The technology relates to software, systems, and methods for automated medical image segmentation via deep learning. Standard holistically-nested networks (HNNs) used in computer vision and medical imaging for segmentation and edge detection have a problem with coarsening resolution. The described technology addresses this.
 
Simple, progressive multipath connections enhance the standard HNN model.  Processing in this new model achieves improved segmentations detail levels without need for additional model parameters.  Variations in appearance are handled without being affected by variations in shape for a given image feature. Early experiments improved segmentation masks compared to standard HNN. Anatomical regions, often misidentified, are correctly captured via this approach.

This technology is generalizable to image segmentation across multiple imaging modalities, including CT, MRI and X-ray.  The improved image segmentation detail levels may have clinically relevant applications to high variability image features, such as tumors and pathologies of the lung. This technology has potential to improve diagnostic capabilities and treatment outcomes in many disease conditions. 

Competitive Advantages:

• Improved detail in automated medical image segmentation without need for additional training or processing of images on other parameters. 
• Generalizable to image segmentation across multiple imaging modalities, including CT, MRI and X-ray
• Improved segmentations detail levels without need for additional model parameters
• Improved segmentation masks compared to standard HNN

Commercial Applications:

• Computer assisted diagnostics and medical imaging processing
• Tumor imaging/diagnosis 
• Imaging pathologies of the lung
• Diagnostic capabilities for a range of disease conditions; e.g., cardiovascular and neurodegenerative
• Providing framework to develop more cost-effective health care