key: 'encoder',
feedType: 'images',
},
{
name: 'sam-l-decoder',
url: '/sam_l/vit_l_decoder.onnx',
size: 17,
key: 'decoder',
},
],
sam_h: [
{
name: 'sam-h-encoder',
url: '/sam_h/vit_h_encoder.onnx',
size: 18,
key: 'encoder',
feedType: 'images',
},
{
name: 'sam-h-decoder',
url: '/sam_h/vit_h_decoder.onnx',
size: 1,
key: 'decoder',
},
],
};
public canvas = document.createElement('canvas');
public canvasMask = document.createElement('canvas');
/** Store other sessions to be used for next images. */
private sessions = [];
private config;
private points = [];
private labels = [];
private worldPoints = new Array<Types.Point3>();
private randomPoints;
private _searchBreadth = 3;
private loadingAI: Promise<unknown>;
protected viewport;
protected excludeTool = ONNXSegmentationController.MarkerExclude;
protected currentImage;
private listeners = [console.log];
protected desiredImage = {
imageId: null,
sampleImageId: null,
imageIndex: -1,
decoder: null,
encoder: null,
};
protected imageEncodings = new Map();
protected sharedImageEncoding;
protected boxRadius = 5;
protected imageImageData;
protected isGpuInUse = false;
protected annotationsNeedUpdating = false;
protected maskImageData;
protected promptAnnotationTypes = [
ONNXSegmentationController.MarkerInclude,
ONNXSegmentationController.MarkerExclude,
ONNXSegmentationController.BoxPrompt,
];
protected _cachedPromptAnnotations;
// autoSegment mode properties
protected _enabled = false;
protected _autoSegmentMode = false;
protected imageIdsRunAgainst = new Map();
protected numRandomPoints = 25; // Default number of random points to sample
/**
* Fill internal islands by size, and consider islands at the edge to
* be included as internal.
*/
protected islandFillOptions: {
maxInternalRemove: number;
fillInternalEdge: boolean;
} = {
maxInternalRemove: 16,
fillInternalEdge: true,
};
/**
* The p cutoff to apply, values p and above are included.
* The values are pixel values, so 0 means everything, while 255 means
* only certainly included. A value of 64 seems reasonable as it omits low probability areas,
* but most areas are >190 in actual practice.
*/
protected pCutoff = 64;
/**
* Configure the ML Controller. No parameters are required, and will default
* to the basic set of controls using MarkerInclude/Exclude and the default SAM
* model for segmentation.
*
* The plan is to add additional prompt types and default models which can be
* applied, but this version is a simple/basic version.
*
* @param options - a set of options to configure this with
* * listeners - a set of functions to call to get log type feedback on the status of the segmentation
* * getPromptAnnotations - a function to get the annotations which are used as input to the AI segmentation
* * promptAnnotationTypes - a list of annotation type names to use to generate the prompts. This is an
* alternate to the getPromptAnnotations
* * models - a set of model configurations to run - note these are added globally to the static models
* * modelName - the specific model name to choose. Must exist in models.
* * autoSegmentMode - whether to enable autoSegment mode by default
* * numRandomPoints - the number of random points to sample for autoSegment mode
*/
constructor(
options: {
listeners?: Array<(message: string) => void>;
getPromptAnnotations?: (
viewport?: Types.IViewport
) => cornerstoneTools.Types.Annotation[];
promptAnnotationTypes?: string[];
models?: Record<string, ModelType[]>;
modelName?: string;
islandFillOptions?: {
maxInternalRemove?: number;
fillInternalEdge?: boolean;
};
autoSegmentMode?: boolean;
numRandomPoints?: number;
searchBreadth?: number;
} = {
listeners: null,
getPromptAnnotations: null,
promptAnnotationTypes: null,
models: null,
modelName: null,
islandFillOptions: {
maxInternalRemove: 16,
fillInternalEdge: true,
},
autoSegmentMode: false,
numRandomPoints: 2,
searchBreadth: 3,
}
) {
if (options.listeners) {
this.listeners = [...options.listeners];
}
if (options.getPromptAnnotations) {
this.getPromptAnnotations = options.getPromptAnnotations;
}
this.promptAnnotationTypes =
options.promptAnnotationTypes || this.promptAnnotationTypes;
if (options.models) {
Object.assign(ONNXSegmentationController.MODELS, options.models);
}
this.config = this.getConfig(options.modelName);
// Ensure we have non-optional properties when assigning
if (options.islandFillOptions) {
this.islandFillOptions = {
maxInternalRemove:
options.islandFillOptions.maxInternalRemove ??
this.islandFillOptions.maxInternalRemove,
fillInternalEdge:
options.islandFillOptions.fillInternalEdge ??
this.islandFillOptions.fillInternalEdge,
};
}
// Initialize autoSegment mode
this._autoSegmentMode = options.autoSegmentMode || false;
this.numRandomPoints = options.numRandomPoints || this.numRandomPoints;
// Set search breadth for neighbor slices
this._searchBreadth = options.searchBreadth || this._searchBreadth;
}
/**
* Enable or disable the controller
*/
public set enabled(enabled: boolean) {
this._enabled = enabled;
}
public get enabled() {
return this._enabled;
}
/**
* Enable or disable autoSegment mode
*/
public set autoSegmentMode(enabled: boolean) {
this._autoSegmentMode = enabled;
// Automatically enable the controller when autoSegment mode is enabled
if (enabled) {
this._enabled = true;
}
}
public get autoSegmentMode() {
return this._autoSegmentMode;
}
/**
* Set the number of random points to sample in autoSegment mode
*/
public set numSamplePoints(num: number) {
this.numRandomPoints = num;
}
/**
* Loads the AI model. This can take a while and will return a promise
* which resolves when the model is completed. If the model is already loaded,
* then the promise returned will be resolved already. Can safely be called multiple
* times to allow starting the model load early, and then waiting for it when done.
*/
public initModel(): Promise<unknown> {
if (!this.loadingAI) {
this.loadingAI = this.load();
}
return this.loadingAI;
}
public setPCutoff(cutoff: number) {
this.pCutoff = cutoff;
this.annotationsNeedUpdating = true;
this.tryLoad();
}
/**
* Connects a viewport up to get annotations and updates
* Note that only one viewport at a time is permitted as the model needs to
* load data about the active viewport. This method will disconnect a previous
* viewport automatically.
*
* The viewport must have a labelmap segmentation registered, as well as a
* tool which extends LabelmapBaseTool to use for setting the preview view
* once the decode is completed. This is provided as toolForPreview
*
* @param viewport - a viewport to listen for annotations and rendered events
* @param toolForPreview - this tool is used to access the preview object and
* create a new preview instance.
*/
public initViewport(viewport) {
const { desiredImage } = this;
if (this.viewport) {
this.disconnectViewport(this.viewport);
}
this.currentImage = null;
this.viewport = viewport;
const brushInstance = new LabelmapBaseTool(
{},
{
configuration: {
strategies: {
FILL_INSIDE_CIRCLE: fillInsideCircle,
},
activeStrategy: 'FILL_INSIDE_CIRCLE',
preview: {
enabled: true,
},
},
}
);
this.tool = brushInstance;
desiredImage.imageId =
viewport.getCurrentImageId?.() || viewport.getViewReferenceId();
if (desiredImage.imageId.startsWith('volumeId:')) {
desiredImage.sampleImageId = viewport.getImageIds(
viewport.getVolumeId()
)[0];
} else {
desiredImage.sampleImageId = desiredImage.imageId;
}
viewport.element.addEventListener(
Events.IMAGE_RENDERED,
this.viewportRenderedListener
);
const boundListener = this.annotationModifiedListener;
eventTarget.addEventListener(toolsEvents.ANNOTATION_ADDED, boundListener);
eventTarget.addEventListener(
toolsEvents.ANNOTATION_MODIFIED,
boundListener
);
eventTarget.addEventListener(
toolsEvents.ANNOTATION_COMPLETED,
boundListener
);
if (desiredImage.imageId) {
this.tryLoad();
}
}
public acceptPreview(element) {
this.tool.acceptPreview(element);
}
public rejectPreview(element) {
this.tool.rejectPreview(element);
}
restoreCachedPromptAnnotations(viewport: Types.IViewport) {
if (!this._cachedPromptAnnotations) {
return [];
}
const annotations = [];
const { include, exclude } = this._cachedPromptAnnotations;
if (include) {
const newInclude = cornerstoneTools.utilities.moveAnnotationToViewPlane(
include,
viewport
);
annotations.push(newInclude);
}
if (exclude) {
const newExclude = cornerstoneTools.utilities.moveAnnotationToViewPlane(
exclude,
viewport
);
annotations.push(newExclude);
}
return annotations;
}
/**
* Removes all prompt annotations but caches one positive (include) and one negative (exclude)
* annotation. These cached annotations can be re-added later (for example, after an interpolateScroll).
*
* @param viewport - The viewport element on which annotations are rendered.
*/
removePromptAnnotationsWithCache(viewport: Types.IViewport) {
const toolNames = [
ONNXSegmentationController.MarkerInclude,
ONNXSegmentationController.MarkerExclude,
ONNXSegmentationController.BoxPrompt,
];
let cachedInclude = null;
let cachedExclude = null;
// Get all annotations once
const allAnnotations =
cornerstoneTools.annotation.state.getAllAnnotations();
// Single loop to filter, cache, and remove prompt annotations
for (const annotation of allAnnotations) {
const toolName = annotation.metadata.toolName;
if (toolNames.includes(toolName)) {
// Cache first found include/exclude annotations
if (
toolName === ONNXSegmentationController.MarkerInclude &&
!cachedInclude
) {
cachedInclude = annotation;
} else if (
toolName === ONNXSegmentationController.MarkerExclude &&
!cachedExclude
) {
cachedExclude = annotation;
}
// Remove the annotation
cornerstoneTools.annotation.state.removeAnnotation(
annotation.annotationUID
);
}
}
// Store cached annotations
this._cachedPromptAnnotations = {
include: cachedInclude,
exclude: cachedExclude,
};
viewport.render();
}
/**
* The interpolateScroll checks to see if there are any annotations on the
* current image in the specified viewport, and if so, scrolls in the given
* direction and copies the annotations to the new image displayed.
* It will not copy any annotations onto a viewport already containing
* prompt annotations, nor will it do anything if there are no annotations
* on the current viewport.
*
* Assumes the current and next viewports have the same viewport normal, and
* that the difference between positions is entirely contained by the difference
* in the focal point between viewports. This difference is added to each
* point in the annotations.
*/
public async interpolateScroll(viewport = this.viewport, dir = 1) {
const { element } = viewport;
this.tool.acceptPreview(element);
const promptAnnotations = this.getPromptAnnotations(viewport);
const currentSliceIndex = viewport.getCurrentImageIdIndex();
const viewRef = viewport.getViewReference({
sliceIndex: currentSliceIndex + dir,
});
if (!viewRef || viewRef.sliceIndex === currentSliceIndex) {
console.warn('No next image in direction', dir, currentSliceIndex);
return;
}
viewport.scroll(dir);
// Wait for the scroll to complete
await new Promise((resolve) => window.setTimeout(resolve, 250));
let annotations = [];
if (!promptAnnotations.length) {
annotations = this.restoreCachedPromptAnnotations(viewport);
} else {
// Add the difference between the new and old focal point as being the
// position difference between images. Does not account for any
// rotational differences between frames that may occur on stacks
for (const annotation of promptAnnotations) {
const newAnnotation = <cstTypes.Annotation>structuredClone(annotation);
// remove the old annotation
cornerstoneTools.annotation.state.removeAnnotation(
annotation.annotationUID
);
Object.assign(newAnnotation.metadata, viewRef);
cornerstoneTools.utilities.moveAnnotationToViewPlane(
newAnnotation,
viewport
);
annotations.push(newAnnotation);
}
}
for (const annotation of annotations) {
annotationState.addAnnotation(annotation, viewport.element);
}
viewport.render();
}
/**
* Logs the message to the given log level
*/
protected log(logger: Loggers, ...args) {
for (const listener of this.listeners) {
listener(logger, ...args);
}
}
/**
* Gets a list of the include/exclude orientation annotations applying to the
* current image id.
*/
protected getPromptAnnotations = (viewport = this.viewport) => {
const annotations = [];
const { element } = viewport;
for (const annotationName of this.promptAnnotationTypes) {
annotations.push(
...annotationState.getAnnotations(annotationName, element)
);
}
const currentAnnotations = filterAnnotationsForDisplay(
this.viewport,
annotations
);
return currentAnnotations;
};
/**
* A listener for viewport being rendered that tried loading/encoding the
* new image if it is different from the previous image. Will return before
* the image is encoded. Can be called without binding as it is already
* bound to the this object.
* The behaviour of the callback is that if the image has changed in terms
* of which image (new view reference), then that image is set as the
* currently desired encoded image, and a new encoding will be read from
* cache or one will be created and stored in cache.
*
* This does not need to be manually bound, the initViewport will bind
* this to the correct rendering messages.
*/
protected viewportRenderedListener = (_event) => {
const { viewport, currentImage, desiredImage } = this;
desiredImage.imageId =
viewport.getCurrentImageId() || viewport.getViewReferenceId();
desiredImage.imageIndex = viewport.getCurrentImageIdIndex();
if (!desiredImage.imageId) {
return;
}
if (desiredImage.imageId.startsWith('volumeId:')) {
desiredImage.sampleImageId = viewport.getImageIds(
viewport.getVolumeId()
)[0];
} else {
desiredImage.sampleImageId = desiredImage.imageId;
}
if (desiredImage.imageId === currentImage?.imageId) {
return;
}
if (this._enabled && this._autoSegmentMode) {
if (
'isInAcquisitionPlane' in viewport &&
!viewport.isInAcquisitionPlane()
) {
console.warn(
'Non acquisition plane viewports and auto segment mode is not yet supported'
);
return;
}
const segmentation =
cornerstoneTools.segmentation.activeSegmentation.getActiveSegmentation(
viewport.id
);
const segmentIndex =
cornerstoneTools.segmentation.segmentIndex.getActiveSegmentIndex(
segmentation.segmentationId
);
// Get all image IDs
const imageIds = viewport.getImageIds();
const currentImageIdIndex = viewport.getCurrentImageIdIndex();
const pointLists = [];
let foundPrevious = false;
let foundNext = false;
// Check slices with increasing offset until we find points or reach max breadth
for (let offset = 1; offset <= this._searchBreadth; offset++) {
if (!foundPrevious) {
const previousImageIdIndex = currentImageIdIndex - offset;
if (previousImageIdIndex >= 0) {
const previousImageId = imageIds[previousImageIdIndex];
const previousLabelmapImage =
cache.getImageByReferencedImageId(previousImageId);
const previousLabelmapVoxelManager =
previousLabelmapImage?.voxelManager;
if (previousLabelmapVoxelManager) {
let foundInThisSlice = false;
previousLabelmapVoxelManager.forEach(({ value, pointIJK }) => {
if (value === segmentIndex) {
const worldCoords = utilities.imageToWorldCoords(
previousLabelmapImage.imageId,
[pointIJK[0], pointIJK[1]]
);
pointLists.push(worldCoords);
foundInThisSlice = true;
}
});
if (foundInThisSlice) {
foundPrevious = true;
}
}
}
}
// Check next slice if we haven't found points in that direction yet
if (!foundNext) {
const nextImageIdIndex = currentImageIdIndex + offset;
if (nextImageIdIndex < imageIds.length) {
const nextImageId = imageIds[nextImageIdIndex];
const nextLabelmapImage =
cache.getImageByReferencedImageId(nextImageId);
const nextLabelmapVoxelManager = nextLabelmapImage?.voxelManager;
if (nextLabelmapVoxelManager) {
let foundInThisSlice = false;
nextLabelmapVoxelManager.forEach(({ value, pointIJK }) => {
if (value === segmentIndex) {
const worldCoords = utilities.imageToWorldCoords(
nextLabelmapImage.imageId,
[pointIJK[0], pointIJK[1]]
);
pointLists.push(worldCoords);
foundInThisSlice = true;
}
});
if (foundInThisSlice) {
foundNext = true;
}
}
}
}
// If we have found points in both directions, we can stop
if (foundPrevious && foundNext) {
break;
}
}
// Pick random points from the pointLists
this.randomPoints =
pointLists.length > 0
? pointLists
.sort(() => Math.random() - 0.5)
.slice(0, Math.min(pointLists.length, this.numRandomPoints))
: [];
}
const { canvasMask } = this;
const ctxMask = canvasMask.getContext('2d');
ctxMask.clearRect(0, 0, canvasMask.width, canvasMask.height);
this.tryLoad({ resetImage: true });
};
/**
* This is an already bound annotation modified listener, that is added/removed
* from the viewport by the initViewport method.
* This listener does the following:
* * Gets the annotations, returning immediately if there are no annotations
* * Marks the annotations as needing an update
* * Starts an encoding on the current image if it is not already encoded
* * When the image is encoded, runs the decoder
* * Once the decoder has completed, converts the results into a CS3D segmentation preview
*
* Note that the decoder run will not occur if the image is changed before the
* decoder starts running, and that encoding a new image may not start until
* an ongoing decoder operations has completed.
*/
protected annotationModifiedListener = cornerstoneTools.utilities.debounce(
(_event?) => {
const currentAnnotations = this.getPromptAnnotations();
if (!currentAnnotations.length) {
return;
}
this.annotationsNeedUpdating = true;
this.tryLoad();
},
300
);
/**
* Disconnects the given viewport, removing the listeners.
*/
public disconnectViewport(viewport) {
viewport.element.removeEventListener(
Events.IMAGE_RENDERED,
this.viewportRenderedListener
);
const boundListener = this.annotationModifiedListener;
eventTarget.removeEventListener(
toolsEvents.ANNOTATION_MODIFIED,
boundListener
);
eventTarget.removeEventListener(
toolsEvents.ANNOTATION_COMPLETED,
boundListener
);
}
/**
* Does the actual load, separated from the public method to allow starting
* the AI to load and then waiting for it once other things are also ready.
* This is done internally so that only a single load/setup is created, allowing
* for the load to be started and only waited for when other things are ready.
*/
protected async load() {
const { sessions } = this;
this.canvas.style.cursor = 'wait';
let loader;
// Create two sessions, one for the current images, and a second session
// for caching non-visible images. This doesn't create two GPU sessions,
// but does create two sessions for storage of encoded results.
for (let i = 0; i < 2; i++) {
sessions.push({
sessionIndex: i,
encoder: null,
decoder: null,
imageEmbeddings: null,
isLoading: false,
canvas: i === 0 ? this.canvas : document.createElement('canvas'),
});
if (i === 0) {
loader = this.loadModels(
ONNXSegmentationController.MODELS[this.config.model],
sessions[i]
).catch((e) => {
this.log(Loggers.Log, "Couldn't load models", e);
});
await loader;
} else {
// Only the encoder is needed otherwise
sessions[i].encoder = sessions[0].encoder;
}
sessions[i].loader = loader;
}
}
/**
* Clears the points, labels and annotations related to the ML model from the
* viewport.
*/
public clear(viewport) {
this.points = [];
this.labels = [];
this.getPromptAnnotations(viewport).forEach((annotation) =>
annotationState.removeAnnotation(annotation.annotationUID)
);
if (this.tool) {
this.tool.rejectPreview(this.viewport.element);
}
}
/**
* Cache the next image encoded. This will start at the current image id,
* and will keep on fetching additional images, wrapping round to the 0...current
* position-1 so as to fetch all images.
* Works with both volume (orthographic) and stack viewports.
* This will interfere with any image navigation
*
* @param current - the starting image near which other images should be cached.
* @param offset - what offset to the current image should be used, that is,
* for 125 images, if the current was 5, and the offset is 6, then the
* image at sliceIndex 5+6+1 will be used.
* @param length - the number of images. This will be determined dynamically
* based on when the view reference for the next slice returns undefined.
* Defaults to 1,000,000 to start with.
*/
public async cacheImageEncodings(
current = this.viewport.getCurrentImageIdIndex(),
offset = 0,
length = 1000_000
) {
const { viewport, imageEncodings } = this;
if (offset >= length) {
// We are done.
return;
}
const index = (offset + current) % length;
const view = viewport.getViewReference({ sliceIndex: index });
if (!view) {
length = index;
return this.cacheImageEncodings(current, offset, length);
}
const imageId =
view.referencedImageId ||
viewport.getViewReferenceId({ sliceIndex: index });
if (!imageEncodings.has(imageId)) {
// Try loading from storage
await this.loadStorageImageEncoding(current, imageId, index);
}
if (imageEncodings.has(imageId)) {
this.cacheImageEncodings(current, offset + 1, length);
return;
}
// Try doing a load, so that UI has priority
this.tryLoad();
if (this.isGpuInUse) {
setTimeout(() => this.cacheImageEncodings(current, offset), 500);
return;
}
this.log(Loggers.Log, 'Caching', index, imageId);
const sampleImageId = viewport.getImageIds()[0];
this.handleImage({ imageId, sampleImageId }, this.sessions[1]).then(() => {
this.cacheImageEncodings(current, offset + 1, length);
});
}
/**
* Handles a new image. This will render the image to a separate canvas
* using the load image to canvas, and then will load or generate encoder
* values into the imageSession provided.
* If there is already an image being handled or worked on, returns immediately.
*
* At the end of the handle, tries calling the tryLoad method to see if there
* are other high priority tasks to complete.
*/
protected async handleImage({ imageId, sampleImageId }, imageSession) {
if (imageId === imageSession.imageId || this.isGpuInUse || !this.enabled) {
return;
}
const { viewport, desiredImage } = this;
this.isGpuInUse = true;
imageSession.imageId = imageId;
imageSession.sampleImageId = sampleImageId;
try {
const isCurrent = desiredImage.imageId === imageId;
const { canvas } = imageSession;
if (isCurrent) {
this.log(
Loggers.Encoder,
`Loading image on ${imageSession.sessionIndex}`
);
this.log(Loggers.Decoder, 'Awaiting image');
canvas.style.cursor = 'wait';
}
this.points = [];
this.labels = [];
const width = this.maxWidth;
const height = this.maxHeight;
canvas.width = width;
canvas.height = height;
imageSession.imageEmbeddings = undefined;
const size = canvas.style.width;
const ctx = canvas.getContext('2d', { willReadFrequently: true });
ctx.clearRect(0, 0, width, height);
const renderArguments = {
canvas,
imageId,
viewportOptions: {
...viewport.defaultOptions,
...ONNXSegmentationController.viewportOptions,
},
viewReference: null,
renderingEngineId: viewport.getRenderingEngine().id,
};
if (imageId.startsWith('volumeId:')) {
const viewRef = viewport.getViewReference();
renderArguments.viewReference = viewRef;
renderArguments.imageId = null;
}
imageSession.canvasPosition =
await utilities.loadImageToCanvas(renderArguments);
canvas.style.width = size;
canvas.style.height = size;
if (isCurrent) {
this.log(
Loggers.Encoder,
`Rendered image on ${imageSession.sessionIndex}`
);
}
this.imageImageData = ctx.getImageData(0, 0, width, height);
const data = await this.restoreImageEncoding(imageSession, imageId);
if (data) {
imageSession.imageEmbeddings = data;
if (desiredImage.imageId === imageId) {
this.log(Loggers.Encoder, 'Cached Image');
canvas.style.cursor = 'default';
}
} else {
const t = await ort.Tensor.fromImage(this.imageImageData, {
resizedWidth: this.modelWidth,
resizedHeight: this.modelHeight,
});
const { feedType = 'input_image' } = this.config.encoder;
const feed = (feedType === 'images' && { images: t }) ||
(feedType === 'pixelValues' && { pixel_values: t }) || {
input_image: t,
};
await imageSession.loader;
const session = await imageSession.encoder;
if (!session) {
this.log(Loggers.Log, '****** No session');
return;
}
const start = performance.now();
imageSession.imageEmbeddings = session.run(feed);
const data = await imageSession.imageEmbeddings;
this.storeImageEncoding(imageSession, imageId, data);
if (desiredImage.imageId === imageId) {
this.log(
Loggers.Encoder,
`Image Ready ${imageSession.sessionIndex} ${(
performance.now() - start
).toFixed(1)} ms`
);
canvas.style.cursor = 'default';
}
}
} finally {
this.isGpuInUse = false;
}
this.tryLoad();
}
/**
* This method tries to run the decode that wraps the decode operation in
* checks for whether hte GPU is in use, whether the decode has otherwise completed,
* and a general try/catch around the decode.
*/
protected async runDecode() {
const { canvas } = this;
if (this.isGpuInUse || !this.currentImage?.imageEmbeddings) {
return;
}
this.isGpuInUse = true;
try {
this.canvas.style.cursor = 'wait';
await this.decode(this.points, this.labels);
} finally {
canvas.style.cursor = 'default';
this.isGpuInUse = false;
}
}
/**
* This function will try setting the current image to the desired loading image
* if it isn't already the desired one, and invoke the handleImage.
* If the desired image is already the right one, then it will try to run
* and outstanding decoder task.
* This sequence allows out of order decodes to happen and to start the latest
* encode/decode at the time the last operation has completed. If the user
* performs multiple operations, then only the last set is handled.
*/
public tryLoad(options = { resetImage: false }) {
const { viewport, desiredImage } = this;
if (!desiredImage.imageId || options.resetImage) {
desiredImage.imageId =
viewport.getCurrentImageId() || viewport.getViewReferenceId();
this.currentImage = null;
}
// Always use session 0 for the current session
const [session] = this.sessions;
if (session?.imageId === desiredImage.imageId) {
if (this.currentImage !== session) {
this.currentImage = session;
}
if (this._enabled && this._autoSegmentMode) {
this.points = [];
this.labels = [];
// Process random points from previous segmentation if available
if (this.randomPoints?.length) {
this.randomPoints.forEach((point) => {
const mappedPoint = this.mapAnnotationPoint(
point,
this.currentImage.canvasPosition
);
this.points.push(...mappedPoint);
this.labels.push(1);
});
this.runDecode();
}
} else {
// Regular mode - process annotations
this.updateAnnotations();
}
return;
}
this.handleImage(desiredImage, session);
}
/**
* Maps world points to destination points.
* Assumes the destination canvas is defined by the canvasPosition value
*/
mapAnnotationPoint(worldPoint, canvasPosition) {
const { origin, downVector, rightVector } = canvasPosition;
// Vectors are scaled to unit vectors in canvas index space
const deltaOrigin = vec3.sub([0, 0, 0], worldPoint, origin);
const x = Math.round(
vec3.dot(deltaOrigin, rightVector) / vec3.sqrLen(rightVector)
);
const y = Math.round(
vec3.dot(deltaOrigin, downVector) / vec3.sqrLen(downVector)
);
return [x, y];
}
/**
* Updates annotations when they have changed in some way, running the decoder.
* This will mark the annotations as needing update, so that if the
* encoding of the image isn't ready yet, or the encoder is otherwise busy,
* it will run the update again once the tryLoad is done at the end of the task.
*/
updateAnnotations(useSession = this.currentImage) {
if (
this.isGpuInUse ||
!this.annotationsNeedUpdating ||
!this.currentImage
) {
return;
}
const promptAnnotations = this.getPromptAnnotations();
this.annotationsNeedUpdating = false;
this.points = [];
this.labels = [];
this.worldPoints = [];
if (!promptAnnotations?.length || !useSession?.canvasPosition) {
return;
}
for (const annotation of promptAnnotations) {
const handle = annotation.data.handles.points[0];
const point = this.mapAnnotationPoint(handle, useSession.canvasPosition);
this.points.push(...point);
if (
annotation.metadata.toolName === ONNXSegmentationController.BoxPrompt
) {
// 2 and 3 are the codes for the handles on a box prompt
this.labels.push(2, 3);
this.points.push(
...this.mapAnnotationPoint(
annotation.data.handles.points[3],
useSession.canvasPosition
)
);
} else {
const label = annotation.metadata.toolName === this.excludeTool ? 0 : 1;
if (label) {
this.worldPoints.push(handle);
}
this.labels.push(label);
}
}
this.runDecode();
}
/**
* Restores a stored image encoding from memory cache first, and from
* the browser storage secondly. This is much faster than re-generating it
* all the time.
*/
async restoreImageEncoding(session, imageId) {
if (!this.sharedImageEncoding) {
return;
}
if (!this.imageEncodings.has(imageId)) {
await this.loadStorageImageEncoding(session, imageId);
}
const floatData = this.imageEncodings.get(imageId);
if (floatData) {
const key =
(this.sharedImageEncoding.image_embeddings && 'image_embeddings') ||
'embeddings';
this.sharedImageEncoding[key].cpuData.set(floatData);
return this.sharedImageEncoding;
}
}
/**
* Loads the image encoding from browser storage.
*/
async loadStorageImageEncoding(session, imageId, index = null) {
try {
const root = await this.getDirectoryForImageId(session, imageId);
const name = this.getFileNameForImageId(imageId, this.config.model);
if (!root || !name) {
return null;
}
const fileHandle = await findFileEntry(root, name);
if (!fileHandle) {
return null;
}
this.log(Loggers.Log, 'Loading from storage', index || imageId, name);
const file = await fileHandle.getFile();
if (file) {
const buffer = await getBuffer(file);
this.imageEncodings.set(imageId, buffer);
}
} catch (e) {
this.log(Loggers.Log, 'Unable to fetch file', imageId, e);
}
}
/**
* Stores the image encoding to both memory cache and browser storage.
*/
async storeImageEncoding(session, imageId, data) {
if (!this.sharedImageEncoding) {
this.sharedImageEncoding = data;
}
const storeData = (data.image_embeddings || data.embeddings)?.cpuData;
if (!storeData) {
console.log('Unable to store data', data);
return;
}
const writeData = new Float32Array(storeData);
this.imageEncodings.set(imageId, writeData);
try {
const root = await this.getDirectoryForImageId(session, imageId);
const name = this.getFileNameForImageId(imageId, this.config.model);
if (!root || !name) {
return;
}
const fileHandle = await root.getFileHandle(name, { create: true });
const writable = await fileHandle.createWritable();
await writable.write(writeData);
await writable.close();
// Note, this data is not considered for persistence as it is assumed multiple
// series are being worked on. See the persistence model for adding ahead of
// time caching.
} catch (e) {
this.log(Loggers.Log, 'Unable to write', imageId, e);
}
}
/**
* Given the mask created by the AI model, assigns the data to a new preview
* instance of a labelmap and triggers the modified event so that the new
* segmentation data is visible. Replaces existing segmentation on that
* image.
*/
createLabelmap(mask, canvasPosition, _points, _labels) {
const { canvas, viewport } = this;
const preview = this.tool.addPreview(viewport.element);
const {
previewSegmentIndex,
memo,
segmentationId,
segmentIndex,
segmentationVoxelManager,
} = preview;
const previewVoxelManager = memo?.voxelManager;
const { dimensions } = previewVoxelManager;
const { data } = mask;
const { origin, rightVector, downVector } = canvasPosition;
const worldPointJ = vec3.create();
const worldPoint = vec3.create();
const imageData = viewport.getDefaultImageData();
// Assumes that the load to canvas size is bigger than the destination
// size - if that isn't true, then this should super-sample the data
for (let j = 0; j < canvas.height; j++) {
vec3.scaleAndAdd(worldPointJ, origin, downVector, j);
for (let i = 0; i < canvas.width; i++) {
vec3.scaleAndAdd(worldPoint, worldPointJ, rightVector, i);
const ijkPoint = imageData.worldToIndex(worldPoint).map(Math.round);
if (
ijkPoint.findIndex((v, index) => v < 0 || v >= dimensions[index]) !==
-1
) {
continue;
}
// 4 values - RGBA - per pixel
const maskIndex = 4 * (i + j * this.maxWidth);
const v = data[maskIndex];
const segmentValue = segmentationVoxelManager.getAtIJKPoint(ijkPoint);
if (segmentValue !== 0 && segmentValue !== 255) {
continue;
}
if (v > this.pCutoff) {
previewVoxelManager.setAtIJKPoint(ijkPoint, previewSegmentIndex);
} else {
previewVoxelManager.setAtIJKPoint(ijkPoint, null);
}
}
}
if (this._autoSegmentMode) {
this.tool.doneEditMemo();
}
this.tool._previewData.isDrag = true;
const voxelManager =
previewVoxelManager.sourceVoxelManager || previewVoxelManager;
if (this.islandFillOptions) {
const islandRemoval = new IslandRemoval(this.islandFillOptions);
if (
islandRemoval.initialize(viewport, voxelManager, {
previewSegmentIndex,
segmentIndex,
points: this.worldPoints.map((point) =>
imageData.worldToIndex(point).map(Math.round)
),
})
) {
islandRemoval.floodFillSegmentIsland();
islandRemoval.removeExternalIslands();
islandRemoval.removeInternalIslands();
}
}
triggerSegmentationDataModified(segmentationId);
}
/**
* Runs the GPU decoder operation itself.
*/
async decode(points, labels, useSession = this.currentImage) {
const { canvas, canvasMask, imageImageData, desiredImage, boxRadius } =
this;
const ctx = canvas.getContext('2d', { willReadFrequently: true });
ctx.clearRect(0, 0, canvas.width, canvas.height);
canvas.width = imageImageData.width;
canvas.height = imageImageData.height;
canvasMask.width = imageImageData.width;
canvasMask.height = imageImageData.height;
if (!useSession || useSession.imageId !== desiredImage.imageId) {
this.log(
Loggers.Log,
'***** Image not current, need to wait for current image'
);
return;
}
// Comment this line out to draw just the overlay mask data
ctx.putImageData(imageImageData, 0, 0);
if (points.length) {
// need to wait for encoder to be ready
if (!useSession.imageEmbeddings) {
await useSession.encoder;
}
// wait for encoder to deliver embeddings
const emb = await useSession.imageEmbeddings;
// the decoder
const session = useSession.decoder;
const feed = feedForSam(emb, points, labels);
const res = await session.run(feed);
for (let i = 0; i < points.length; i += 2) {
const label = labels[i / 2];
ctx.fillStyle = label ? 'blue' : 'pink';
ctx.fillRect(
points[i] - boxRadius,
points[i + 1] - boxRadius,
2 * boxRadius,
2 * boxRadius
);
}
const mask = res.masks;
this.maskImageData = mask.toImageData();
this.createLabelmap(
this.maskImageData,
useSession.canvasPosition,
points,
labels
);
ctx.globalAlpha = 0.3;
const { data } = this.maskImageData;
const counts = [];
for (let i = 0; i < data.length; i += 4) {
const v = data[i];
if (v > 0) {
if (v < 255) {
data[i] = 0;
if (v > 192) {
data[i + 1] = 255;
} else {
data[i + 2] = v + 64;
}
}
counts[v] = 1 + (counts[v] || 0);
}
}
const bitmap = await createImageBitmap(this.maskImageData);
ctx.drawImage(bitmap, 0, 0);
const ctxMask = canvasMask.getContext('2d');
ctxMask.globalAlpha = 0.9;
ctxMask.drawImage(bitmap, 0, 0);
}
}
/*
* fetch and cache the ONNX model at the given url/name.
*/
async fetchAndCacheModel(url, name) {
try {
const cache = await caches.open('onnx');
let cachedResponse = await cache.match(url);
if (cachedResponse == undefined) {
// Trigger event when model component starts loading from network
triggerEvent(eventTarget, aiEvents.MODEL_COMPONENT_LOADED, {
name,
url,
status: 'loading',
source: 'network',
});
await cache.add(url);
cachedResponse = await cache.match(url);
this.log(Loggers.Log, `${name} (network)`);
} else {
// Trigger event when model component is loaded from cache
triggerEvent(eventTarget, aiEvents.MODEL_COMPONENT_LOADED, {
name,
url,
status: 'loaded',
source: 'cache',
});
this.log(Loggers.Log, `${name} (cached)`);
}
const data = await cachedResponse.arrayBuffer();
return data;
} catch (error) {
this.log(Loggers.Log, `${name} (network)`);
// Trigger event when model component has an error
triggerEvent(eventTarget, aiEvents.MODEL_COMPONENT_LOADED, {
name,
url,
status: 'error',
error,
});
return await fetch(url).then((response) => response.arrayBuffer());
}
}
/*
* load model cache data and creates an instance. This calls fetchAndCacheModle
* once for the decoder and encoder, and then instantiates an instance.
*/
async loadModels(models, imageSession = this.currentImage) {
const cache = await caches.open('onnx');
let missing = 0;
const urls = [];
// Get the list of urls to download
// eslint-disable-next-line @typescript-eslint/no-explicit-any
for (const model of Object.values(models) as ModelType[]) {
const cachedResponse = await cache.match(model.url);
if (cachedResponse === undefined) {
missing += model.size;
}
urls.push(model.url);
}
// Trigger event for model loading started
triggerEvent(eventTarget, aiEvents.MODEL_LOADING_STARTED, {
modelConfig: this.config.model,
totalSize: missing,
urls,
});
if (missing > 0) {
this.log(
Loggers.Log,
`downloading ${missing} MB from network ... it might take a while`
);
} else {
this.log(Loggers.Log, 'loading...');
}
const start = performance.now();
for (const model of Object.values(models) as ModelType[]) {
const opt = {
executionProviders: [this.config.provider],
enableMemPattern: false,
enableCpuMemArena: false,
extra: {
session: {
disable_prepacking: '1',
use_device_allocator_for_initializers: '1',
use_ort_model_bytes_directly: '1',
use_ort_model_bytes_for_initializers: '1',
},
},
interOpNumThreads: 4,
intraOpNumThreads: 2,
};
const model_bytes = await this.fetchAndCacheModel(model.url, model.name);
const extra_opt = model.opt || {};
const sessionOptions = { ...opt, ...extra_opt };
this.config[model.key] = model;
imageSession[model.key] = await ort.InferenceSession.create(
model_bytes,
sessionOptions
);
}
const stop = performance.now();
const loadTime = stop - start;
// Trigger event for model loading completed
triggerEvent(eventTarget, aiEvents.MODEL_LOADING_COMPLETED, {
modelConfig: this.config.model,
loadTimeMs: loadTime,
urls,
});
this.log(Loggers.Log, `ready, ${loadTime.toFixed(1)}ms`, urls.join(', '));
}
/**
* Gets the storage directory for storing the given image id
*/
async getDirectoryForImageId(session, imageId) {
if (
imageId.indexOf('/studies/') === -1 ||
imageId.indexOf('/instances/') === -1
) {
imageId = session.sampleImageId;
if (
!imageId ||
imageId.indexOf('/studies/') === -1 ||
imageId.indexOf('/instances/') === -1
) {
return null;
}
}
const studySeriesUids = imageId
.split('/studies/')[1]
.split('/instances/')[0]
.split('/');
const [studyUID, _series, seriesUID] = studySeriesUids;
const root = await window.navigator.storage.getDirectory();
const modelRoot = await getOrCreateDir(root, this.config.model);
const studyRoot = await getOrCreateDir(modelRoot, studyUID);
const seriesRoot = await getOrCreateDir(studyRoot, seriesUID);
return seriesRoot;
}
/**
* Gets the storage file name for the given imageId
*/
getFileNameForImageId(imageId, extension) {
if (imageId.startsWith('volumeId:')) {
const sliceIndex = imageId.indexOf('sliceIndex=');
const focalPoint = imageId.indexOf('&focalPoint=');
const name = imageId
.substring(sliceIndex, focalPoint)
.replace('&', '.')
.replace('sliceIndex=', 'volume.');
return name + extension;
}
const instancesLocation = imageId.indexOf('/instances/');
if (instancesLocation != -1) {
const sopLocation = instancesLocation + 11;
const nextSlash = imageId.indexOf('/', sopLocation);
return imageId.substring(sopLocation, nextSlash) + extension;
}
}
/**
* Creates a configuration for which encoder/decoder to run. TODO - move
* this into the constructor.
*/
getConfig(modelName = 'sam_b') {
if (this.config) {
return this.config;
}
const query = window.location.search.substring(1);
const config = {
model: modelName,
provider: 'webgpu',
device: 'gpu',
threads: 4,
local: null,
isSlimSam: false,
};
const vars = query.split('&');
for (let i = 0; i < vars.length; i++) {
const pair = vars[i].split('=');
if (pair[0] in config) {
config[pair[0]] = decodeURIComponent(pair[1]);
}
}
config.threads = parseInt(String(config.threads));
config.local = parseInt(config.local);
ort.env.wasm.wasmPaths = 'ort/';
ort.env.wasm.numThreads = config.threads;
ort.env.wasm.proxy = config.provider == 'wasm';
this.config = config;
return config;
}
}
/** Gets or creates a storage directory */
async function getOrCreateDir(dir, name) {
return (
(await findFileEntry(dir, name)) ||
dir.getDirectoryHandle(name, { create: true })
);
}
/** Finds a file entry in the given directory. */
async function findFileEntry(dir, name) {
for await (const [key, value] of dir) {
if (key === name) {
return value;
}
}
}
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