Revolutionizing MRI Analysis Through AI-Powered Standardization
Transforming the Way We Analyze brain MRI Scans
Darwina aims to revolutionize the field of medical imaging by using artificial intelligence to create synthetic standardized brain images from any MR scanners and protocols. Our goal is to generate one standardized brain MRI image that can be read and ready to analyze. We are developing cutting-edge algorithms to create a powerful software that converts any MRI scan to a standardized image for researchers to use. This software will be available for licensing, empowering healthcare professionals to analyze heterogeneous MRI datasets based on high-quality, standardized MRI images.
Advanced AI Technology
Our software utilizes state-of-the-art artificial intelligence deep learning (DL) algorithms to convert brain MRI scans to standardized images, ensuring consistent and reliable analysis.
Generating MRI Metrics
By synthesizing and standardizing MRI images, our software will help healthcare researchers generate specific MRI metrics typically used for diseases such as multiple sclerosis.
Compatibility with Existing Systems
Darwina's software will seamlessly integrate existing medical magnetic resonance imaging systems, making it easy to incorporate into your workflow.
About Darwina
Transforming Medical Magnetic Resonance Imaging with AI
Deep learning-based methods have recently received significant attention in medical imaging. A broad spectrum of medical tasks has been accomplished using deep learning (DL), many of them with superior performance when compared to traditional post-processing methods. A group of DL-based methods, commonly referred to as deep convolutional neural network (CNN or DCNN)-based works, use neural networks as function approximate in a supervised direct inference framework. That is, an input image passes through several convolutional layers to provide an output. The aim is to minimize a loss function calculated based on the difference between the prediction and the target, thus producing results as close as possible to the target, i.e. the ground truth.
Manual segmentation of brain parenchyma from head MR images, known as brain extraction (BET), is an essential first step for any head image post-processing software. Traditional BET methods are labor intensive and time-consuming. We recently developed a state-of-the-art and fast DL algorithm that outperforms most methods used by medical imaging investigators (1). DL image synthesis methods can also be used to generate standardized images from any clinical images without losing patient information. Generative adversarial networks (GAN) are useful for image-to-image translation in computer vision applications. A few years ago, we trained a proof of concept algorithm using an unpaired GAN method for image-to-image translation of 3D T1-weighted MR images converted to 3D FLAIR-weighted images and vice versa, while preserving multiple sclerosis lesion information (2).
(1) Moazami S, Ray D, Pelletier D, and Oberai A; “Probabilistic Brain Extraction in MR Images via Conditional Generative Adversarial Networks”. IEEE Transactions on Medical Imaging 2023 Oct. doi 10.1109/TMI.2023.3327942. PMID: 37883281.
912bef76-9fc0-46c7-a80d-19543c44cf9a
(2) Jaberzadeh A, Rukmangadachar L, and Pelletier D; "3D MR Image Synthesis using Unsupervised Deep Learning Algorithm in MS Patients". Proceedings of the 71th Annual Meeting of the American Academy of Neurology. May 2019. P5.2-205.
5b8366e7-5213-445f-931b-028f8d64746f
Get in touch
©Copyright. All rights reserved.
We need your consent to load the translations
We use a third-party service to translate the website content that may collect data about your activity. Please review the details in the privacy policy and accept the service to view the translations.