Superficial femoral artery calcification segmentation and detection in CT angiography using convolutional neural network.

PURPOSE: Calcification detection and segmentation in CT angiography (CTA) is the basis of preoperative calcification assessment and treatment determination in endovascular interventional surgery for lower-extremity atherosclerotic occlusion disease. However, the complex calcification-lumen contrast and difficult-to-locate occluded superficial femoral artery (SFA) make it challenging. This paper proposes a fast and accurate method without artery extraction to segment and detect SFA calcification in CTA using a convolutional neural network.

METHOD: The thigh region containing the target SFA is first automatically extracted based on the human anatomical position. Then, 3D Unet with a large receptive field is used to segment calcifications in image patches with a large field of view. The lumen label is introduced and a calcification-lumen contrast data augmentation method is developed to improve the segmentation performance on images with varying calcification-lumen contrast. Finally, false-positive errors far from the SFA are eliminated based on the SFA centerline estimated from the segmentation results.

RESULTS

Five-fold cross validation experiments were conducted on a local dataset of CTA images containing 128 SFAs. The average Dice scores of calcification segmentation on the entire, occluded and non-occluded arteries achieved 89.12%, 92.98% and 88.96%, respectively. The average recall and precision of calcification detection on each slice were 93.50% and 91.51%, respectively. The total processing time was about 2 min.

CONCLUSIONS

This paper proposes a CNN-based method to segment and detect SFA calcification in CTA without artery extraction for varying calcification-lumen intensity contrast and arterial occlusion situations. The work can be used to improve clinical calcification analysis.