We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us

Download Mobile App




Events

ATTENTION: Due to the COVID-19 PANDEMIC, many events are being rescheduled for a later date, converted into virtual venues, or altogether cancelled. Please check with the event organizer or website prior to planning for any forthcoming event.

MRI AI Model Classifies Common Intracranial Tumors

By MedImaging International staff writers
Posted on 07 Sep 2021
Print article
Image: GradCAM color maps colors showing tumor prediction (Photo courtesy of WUSTL)
Image: GradCAM color maps colors showing tumor prediction (Photo courtesy of WUSTL)
An artificial intelligence (AI) 3D model is capable of classifying a brain tumor as one of six common types from a single magnetic resonance imaging (MRI) scan, claims a new study.

To develop the GradCAM algorithm, researchers at Washington University (WUSTL; St. Louis, MO, USA), used 2,105 T1-weighted MRI scans from four publicly available datasets, split into training (1396), internal (361), and an external (348) datasets. A convolutional neural network (CNN) was trained to discriminate between healthy scans and those with tumors, classified by type (high grade glioma, low grade glioma, brain metastases, meningioma, pituitary adenoma, and acoustic neuroma). Performance of the model was then evaluated, with feature maps plotted to visualize network attention.

The internal test results showed GradCAM achieved an accuracy of 93.35% across seven imaging classes (a healthy class and six tumor classes). Sensitivities ranged from 91% to 100%, and positive predictive value (PPV) ranged from 85% to 100%. Negative predictive value (NPV) ranged from 98% to 100% across all classes. Network attention overlapped with the tumor areas for all tumor types. For the external test dataset, which included only two tumor types (high-grade glioma and low-grade glioma), GradCAM had an accuracy of 91.95%. The study was published on August 11, 2021, in Radiology: Artificial Intelligence.

“These results suggest that deep learning is a promising approach for automated classification and evaluation of brain tumors. The model achieved high accuracy on a heterogeneous dataset and showed excellent generalization capabilities on unseen testing data,” said lead author Satrajit Chakrabarty, MSc, of the department of electrical and systems engineering. “This network is the first step toward developing an artificial intelligence-augmented radiology workflow that can support image interpretation by providing quantitative information and statistics.”

Deep learning is part of a broader family of AI machine learning methods based on learning data representations, as opposed to task specific algorithms. It involves CNN algorithms that use a cascade of many layers of nonlinear processing units for feature extraction, conversion, and transformation, with each successive layer using the output from the previous layer as input to form a hierarchical representation.

Related Links:
Washington University


Print article
CIRS -  MIRION
Sun Nuclear -    Mirion

Channels

Radiography

view channel
Image: The FDA has cleared the CSA system with Dynamic Digital Radiography (Photo courtesy of 20/20 Imaging)

Advanced Digital X-Ray System Allows Clinicians to Capture and Visualize Anatomy in Motion

Dynamic Digital Radiography (DDR) is a revolutionary X-ray technology that enables the visualization of anatomy in motion, so clinicians can interpret the dynamic interaction of anatomical structures,... Read more

Ultrasound

view channel
Image: EG-740UT ultrasound endoscope combined with ARIETTA 850 provides outstanding ultrasound image quality (Photo courtesy of FUJIFILM)

Next-Gen Ultrasound Endoscope Enables Complex Diagnostic and Therapeutic Procedures

Endoscopic ultrasound is a specialist procedure performed utilizing an endoscope equipped with an ultrasonic transducer which emits and receives ultrasonic waves within the gastrointestinal tract, such... Read more

General/Advanced Imaging

view channel
Image: New guidance standardizes care for patients presenting with acute chest pain in the ED (Photo courtesy of Pexels)

New Guidance Recommends Coronary CTA as First-Line Test when Treating Acute Chest Pain in ED

Diagnosis and triage of emergency department (ED) patients with suspected acute coronary syndrome (ACS) consume a large and increasing amount of healthcare resources. ED overcrowding is associated with... Read more

Imaging IT

view channel
Illustration

Global AI in Medical Diagnostics Market to Be Driven by Demand for Image Recognition in Radiology

The global artificial intelligence (AI) in medical diagnostics market is expanding with early disease detection being one of its key applications and image recognition becoming a compelling consumer proposition... Read more
Copyright © 2000-2022 Globetech Media. All rights reserved.