Development of a Brain Tumor Localization Application

Customer

ScienceSoft has developed a solution for the healthcare industry aimed at improving automated brain cancer diagnostics by applying convolutional neural network (CNN) algorithms.

Challenge

The automated medical diagnostics application was to analyze uploaded brain MRI scans and mark the scanned area with the segmented tumor, including each tissue type defined – normal tissue, edema, nonenhancing core, necrotic core and enhancing core.

Solution

The project stages included building a specific CNN structure; preparing training and testing datasets; training and testing the CNN, and evaluating accuracy.

ScienceSoft’s team of senior С++ engineers created a CNN structure with the following layers:

  • 5 convolutional layers
  • 1 ReLU activation layer
  • 1 pooling layer
  • 1 fully connected layer

The MRI analysis process included:

  • Segmentation of 3 planes - XY, XZ and YZ
  • Application of 3 post-processing filters to remove noise and other artifacts
  • Merging of 3 output files into one
  • Application of the final post-processing filter to the merged file

To evaluate CNN performance, the team compared acquired results with the ground truth. The ground truth was taken from BRATS imaging datasets, which have been segmented and annotated manually by one to four raters as well as approved by neuro-radiologists. The maximum network accuracy achieved is 87%.

Comparison: ground truth (top) and the project result (bottom)

Results

ScienceSoft developed a CNN-based application to automatically analyze brain MRI scans, localize tumors and define each tissue type. The application allows to assist health specialists in brain cancer diagnostics, surgery planning and treatment progress tracking.

Technologies and Tools

C++, Caffe framework, CMake, VTK, ITK

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COVID-19 – An update to our clients
In the uncertain time of Coronavirus (COVID-19) outbreak, I want to assure you that ScienceSoft remains fully operational and dedicated to supporting the continuity of our customers’ businesses. Most of ScienceSoft’s employees work remotely, and we’re equipped to provide our services in new conditions, with no impact on the quality of service or communication.
In the uncertain time of Coronavirus (COVID-19) outbreak, I want to assure you that ScienceSoft remains fully operational and dedicated to supporting the continuity of our customers’ businesses. Most of ScienceSoft’s employees work remotely, and we’re equipped to provide our services in new conditions, with no impact on the quality of service or communication.
Stay safe and healthy,
Nikolay Kurayev,
Chief Executive Officer at ScienceSoft