We implemented U-Net variants for nerve segmentation from ultrasonic images in the scope of a research project in Deep Learning for Computer Vision @TU-Munich. The underlying data is freely available on Kaggle:
"Even the bravest patient cringes at the mention of a surgical procedure. Surgery inevitably brings discomfort, and oftentimes involves significant post-surgical pain. Currently, patient pain is frequently managed through the use of narcotics that bring a bevy of unwanted side effects. This competition's sponsor is working to improve pain management through the use of indwelling catheters that block or mitigate pain at the source. Pain management catheters reduce dependence on narcotics and speed up patient recovery. Accurately identifying nerve structures in ultrasound images is a critical step in effectively inserting a patient’s pain management catheter. In this competition, Kagglers are challenged to build a model that can identify nerve structures in a dataset of ultrasound images of the neck. Doing so would improve catheter placement and contribute to a more pain free future."
Simply clone the repository and add it to your virtual environment by
echo $PWD > ~/path/to/env/lib/python3.7/site-packages/nervenet.pth
The data should be located in a parent directory as follows
..\data
\images
1_1.tif
...
9_99.tif
\test
1.tif
...
999.tif
image_files.txt
text_files.txt
Note: we do not guarantee code consistency due to later cleanup without testing. Feel free to contact for support.