Segmenting X-Ray images with deep learning

STFC/Durham University CDT in Data Intensive Science.

Carolina Cuesta-Lázaro

Arnau Quera-Bofarull

Joseph Bullock

Who are we?

2 months PhD placement at IBEX innovations, as part of the CDT program

Carolina / Arnau

Cosmology

Joe

Particle Physics

Detect bone and soft-tissue on X-Ray images

Detect

collimator

Segment

Open beam

Bone

Soft-tissue

SegNet

• The network has more than 15 Million free parameters.
• To find the values of the parameters that produce the correct segmentation, it has been trained on 1.3 Million images.

XNet

• Original Architecture based on SegNet but fewer parameters.

• Trained on 150 images, artificially augmented to more than 7000.

Results

• Generalises well even for unseen categories!
• Overall accuracy on test set: 92%
• Soft tissue TP/FP rate: 82% / 4%

• Promising ML applications to medical imaging.
• Possible to train ML models with limited hardware and resources.

• Knowledge of building and deploying a machine learning product in an industrial setting.
• XNet Paper is on the arXiv:1812.00548v1, and will be presented in the upcoming SPIE Medical Imaging conference in San Diego.

Conclusions

Learning outcomes