Physicians have long used visual judgment of medical images to determine the course of cancer treatment. A new program package from Fraunhofer researchers reveals changes in images and facilitates this task using deep learning.
The experts will demonstrate this software in Chicago from November 27 to December 2 at RSNA, the world’s largest radiology meeting.
Has a tumor shrunk during the course of treatment over several months, or have new tumors developed? To answer questions like these, physicians often perform CT and MRI scans. Tumors are usually evaluated only visually, and new tumors are often over-
looked. “Our program package increases confidence during tumor measurement and follow-up,” explains Mark Schenk from the Fraunhofer Institute for Medical Image Computing MEVIS in Bremen, Germany. “The software can, for example, determine how the volume of a tumor changes over time and supports the detection of new tumors.” The package consists of modular processing components and can help medical technology manufacturers automate progress monitoring.
The computer learns on its own
The package is unique in its use of deep learning, a new type of machine learning that reaches far beyond existing approaches. This method is helpful for image segmentation, during which experts designate exact organ outlines. Existing computer segmentation programs seek clearly defined image features such as certain gray values. “How-
ever, this can often lead to errors,” according to Fraunhofer researcher Markus Harz. “The software assigns areas to the liver that do not belong to the organ.” These errors must be corrected by physicians, a process which can often be quite time-consuming.
The new deep learning approaches promise improved results and should save physicians valuable time. To demonstrate their self-learning methods, Fraunhofer scientists trained the software with CT liver images from 149 patients. Results showed that the more data the program analyzed, the better it could automatically identify liver contours.
Finding hidden metastases
A further application of the approach is image registration, in which software aligns images from different patient visits so that physicians can easily compare them. Machine learning can aid the particularly difficult task of locating bone metastases in the torso in which hip bones, ribs, and spine are visible. Currently, these metastases are often overlooked due to time constraints in clinical practice. Deep learning methods can help reliably discover metastases and thus improve treatment outcomes.
Researchers focus on a combination of classical approaches and machine learning: “We wish to harness existing expertise to implement deep learning as effectively and reliably as possible,” stresses Harz. Fraunhofer MEVIS builds upon years of experience in practical application: for example, the algorithms for highly precise lung image registration have been integrated into several commercial medical software applications.
Learn more: Machine learning to help physicians
The Latest on: Deep learning
[google_news title=”” keyword=”deep learning” num_posts=”10″ blurb_length=”0″ show_thumb=”left”]
via Google News
The Latest on: Deep learning
- Deep Learning and Neural Networks Drive a Potential $7.9 Trillion AI Economyon April 29, 2024 at 12:30 pm
As artificial intelligence (AI) continues to permeate the corporate landscape, its potential economic impact is ...
- Intel quietly launched mysterious new AI CPU that promises to bring deep learning inference and computing to the edge — but you won't be able to plug them in a motherboard ...on April 27, 2024 at 5:33 am
I ntel has launched a new AI processor series for the edge, promising industrial-class deep learning inference. The new ‘Amston Lake’ Atom x7000RE chips offer up to double the cores and twice the ...
- New multi-task deep learning framework integrates large-scale single-cell proteomics and transcriptomics dataon April 26, 2024 at 7:35 am
The exponential progress in single-cell multi-omics technologies has led to the accumulation of large and diverse multi-omics datasets. However, the integration of single-cell proteomics and ...
- Europe taps deep learning to make industrial robots safer colleagueson April 26, 2024 at 1:07 am
European researchers have launched the RoboSAPIENS project to make adaptive industrial robots more efficient and safer to work with humans.
- AI-powered 'deep medicine' could transform health care in the NHS and reconnect staff with their patientson April 25, 2024 at 10:20 am
Today's NHS faces severe time constraints, with the risk of short consultations and concerns about the risk of misdiagnosis or delayed care. These challenges are compounded by limited resources and ...
- Deep learning predicts heart arrhrythmia 30 minutes in advanceon April 23, 2024 at 4:15 am
Atrial fibrillation is the most common cardiac arrhythmia worldwide with around 59 million people concerned in 2019. This irregular heartbeat is associated with increased risks of heart failure, ...
- Researchers develop deep-learning model capable of predicting cardiac arrhythmia 30 minutes before it happenson April 22, 2024 at 1:23 pm
Atrial fibrillation is the most common cardiac arrhythmia worldwide with around 59 million people concerned in 2019. This irregular heartbeat is associated with increased risks of heart failure, ...
- Deep learning tool may advance precision medicine approacheson April 19, 2024 at 6:00 am
The deep learning-based Lifelong Neural Network for Gene Regulation tool may shed light on how genetic variations influence a patient's drug response.
- Deep Learning in a Disorienting Worldon April 18, 2024 at 10:55 pm
In this book, psychologist Jon F. Wergin calls upon recent research in learning theory, social psychology, politics, and the arts to show how a deep learning mindset can be developed in both oneself ...
via Bing News