MACHINE LEARNING, IMAGE PROCESSING, SOFTWARE TECHNOLOGY, AND ARCHITECTURES

Technology

 

The Machine Learning Revolution and Computer Aided Detection

Machine learning technology is on the move with high profile applications like IBM’s Watson or Google Deepmind’s AlphaGo. The ability to accumulate and organize large datasets and leverage faster computers with greater memory capacity has taken a once niche technology to the mainstream.

One of the primary goals of automatic medical image analysis is to quickly and effectively detect a disease or other condition to a radiologist for further analysis. Computer-aided detection (CAD) has been around for some time; the rate of progress was slow in the past. Rapid development is now underway. The reason is simple: we can now build large complex models for perception. These sophisticated models are a definitive break from the past.

Riverain’s ClearRead CT exemplifies this machine learning revolution, enabled by a combination of engineering know-how, advances in frameworks, and increased computational capacity.

A Unique technology for
Computer Assisted Reading

Interpretation of medical images is a notoriously difficult task. This is due to a range of issues, including; low disease prevalence, interfering structures, human fatigue, satisfaction of search, and distraction. As an example, identifying subtle, small, nodules in the lungs is hampered by complex vascular structures.

An expert must carefully review a CT scan comprised of hundreds of slices in the presence of confounding vascular structures, to which nodules can attach. Doctors must resort to scrolling through each slice of data, which imposes limitations. Heuristics such as using maximum or minimum intensity projections assist the clinician, but have inherent drawbacks.

Powered by machine learning and advanced modeling, Riverain’s ClearRead CT Vessel Suppression uses a model of the local geometry within a chest CT for robust removal of structures that are not nodules. The technology has substantial advantages when compared to traditional approaches. Vessel Suppression opens the black box by allowing a radiologist to have an unprecedented level of transparency into the CAD’s decision process.

Additionally, through vessel suppression, subsequent analysis algorithms are far more straightforward. A traditional system deals with vascular attachments by thresholding and then applying post-processing. Such standard processes are incapable of handling many scenarios, making them inherently brittle.

CT comparison

CT Slice before and after vessel suppression

The figure above is a slice from a CT volume before and after vessel suppression. As can be seen in the vessel suppressed slice to the right, the nodule is cleanly detached from the adjacent vascular structure. The nodule is identified in the vessel suppressed slice by the blue box. The vessel suppression algorithm works with contrast and non-contrast scans, and seamlessly operates on slices with section thickness up to 3mm.

 

Building Vessel Suppression

Many challenging problems had to be solved to realize vessel suppression. There were essentially three key challenges to address:

  • Removal of the dependency on the acquisition settings
  • Accumulation of sufficient data for training large complex models
  • Capturing the complex entanglement of normal and diseased tissues

The third challenge is solved by a model, but in order to build the vessel suppression model, the first two challenges must be addressed.


Acquisition Independence

ClearRead CT handles a broad range of acquisition protocols, a notoriously difficult problem for automatic analysis algorithms. Riverain developed adaptive algorithms so each scan is normalized for factors such as noise, reconstruction kernels, and slice thickness in a systematic fashion. This is in stark contrast to conventional approaches that collect data from different sensors to adjust component algorithms leaving them vulnerable to changes in hardware or reconstruction methods. Additionally, Riverain’s adaptive approach removes limitations imposed by having software from one particular vendor, whose system was designed to operate best for one particular scanning device. This gives ClearRead CT the ability to provide enterprise imaging without compromise, while also enabling fast and simple installation.

Slice showing simulated nodules

CREATING ADEQUATE AMOUNTS OF DATA THROUGH SIMULATION

Building large complex models, such as deep neural networks, requires large training sets. Collecting large, high-quality medical datasets is both time-consuming and expensive, making it impractical in any realistic sense. To circumvent this problem, Riverain developed the capability to create synthetic nodules automatically, and place them into relevant anatomical contexts – such as next to the pleura wall or attached to a vessel. Creating synthetic nodules was an essential capability to build vessel suppression. Vessel suppression, and other algorithms within ClearRead CT, were developed on thousands of simulated nodules. The simulated nodules are instrumental in realizing a robust clinical solution. The figure above illustrates a slice with several simulated nodules embedded.

ClearRead CT Vessel Suppressed Chip

RELIABLE QUANTIFICATION AND UNIQUE ACCESS TO CLINICALLY IMPORTANT QUANTITIES

Vessel suppression enables improved nodule detection, but its benefit continues throughout the processing chain. Suppressing vessels and surrounding structures allows for highly precise segmentation of nodules; which, provides an accurate assessment of size, volume, and other general nodule characteristics.

Vessel suppression aids the radiologist in interpretation through the removal of vascular structure within ground glass nodules, critical to the assessment of density and tissue composition. This allows a radiologist – or analysis software – to rapidly determine the relative amount of solid tissue, which is a critical aspect for clinical decision making. The figure above shows a zoomed-in chip of a ground glass nodule with and without vessel suppression.

xray machine

RELIABLE DEEP COMPUTATION USING MACHINE LEARNING

ClearRead CT is a modern approach utilizing the latest advances in machine learning, such as deep learning. ClearRead CT has surpassed the state-of-the-art by a significant margin based on a combination of frameworks, modeling, and computational ingenuity.

Achieving high reliability and significant performance usually uses substantial amounts of processing. ClearRead CT system can run on commodity hardware, without the need for special computer cards (GPUs) or large memory systems.

WHAT IT MEANS

Improving accuracy and efficiency for clinicians

To understand the performance of radiologists in practical terms: if it were possible for a radiologist to thoroughly interrogate each region of a medical scan, then their performance would be much higher. Unfortunately, due to the increasing use of thin-section data and a larger patient workload, radiologists are given less time to read each study. This trend is not sustainable. Radiologists need better tools.

ClearRead CT is a tool designed to improve the accuracy, and more importantly, the efficiency of radiologists.

Similar to computer chess programs do not play chess like people; computer vision systems that read medical images will not read like a radiologist. Clinicians utilize a lot of abstract knowledge in the form of anatomy and physiology when performing medical interpretation tasks, but the use of such knowledge is something that remains elusive for computers. Tools such as ClearRead CT aim to aid in the more arduous medical interpretation tasks, including a systematic, thorough investigation of each voxel so that radiologists can focus on actual clinical decision making and improving patients’ lives.