Content

Hello AMI

	Lesson 00: Load Load DICOM Data and get a nice Series/Stack/Frame structure.		Lesson 01: Visualize 3D Look at the data we loaded in 3D.
	Lesson 02: Mesh Add a mesh to the scene.		Lesson 03: Visualize 2D Look at the data in 2D.
	Lesson 04: Labelmap Overlays on top of you data.		Lesson 05: TRK TRK - coming soon.
	Lesson 06: Volume Rendering Volume Rendering.		Lesson 07: Lookup tables Lookup Tables - coming soon.
	Lesson 08: Custom progress bar Custom progress bars.

(more) Advanced demos

Volume rendering, 2D viewer, arbitrary reslicing and more examples and advanced demos there!

Features

✅ READY 🔶 IN PROGRESS OR LIMITED SUPPORT ❌ ON ROADMAP

Capabilities	Volumes	Meshes	Widgets
✅ 2D Visulization	✅ Dicom	✅ VTK (THREEJS)	🔶 Handle (2D/3D)
✅ 3D Visualization	✅ NRRD	✅ STL (THREEJS)	🔶 Probe (2D/3D)
✅ Volume Rendering	✅ Nifti	✅ TRK	🔶 Ruler (2D/3D)
✅ Lookup Tables	✅ MHD/(Z)RAW	✅ FSM	🔶 Orientation (2D/3D)
✅ Label Maps	✅ MGH/MGZ	❌ CURV	🔶 Angle (2D/3D)
	❌ JPEG

Usage

Pre-requisites

ES2015 promises support. (consider using polyfills if needed)
Load THREEJS your index.html BEFORE AMI.


<script src="https://cdnjs.cloudflare.com/ajax/libs/three.js/r81/three.js"></script>

Yarn


$> yarn add ami.js

Note: you might need to include babel transforms in you build process.


// app.js
import * as AMI form 'ami.js';
window.console.log('Ready to rock!!');

ami.js

Check-out the lessons to get started quickly.

New: Use the new factory not to have to include three in index.html.

import * as THREE from 'three';
import {stackHelperFactory} from 'ami.js';

const StackHelper = stackHelperFactory(THREE);
const stackHelper = new StackHelper();

Add AMI in your index.html after THREEJS.


<!-- index.html -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/three.js/r81/three.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/ami.js/0.0.12/ami.min.js"></script>
<script src="app.js"></script>

#app.js
const AMI = AMI;
window.console.log('Ready to rock!!');

Developer corner

Get the source code and related packages.


$> git clone https://github.com/FNNDSC/ami.git
$> cd ami
$> yarn install

To run examples (browserify/babelify/serve the example)


$> yarn example <examples name>

#run the geometries_slice example
$> yarn example geometries_slice

Build standalone library to lib/


$> yarn build:ami

Build minified standalone version run


$> yarn build:ami:prod

Tests


$> yarn test

Documentation


$> yarn doc

Build and deploy dist/ to gh-pages


$> yarn deploy

Find out more about the API.

Change log

Credits

AMI would not exist without them:

THREEJS

Base components such as Vectors, Matrices and Objects3D.
HTML template for example page.
Author(s): mrdoob

DicomParser

DICOM parsing relies on it.
Author(s): chafey

CornerstoneWADOImageLoader

Was used to figure out how to use the dicom parser properly.
Author(s): chafey

NIFTI-Reader-JS

Nifti parsing relies on it.
Author(s): rii-mango

NRRD-JS

NRRD parsing relies on it.
Author(s): jaspervdg

JPEGLosslessDecoderJS

JPEG Lossless Decoder for DICOM images
Author(s): rii-mango

Image-JPEG2000

JPEG 2000 Decoder for DICOM images
Author(s): jpambrun, mozilla

Pako

GZ file decompression
Author(s): nodeca

Project Name	Stars	Repos Using This	Packages Using This	Most Recent Commit	Total Releases	Latest Release	Open Issues	License	Language
3dunetcnn	1,628			a month ago			1	mit	Python
Pytorch 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation
Medicalnet	1,221			2 years ago			50	other	Python
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project provides a series of 3D-ResNet pre-trained models and relative code.
Medicalzoopytorch	896			a year ago			13	mit	Python
A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation
Learning Deep Learning	834			2 days ago			1		Jupyter Notebook
Paper reading notes on Deep Learning and Machine Learning
Medmnist	764			16 days ago	3	May 06, 2022		apache-2.0	Python
[pip install medmnist] 18 MNIST-like Datasets for 2D and 3D Biomedical Image Classification
Ami	667	2	1	6 months ago	50	September 30, 2020	141	mit	JavaScript
AMI Medical Imaging (AMI) JS ToolKit
Pytorch Medical Segmentation	536			a year ago			3	mit	Python
This repository is an unoffical PyTorch implementation of Medical segmentation in 2D and 3D.
Invesalius3	499			2 days ago			95	gpl-2.0	Python
3D medical imaging reconstruction software
Medpy	439	15	24	6 months ago	6	February 14, 2019	13	gpl-3.0	Python
Medical image processing in Python
Cnn 3d Images Tensorflow	176			4 years ago			5		Python
3D image classification using CNN (Convolutional Neural Network)

Ami