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import type { Types } from '@cornerstonejs/core';
import shaderCode from './growCutShader';
const GB = 1024 * 1024 * 1024;
const WEBGPU_MEMORY_LIMIT = 1.99 * GB;
const DEFAULT_GROWCUT_OPTIONS = {
windowSize: 3,
maxProcessingTime: 30000,
inspection: {
numCyclesInterval: 5,
numCyclesBelowThreshold: 3,
threshold: 1e-4,
},
};
/**
* Grow cut options
*/
type GrowCutOptions = {
/**
* Maximum amount of time the grow cut will be running in the GPU. This value
* also depends on `numCyclesInterval` because N (numCyclesInterval) steps are
* pushed to the GPU asynchronously and only after getting the response back
* for running those N steps it would check if the amount of time spent is
* greater than `maxProcessingTime`.
*
* As an example, let say `numCyclesInterval` is set to 30 and `maxProcessingTime`
* is set to 1 second. If it takes 45ms to run each batch of 30 cycles in the
* GPU that means it should stop after running 23 batches and that would take
* 1.035 second which is greater than 1 second.
*/
maxProcessingTime?: number;
/**
* Window used to compare each voxel to all its NxNxN neighbors. Large window
* size may result in a better segmentation but it would also take more time
* to compute
*/
windowSize?: number;
/**
* Configuration used to monitor and stop processing the volume before
* reaching the number of steps expected to segment the volume.
*/
positiveSeedValue?: number;
negativeSeedValue?: number;
positiveSeedVariance?: number;
negativeSeedVariance?: number;
inspection?: {
/**
* Number of grow cut steps (FOR loop) that shall be run in the GPU before
* checking its current state. This is useful because if the expected number
* of cycles is 100 it may get the segmentation done after 27 cycles. That
* means if `numCyclesInterval` is set to 10 it would stop after 30 cycles
* (3 batches * 10 cycles) saving 70 cycles.
*
* It changes automaticaly to 1 once it detects that its running below the
* `threshold` and get back to `numCyclesInterval` if it gets above the
* `threshold` again.
*
* Small values are not good because taking data out of the gpu to compare
* is very expesive. Higher values may let the algorithm run for a few more
* cycles even when it is already done.
*
* PS: 1 cycle is equal to 1 grow cut iteration (FOR loop running in the GPU)
*/
numCyclesInterval?: number;
/**
* It stops running grow cut once it keeps below the threshold (see `threshold`)
* for a few cycles (`numCyclesBelowThreashold`).
*/
numCyclesBelowThreashold?: number;
/**
* Threshold used to decide if it should or not stop running grow cut. It
* stops only after staying N cycles (`numCyclesBelowThreashold`) below this
* threshold. In some cases the number of voxels updated may decrease
* but increase again after a few cycles.
*
* threshold = number of voxels updated / total number of voxels
*/
threshold?: number;
};
};
/**
* Run the grow cut algorithm in the gpu (compute shader) and updates the label
* map passed in as parameter.
*
* The volume, labelmap, another local buffer called `strengthBuffer` and the
* previous state buffers are sent to the gpu that runs grow cut comparing each
* voxel to all NxNxN neighbors voxels updating the labelmap accordingly.
*
* It also checks if the segmentation is complete before the expected number of
* steps and stop when that happens saving processing time.
*
* @param referenceVolumeId - Volume id
* @param labelmapVolumeId - Label map associated with the given volumeId
* @param options - Options
*/
async function runGrowCut(
referenceVolumeId: string,
labelmapVolumeId: string,
options: GrowCutOptions = DEFAULT_GROWCUT_OPTIONS
) {
const workGroupSize = [8, 8, 4];
const { windowSize, maxProcessingTime } = Object.assign(
{},
DEFAULT_GROWCUT_OPTIONS,
options
);
const inspection = Object.assign(
{},
DEFAULT_GROWCUT_OPTIONS.inspection,
options.inspection
);
const volume = cache.getVolume(referenceVolumeId);
const labelmap = cache.getVolume(labelmapVolumeId);
const [columns, rows, numSlices] = volume.dimensions;
if (
labelmap.dimensions[0] !== columns ||
labelmap.dimensions[1] !== rows ||
labelmap.dimensions[2] !== numSlices
) {
throw new Error('Volume and labelmap must have the same size');
}
let numIterations = Math.floor(
Math.sqrt(rows ** 2 + columns ** 2 + numSlices ** 2) / 2
);
numIterations = Math.min(numIterations, 500);
const labelmapData =
labelmap.voxelManager.getCompleteScalarDataArray() as Types.PixelDataTypedArray;
let volumePixelData =
volume.voxelManager.getCompleteScalarDataArray() as Types.PixelDataTypedArray;
if (!(volumePixelData instanceof Float32Array)) {
volumePixelData = new Float32Array(volumePixelData);
}
const requiredLimits = {
maxStorageBufferBindingSize: WEBGPU_MEMORY_LIMIT,
maxBufferSize: WEBGPU_MEMORY_LIMIT,
};
const adapter = await navigator.gpu?.requestAdapter();
const device = await adapter.requestDevice({ requiredLimits });
const BUFFER_SIZE = volumePixelData.byteLength;
// Stores the number of voxels updated per iteration. If it expects to run 100
// steps to segment the volume it then allocate 100 x 4 bytes and in the end
// we know how many voxels got updated per iteration.
const UPDATED_VOXELS_COUNTER_BUFFER_SIZE =
numIterations * Uint32Array.BYTES_PER_ELEMENT;
// Buffer to track the bounds of modified voxels 6 int32 values for min/max x,y,z
const BOUNDS_BUFFER_SIZE = 6 * Int32Array.BYTES_PER_ELEMENT;
const shaderModule = device.createShaderModule({
code: shaderCode,
});
// `numIteration` index in the `paramsArrayValues` array
const numIterationIndex = 3;
const paramsArrayValues = new Uint32Array([
columns,
rows,
numSlices,
0, // reserved for `numIteration`
]);
const gpuParamsBuffer = device.createBuffer({
size: paramsArrayValues.byteLength,
usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST,
});
const gpuVolumePixelDataBuffer = device.createBuffer({
size: BUFFER_SIZE,
usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_DST,
});
device.queue.writeBuffer(gpuVolumePixelDataBuffer, 0, volumePixelData);
const gpuLabelmapBuffers = [0, 1].map(() =>
device.createBuffer({
size: BUFFER_SIZE,
usage:
GPUBufferUsage.STORAGE |
GPUBufferUsage.COPY_SRC |
GPUBufferUsage.COPY_DST,
})
);
device.queue.writeBuffer(
gpuLabelmapBuffers[0],
0,
new Uint32Array(labelmapData)
);
const gpuStrengthBuffers = [0, 1].map(() => {
const strengthBuffer = device.createBuffer({
size: BUFFER_SIZE,
usage:
GPUBufferUsage.STORAGE |
GPUBufferUsage.COPY_SRC |
GPUBufferUsage.COPY_DST,
});
return strengthBuffer;
});
// This buffer stores the number of voxels updated on each iteration making it
// more performant when calculating the threshold instead of having to move the
// entire labelmap which may be huge from the gpu to the cpu.
const gpuCounterBuffer = device.createBuffer({
size: UPDATED_VOXELS_COUNTER_BUFFER_SIZE,
usage:
GPUBufferUsage.STORAGE |
GPUBufferUsage.COPY_SRC |
GPUBufferUsage.COPY_DST,
});
const gpuBoundsBuffer = device.createBuffer({
size: BOUNDS_BUFFER_SIZE,
usage:
GPUBufferUsage.STORAGE |
GPUBufferUsage.COPY_SRC |
GPUBufferUsage.COPY_DST,
});
// Initialize bounds buffer with extreme values
const initialBounds = new Int32Array([
columns, // minX (initialized to max value)
rows, // minY (initialized to max value)
numSlices, // minZ (initialized to max value)
-1, // maxX (initialized to min value)
-1, // maxY (initialized to min value)
-1, // maxZ (initialized to min value)
]);
device.queue.writeBuffer(gpuBoundsBuffer, 0, initialBounds);
const bindGroupLayout = device.createBindGroupLayout({
entries: [
{
binding: 0,
visibility: GPUShaderStage.COMPUTE,
buffer: {
type: 'uniform',
},
},
{
binding: 1,
visibility: GPUShaderStage.COMPUTE,
buffer: {
type: 'read-only-storage',
},
},
{
binding: 2,
visibility: GPUShaderStage.COMPUTE,
buffer: {
type: 'storage',
},
},
{
binding: 3,
visibility: GPUShaderStage.COMPUTE,
buffer: {
type: 'storage',
},
},
{
binding: 4,
visibility: GPUShaderStage.COMPUTE,
buffer: {
type: 'read-only-storage',
},
},
{
binding: 5,
visibility: GPUShaderStage.COMPUTE,
buffer: {
type: 'read-only-storage',
},
},
{
binding: 6,
visibility: GPUShaderStage.COMPUTE,
buffer: {
type: 'storage',
},
},
{
binding: 7,
visibility: GPUShaderStage.COMPUTE,
buffer: {
type: 'storage',
},
},
],
});
const bindGroups = [0, 1].map((i) => {
const outputLabelmapBuffer = gpuLabelmapBuffers[i];
const outputStrengthBuffer = gpuStrengthBuffers[i];
const previouLabelmapBuffer = gpuLabelmapBuffers[(i + 1) % 2];
const previousStrengthBuffer = gpuStrengthBuffers[(i + 1) % 2];
return device.createBindGroup({
layout: bindGroupLayout,
entries: [
{
binding: 0,
resource: {
buffer: gpuParamsBuffer,
},
},
{
binding: 1,
resource: {
buffer: gpuVolumePixelDataBuffer,
},
},
{
binding: 2,
resource: {
buffer: outputLabelmapBuffer,
},
},
{
binding: 3,
resource: {
buffer: outputStrengthBuffer,
},
},
{
binding: 4,
resource: {
buffer: previouLabelmapBuffer,
},
},
{
binding: 5,
resource: {
buffer: previousStrengthBuffer,
},
},
{
binding: 6,
resource: {
buffer: gpuCounterBuffer,
},
},
{
binding: 7,
resource: {
buffer: gpuBoundsBuffer,
},
},
],
});
});
const pipeline = device.createComputePipeline({
layout: device.createPipelineLayout({
bindGroupLayouts: [bindGroupLayout],
}),
compute: {
module: shaderModule,
entryPoint: 'main',
constants: {
workGroupSizeX: workGroupSize[0],
workGroupSizeY: workGroupSize[1],
workGroupSizeZ: workGroupSize[2],
windowSize,
},
},
});
const numWorkGroups = [
Math.ceil(columns / workGroupSize[0]),
Math.ceil(rows / workGroupSize[1]),
Math.ceil(numSlices / workGroupSize[2]),
];
const gpuUpdatedVoxelsCounterStagingBuffer = device.createBuffer({
size: UPDATED_VOXELS_COUNTER_BUFFER_SIZE,
usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST,
});
const limitProcessingTime = maxProcessingTime
? performance.now() + maxProcessingTime
: 0;
let currentInspectionNumCyclesInterval = inspection.numCyclesInterval;
let belowThresholdCounter = 0;
// Create each iteration step and submit them to the GPU
for (let i = 0; i < numIterations; i++) {
paramsArrayValues[numIterationIndex] = i;
device.queue.writeBuffer(gpuParamsBuffer, 0, paramsArrayValues);
const commandEncoder = device.createCommandEncoder();
const passEncoder = commandEncoder.beginComputePass();
passEncoder.setPipeline(pipeline);
passEncoder.setBindGroup(0, bindGroups[i % 2]);
passEncoder.dispatchWorkgroups(
numWorkGroups[0],
numWorkGroups[1],
numWorkGroups[2]
);
passEncoder.end();
// Get the number of updated voxels for the current iteration only
commandEncoder.copyBufferToBuffer(
gpuCounterBuffer,
i * Uint32Array.BYTES_PER_ELEMENT, // Source offset
gpuUpdatedVoxelsCounterStagingBuffer,
i * Uint32Array.BYTES_PER_ELEMENT, // Destination offset
Uint32Array.BYTES_PER_ELEMENT
);
device.queue.submit([commandEncoder.finish()]);
// Read some data out of the gpu on every N steps to see if it is already done
const inspect = i > 0 && !(i % currentInspectionNumCyclesInterval);
if (inspect) {
// map staging buffer to read results back to JS
await gpuUpdatedVoxelsCounterStagingBuffer.mapAsync(
GPUMapMode.READ,
0, // Offset
UPDATED_VOXELS_COUNTER_BUFFER_SIZE // Length
);
const updatedVoxelsCounterResultBuffer =
gpuUpdatedVoxelsCounterStagingBuffer.getMappedRange(
0,
UPDATED_VOXELS_COUNTER_BUFFER_SIZE
);
const updatedVoxelsCounterBufferData = new Uint32Array(
updatedVoxelsCounterResultBuffer.slice(0)
);
const updatedVoxelsRatio =
updatedVoxelsCounterBufferData[i] / volumePixelData.length;
gpuUpdatedVoxelsCounterStagingBuffer.unmap();
// Skip i=0 because the labelmap is not updated on the first iteration
if (i >= 1 && updatedVoxelsRatio < inspection.threshold) {
// Once if gets below the threshold it needs to check one by one otherwise
// it may skip the stop point.
currentInspectionNumCyclesInterval = 1;
belowThresholdCounter++;
if (belowThresholdCounter === inspection.numCyclesBelowThreshold) {
break;
}
} else {
// Reset it back to its original value
currentInspectionNumCyclesInterval = inspection.numCyclesInterval;
}
}
if (limitProcessingTime && performance.now() > limitProcessingTime) {
console.warn(`Exceeded processing time limit (${maxProcessingTime})ms`);
break;
}
}
const commandEncoder = device.createCommandEncoder();
const outputLabelmapBufferIndex = (numIterations + 1) % 2;
const labelmapStagingBuffer = device.createBuffer({
size: BUFFER_SIZE,
usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST,
});
// Create a staging buffer for reading the bounds
const boundsStagingBuffer = device.createBuffer({
size: BOUNDS_BUFFER_SIZE,
usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST,
});
// Copy output buffer to staging buffer
commandEncoder.copyBufferToBuffer(
gpuLabelmapBuffers[outputLabelmapBufferIndex],
0, // Source offset
labelmapStagingBuffer,
0, // Destination offset
BUFFER_SIZE
);
// Copy bounds buffer to staging buffer
commandEncoder.copyBufferToBuffer(
gpuBoundsBuffer,
0, // Source offset
boundsStagingBuffer,
0, // Destination offset
BOUNDS_BUFFER_SIZE
);
device.queue.submit([commandEncoder.finish()]);
// map staging buffer to read results back to JS
await labelmapStagingBuffer.mapAsync(
GPUMapMode.READ,
0, // Offset
BUFFER_SIZE // Length
);
const labelmapResultBuffer = labelmapStagingBuffer.getMappedRange(
0,
BUFFER_SIZE
);
const labelmapResult = new Uint32Array(labelmapResultBuffer);
// Copy the resulting buffer (Uint32) returned by the gpu to the `labelmapData` (Uint8)
labelmapData.set(labelmapResult);
// Release the gpu staging buffer used to copy the data from the gpu
labelmapStagingBuffer.unmap();
// Read the bounds information from the GPU
await boundsStagingBuffer.mapAsync(
GPUMapMode.READ,
0, // Offset
BOUNDS_BUFFER_SIZE // Length
);
const boundsResultBuffer = boundsStagingBuffer.getMappedRange(
0,
BOUNDS_BUFFER_SIZE
);
const boundsResult = new Int32Array(boundsResultBuffer.slice(0));
boundsStagingBuffer.unmap();
// Extract bounds values
const minX = boundsResult[0];
const minY = boundsResult[1];
const minZ = boundsResult[2];
const maxX = boundsResult[3];
const maxY = boundsResult[4];
const maxZ = boundsResult[5];
// update the voxel manager with the new labelmap data
labelmap.voxelManager.setCompleteScalarDataArray(labelmapData);
labelmap.voxelManager.clearBounds();
labelmap.voxelManager.setBounds([
[minX, maxX],
[minY, maxY],
[minZ, maxZ],
]);
}
export { runGrowCut as default, runGrowCut as run };
export type { GrowCutOptions };
|