ScienceSoft has developed a solution for the healthcare industry aimed at improving automated brain cancer diagnostics by applying convolutional neural network (CNN) algorithms.
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.
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:
The MRI analysis process included:
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.
C++, Caffe framework, CMake, VTK, ITK