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.

A Quantitative Method for Prediction of True Lumen Recanalization in Chronic Total Occlusion of the Superficial Femoral Artery.

BACKGROUND: This study aimed to examine a quantitative method for evaluating calcification in failure in recanalization (FR) in endovascular treatment of superficial femoral artery (SFA) chronic total occlusion, and to investigate the possibility of using a formula to predict the incidence of true lumen recanalization (TR) in such cases. METHODS: Patients who met the inclusion criteria were retrospectively analyzed in our center from January 2012 to September 2017. A Calcification Lesion Analyzing and Scoring System (CLASS) was established to quantify the characteristics of calcification in SFA computed tomography slices, which were ranked as grade 1-4 and class A-E. Corresponding scores were obtained, and the Cumulative Calcification Score (CCSO) of occlusive SFA was calculated on the basis of CLASS. The factors correlating to FR and the formula for predicting TR were evaluated. RESULTS: A total of 215 patients were included in this study. There were 150 cases of TR and 65 cases of subintimal recanalization; 12 (5.6%) cases had FR. The maximum CLASS of occlusion was correlated with FR. Not only the formula including Trans-Atlantic Inter-Society Consensus II grade and CCSO but also the formula including occlusion length and CCSO predicted the incidence of TR well. CONCLUSIONS: The degree of the most severe calcification in occlusive lesions clearly affects success in recanalization. Two quantitative formulas that combine occlusion length or Trans-Atlantic Inter-Society Consensus II grade with CCSO can predict TR in endovascular treatment of SFA lesions with chronic total occlusion.

Locate the Superficial Femoral Artery with Occlusion by Deep Neural Network Correcting Interpolation.

In clinical practice, doctors usually use computed tomography angiography (CTA) to examine lower extremity atherosclerotic occlusive (ASO). Conveniently and accurately locating occlusive superficial femoral artery (SFA) which is difficult to extract from CTA can facilitate diagnosis and surgery. This paper proposed a method locating the occlusive SFA from CTA conveniently. The proposed method first takes control points at a certain interval to bicubic interpolate, and then feeds image patches generated based on the interpolation results to deep neutral network (DNN) to obtain vessel center points. The final location error is less than 9 pixels, which meets the requirements of clinical assessment accuracy. It can be used to assist the diagnosis and surgery of ASO.